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celebrate dyslexiaDraft (2017-18)


Academic confidence and dyslexia at university


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Theoretical perspectives

This thesis is about how the academic confidence of university students may be affected by dyslexia. In this Literature Review section of the thesis the selection of literature chosen is considered as the most relevant and pertinent for supporting the aims and objectives of the project and underpinning the research questions and hypothesis which are suggesting that the academic confidence of students with dyslexia is depressed in comparison to their non-dyslexic peers.

An attempt has been made to distil the most important theoretical concepts about academic confidence and especially its parent construct, academic self-efficacy, and about dyslexia and what it means to be dyslexic into a narrative that demonstrates both an understanding of the concepts but also persistently connects the discussion to the education context, specifically at the tertiary level.


Complete Thesis Contents


Theoretical Perspectives - a review of selected literature


Introduction to the literature review

This thesis is exploring how academic confidence in university students is affected by dyslexia. As a primary research study, data has been collected from student participants which was processed and statistically analysed with dyslexia as the independent, and academic confidence as the dependent variables. The major part of this literature review sketches out the import of academic self-efficacy as the parent construct for academic confidence (Sander & Sanders 2006) because academic confidence is the outcome variable for this study. Academic self-efficacy and academic confidence stem from the same components of self-efficacy (Sander & Sanders, 2006) proposed by Bandura as core to Social Cognitive Theory (SCT) (Bandura, 1977. 1986, 1997a) which is discussed in some detail. Drawing this underpinning theory into university contexts, Sander & Sanders (2003) suggest that academic confidence is thus likely to be a mediating variable that acts between a student's inherent academic capabilities, their learning-style preferences and the opportunities for gaining creditable academic achievements that exist at university as experiences impact on expectations. To gauge academic confidence, the Academic Behavioural Confidence (ABC) Scale was developed as a means to assess students' levels on confidence in their behaviours, actions and plans in tackling their academic study (Sander & Sanders, 2007) and the ABC Scale is the metric that is used in this study for gauging students' levels of academic confidence to look for significant differences in ABC between dyslexic and non-dyslexic students.

However this section of the thesis opens by reviewing a selection of literature that is germane to the nature of dyslexia. The review is not an exhaustive discussion about how dyslexia has come to be understood through over a century of research and theorizing as this is too large a task for this thesis. Instead, the review will try to navigate a path through the competing theories to highlight the some of the tensions, conflicts and contradictions between aetiologies that continue to make research about dyslexia challenging. It will also focus on aspects of these which especially impinge on this project and will align the discussion in support of the view that early, definitional paradoxes can now be set aside, in university contexts at least, not least because the most significant of more recent constructions of dyslexia may be challenging whether it makes any sense to be diagnosed as 'dyslexic' at all. These firstly return to the advocacy that dyslexia should be best considered as a multifactorial set of characteristics or dimensions which although draws on earlier constructions of dyslexia (e.g: Castles & Colheart, 1993) has now attracted significant research interest in the higher education sector (e.g.: Tamboer et al, 2016, Tamboer et al, 2017). This approach to understanding dyslexia is to consider its impact on a student's academic progress in a variety of both positive and less helpful ways: for example, it is suggested that innovative and creative thinking may be heightened in students with dyslexia (eg: Everatt et al, 1999, Chakravarty, 2009) which might be thought advantageous in some disciplines such as in the Arts, architecture or engineering. In contrast, the frequent use of highly specific and precise terminology in mathematics for example, has been shown to cause difficulties to dyslexic students where similar sounding words have very different meanings in mathematics, for example integer and integral (Perkin & Croft, 2007). Secondly, that more recent thinking about the nature of dyslexia might direct educationalists and especially teaching practitioners towards accepting dyslexia as a wide-ranging set of learning attributes that are positioned along a spectrum of entirely natural, human neurodiversity (Cooper, 2006) but which also acknowledges the atypical nature of this blend of attributes. Although tackling the nature of dyslexia from a different perspective, the neurodiversity approach does allude to multifactorialism as a route to understanding more about what it means to be dyslexic. Thirdly, that in order to accommodate both the wider neurodiversity agenda and specifically the multifactorial construction of dyslexia, the focus in learning and teaching environments now needs to shift towards adjusting them in ways that are properly inclusive, accessible and flexible rather than continue to put the dyslexic individual at the centre of the 'reasonable adjustments' agenda. This is one which may reinforce the internalizing of dyslexia as a disabling condition and so it is reasonable to assume that a greater accommodation of learning-and-teaching diversity should ameliorate much of the stigma associated with feelings of being different or disabled in learning contexts (Dykes, 2008, Shaw & Anderson, 2018). And lastly to unequivocally support the suggestion that a more useful framework for understanding dyslexia might now exist by best considering it as alternative form of information processing (Tamboer et al, 2014) which disassociates dyslexia from disability and difference almost completely. The closing narrative of the first sub-section briefly discusses how dyslexia is assessed or identified in higher education contexts and prequels the major part of the study's research design where a new process for gauging dyslexia in university students has been developed as the independent variable in this study which aims to locate the dyslexic individual's learning attributes on a continuum of study and learning dimensions that are observable in any student, either identified as dyslexic or not.


dyslexia is complicated1. Dyslexia


Dyslexia, whatever it is, is complicated

The contemporary view of dyslexia as it occurs in university students is to consider it as a learning difference rather than a learning disability however the syndrome remains widely debated (Elliott & Grigorenko, 2014). Attempts to theorize developmental dyslexia and its aetiology differ quite widely (Peterson & Pennington, 2015) not least when attempting to interpret the variety of characteristics that can be presented (Ramus, 2004, Proctor et al, 2017). This is especially so in relation to how cognitive differences, more usually regarded as deficits, are classified as dysfunctions (Buttner & Hasselhorn, 2011) and whether these differences are causal, consequential or even covariants of dyslexia as a learning disability (Vellutino et al, 2004). The impacts of dyslexia and dyslexia-like profiles on learning are readily apparent in literacy-based education systems ranging from initial identification in early-age learners who experience challenges in the acquisition of reading skills, to university students who attribute many of their struggles to adapt to the independent and self-managed learning processes that are core competencies in higher education learning to a dyslexia or dyslexia-like learning profile (MacCullagh et al, 2016).

In the last half-century, attempts to define dyslexia to account for this range of traits have moved away from earlier definitions which focused on dyslexia as a reading impairment in children, specifically a difficulty in single-word reading fluency and spelling. For example Critchley (1970) provided a brief summary of the historical origins of identifying and attempting to define dyslexia to date, pointing out that the challenges in arriving at a convincing definition of dyslexia had led some authorities to abandon attempts to do so, and although it is not known which authorities were being referred to we might assume that the reason for this casting-aside were due to the plethora of competing definitions of dyslexia that were available to choose between. Drawing on the most recent definition at that time from the World Federation of Neurology (WFN), Critchley supported his point by quoting two, parallel definitions from the WFN which were recommended for acceptance by neurologists, paediatricians, psychologists and those practicing in the pedagogic domains who presumably, chose the definition that most suited their purposes at the time (ibid, p11):

  • Specific developmental dyslexia:
    • "A disorder manifested by difficulty in learning to read despite conventional instruction, adequate intelligence, and socio-cultural opportunity. It is dependent upon fundamental cognitive disabilities which are frequently of constitutional origin"
  • Dyslexia
    • "A disorder in children who, despite conventional classroom experience, fail to attain the language skills of reading, writing and spelling commensurate with their intellectual abilities"

Some three decades later, Snowling (2002) cauterized the WFN definitions by identifying significantly that without further defining some of the constituent terms in the definitions, such as explaining what should be understood as 'conventional instruction' or 'intellectual abilities' for example, the definitions were weak to the extent that practitioners attempting to use them to determine whether a child was presenting dyslexia or not were likely to find this a challenging conclusion to arrive at. Snowling advanced more recent thinking about how phonemics, that is, the study of the sound system of a language and the classification of its phonemes (sound parts) being now better comprehended, were thought to be instrumental in understanding dyslexia in children. Significant amongst studies drawn upon was a project conducted to explore and explain differences between children who, as poor readers, responded to interventions and remediation, and others of similar intellectual abilities who did not (Vellutino et al, 1996). Amongst the research outcomes of this study were the identification of other apparent deficits which appeared to result from phonological skills differences between 'regular' poor readers and dyslexic children. These were reported as poorer short-term memory performance and rapid-naming deficits, but especially, depressed phonological awareness - which is the ability to recognize how words are comprised of connected sound structures, the ability to distinguish the syllables of a word and particularly to tune in to the individual sounds, or phonemes, of a word. This will be discussed a little more in section ## below. But an additional important point to be drawn out of Snowling's (2002) discussion beyond emphasizing the importance of acknowledging phonological processing difficulties as significant in understanding what dyslexia is, proposes that dyslexia should be thought of as more than an issue with literacy. This is demonstrated not least by stating that 'dyslexia is [likely to be] characterized by a particular cognitive profile that places a child at risk of reading failure' (ibid, p20) which suggests the usefulness of profiling in comprehending more about a range of deficits, differences, characteristics or dimensions which are likely to exist on a continuum as opposed to being discrete categories, a suggestion proposed some time earlier (e.g.: Ellis, 1984). This idea is highly pertinent to this current project as a dyslexia profiling tool has been designed as one of the evaluating metrics. Much later work by Callens et al (2012) took the cognitive profiling idea both into higher education contexts and also into a language other than English with a study of Dutch students and this is discussed in more detail later (section ##). The dimensional aspect of dyslexia is also key to this current project and this will be demonstrated in the justifications for designing the Dyslexia Index Profiler as a profiling tool reliant on determining levels of dyslexia-ness on a dimensional basis in the Research Design report of this thesis (Section 3).

However in moving the definition discussion into the contemporary context of dyslexia amongst university students, a straw poll enquiry conducted as part of the foundations of this current project (see Section ## and Appendix ##) established not unsurprisingly that the definition of dyslexia proposed as workable and understandable by the British Dyslexia Association (2007) has tended to be the one that has been broadly adopted in higher education institutions in the UK over the last decade. This is a definition which acknowledges much of the preceding research evidence but which also takes a more inclusive approach by making no specific mention of deficits and affirms that some of the traits of dyslexia should be recognized as abilities rather than as disabling:

  • "Dyslexia is a combination of abilities and difficulties that affect the learning process in one or more of reading, spelling or writing and may have accompanying weaknesses in processing speed, short-term memory, organization and sequencing" (ibid)

This earlier, 2007 definition has since been updated with the most recent version (BDA, 2018) enshrining the findings of a substantial report commissioned by the UK Government's Department for Children, Schools and Families about identifying and teaching people with dyslexia (Rose, 2009). It is evident that the most substantial changes are in widening the range of characteristics of dyslexia still further to distil the primary features of the syndrome into a comprehensive, working definition:

  1. "Dyslexia is a learning difficulty that primarily affects the skills involved in accurate and fluent word reading and spelling;
  2. Characteristic features of dyslexia are difficulties in phonological awareness, verbal memory and verbal processing speed;
  3. Dyslexia occurs across the range of intellectual abilities;
  4. [Dyslexia] is best thought of as a continuum, not a distinct category, and there are no clear cut-off points;
  5. Co-occurring difficulties may be seen in aspects of language, motor co-ordination, mental calculation, concentration and personal organization, but these are not, by themselves, markers of dyslexia;
  6. A good indication of the severity and persistence of dyslexic difficulties can be gained by examining how the individual responds or has responded to well-founded intervention." (BDA, 2018)

For the purposes of necessarily grounding a research study in definitions of the principal ideas being explored, it is this British Dyslexia Association (2018) definition of dyslexia that has been chosen as the most appropriate. This is partly because this working definition is quite broad-based but also because there are two important features of the definition that are significant to this project: firstly point 4, referring to dyslexia as a continuum, supports the formulation of the Dyslexia Index Profiler which has been designed and developed for this project to gauge levels of dyslexia-ness of respondents who participated in the study according to how their responses in the research questionnaire positioned them along a continuous scale; and secondly point 5 is important to dyslexia in higher education because it is indeed many of the co-occurring difficulties that are manifested by students with dyslexia at university, particularly aspects of personal organization which have also been incorporated into the Dyslexia Index Profiler. These co-occurring issues are discussed below in section ##.

Significant in both the original and the current BDA definitions is an absence of any reference to dyslexia as a disability, learning, or otherwise. However dyslexia is categorized as a disability as defined by the Terms and Definitions of the Equality Act 2010 because the Act considers dyslexia to be a condition recognizable as 'a mental impairment that has a substantial and long-term adverse effect on an individual's ability to conduct normal day-to-day activities' (Office for Disability Issues, 2011, p7). Dyslexia is referred to twice in the Guidance Notes (ibid) firstly as an example of a disability which can arise from impairments (ibid, Section A5, p9) and later as a part of one of the many examples of circumstances intended to provide clarification about the scope of the Act in which dyslexia is suggested as a condition which may cause an individual to develop coping or avoidance strategies which can fail in some circumstances (ibid, Section B10, p19). Translated into the environment of learning and study at university, this means that a student with dyslexia - considered as an unseen, hidden and not immediately obvious disability which is substantial and long-term - is assumed likely to be an learner who will find the conventional academic processes of university particularly challenging due to the mental impairments that are considered as characteristic of the condition. Setting aside for the moment how dyslexic students may feel about being labelled as disabled - which is discussed below in sub-section ### - the first immediate outcome is that such students will be eligible to apply for support through the Disabled Students' Allowance (DSA) in the UK. The DSA is a funding stream that is reserved for disabled students to provide financial assistance to cover the purchase of equipment, resources and personal support with the aim of ensuring that study at university becomes as fair and equitable as possible in comparison to students with no disabilities. In 2015 the UK Government announced an intention to remove dyslexia as a qualifying condition that is eligible for consideration under the DSA, presumably because it was considered no longer substantial enough, although it is indisputable that dyslexia is long term and persists into adulthood (e.g.: Bruck, 1992, Carawan et al, 2016). But as a direct result of lobbying from parent groups, individuals and not least, professional associations such as the British Dyslexia Association (BDA) and the Association of Dyslexia Specialists in Higher Education (ADSHE) the decision was deferred (2016/17).

Thus at this time, students with a dyslexia that has been identified and documented may apply for help with their studies through the DSA. This means that following a formal Needs Assessment, usually conducted by a Disability Needs Assessor either at the student's university or at a specialist centre nearby, a list of recommended equipment and resources is drawn up. Typically this includes, for example, a laptop computer with specialist assistive technology software installed such as an advanced spell-checker or text-to-speech software applications, together with a schedule of personal study assistance, most often consisting of an entitlement to a course of specialist study skills support tutorials designed to guide the student towards more easily managing the administrative, clerical and organizational tasks that are an essential part of study at university. However the Equality Act 2010 also requires universities to provide reasonable adjustments to all aspects of their physical environment, their operational procedures, curriculum delivery and assessment, and associated academic-related and administrative processes. At a practical level for the student with dyslexia at least, this typically may mean providing study areas that are differentiated from those more widely available for other students by being located in quieter environments with fewer distractions; ensuring that some computer workstations are equipped with specialist assistive technology software applications; that Virtual Learning Environments (VLEs) are formatted to be easy to navigate with content that is easy to access; that additional time may be provided for students with dyslexia to complete formal examinations. Not least this recognizes that in the domain of adult learning at higher intellectually functional levels, that is in higher education, early-learning academic challenges that are functions of weaknesses in literacy skills have been shown to be often subsumed by later-learning organizational struggles that impact more substantially on learning confidence in comparison with earlier learning difficulties where processes (Kirby et al, 2008) are developed to circumvent earlier learning weaknesses often through widespread use of study aids and support agencies or technology (Olofsson et al, 2012).

Thus, dyslexia remains a challenging condition to define with a range and scope of definitions that has emerged over a century of study largely stemming from an interest in explaining why some children find learning to read particularly challenging in comparison to many of their peers (Lombardino & Gauger, 2014). For some children this may be through poor social backgrounds associated with low literacy levels (Snowling, 2012) or a low intellectual ability but for others who appear not to bring these challenges to their learning, the slow uptake in reading skills appears to be due to disturbances in some elements of the cognitive processing of some sensory inputs (Stanovich, 2000). It is significant therefore that relatively recent research interest is attempting to more fully understand subtypes of dyslexia. One study which indicates some of the earlier theorizing about dyslexia from this perspective observed that there appeared to be evidence in developmental dyslexia of the subtypes more normally associated with acquired dyslexia - that is, through brain trauma (Castles & Colheart, 1993). This suggests that there may be distinct dyslexia factors which may be more or less prevalent in any single individual who presents dyslexia or a dyslexia-like study and learning profile. More recent work has taken dyslexia in adults as a focus and particularly, students in higher education settings. Centred in The Netherlands, recent studies by Tamboer and colleagues in particular (eg: Tamboer et al, 2014) are taking the discussion in a new direction by building on earlier research that focused on dyslexia as a multi-dimensional condition: Le Jan et al (2009) explored symptoms of dyslexia in group of elementary school children (n=113) to build a diagnostic tool based on a multi-variate analysis of characteristics of dyslexia to guide dyslexia assessors to expediently identify the presence of dyslexia or not. 8 variables from the four cognitive categories of metaphonological skills - that is, awareness of the sound structures of spoken words, more often known as phonological awareness, morphology knowledge - relating to word structures and word inter-relationships, visuo-attentional capacities - which is concerned with the visual span of attention in reading, and differentiating auditory contrasts - such as typically discerning the differences in similar sounding syllables, for example between '~ti~' and '~di~'. These variables were collectively established as significant predictors of the likelihood of dyslexia being present. Pennington (2006) suggested a multi-factorial cognitive deficit model to explain the causes of dyslexia which emerged out interest in explaining the co-morbidities of dyslexia with attention deficit hyperactive disorder (ADHD) and of dyslexia with speech sound disorder (SSD). One of the key findings suggested that although a multi-variate model did not achieve a thorough understanding of developmental disorders such as dyslexia, ADHD and SDD, it did help in explaining more about the 'shared processes at the aetiologic, neural and cognitive levels' (ibid, p405) of such conditions. The focus of the Netherlands studies were to explore more fully the factor structure of dyslexia to try to determine firstly whether understanding more about the subtypes of dyslexia can enable more effective screening tools to be developed for use in identifying the syndrome amongst university students, and secondly whether these are distinguishing features of dyslexic learners alone or that they can be observed to varying degrees in other, even all students. This approach in attempting to understand dyslexia and how it might be identified more specifically in tertiary education settings is particularly pertinent to my study and is developed more thoroughly in sub-section ## below.

It might be argued that much of the problem is a function of the way in which dyslexia is assessed. In the case of the literacy-related dimensions of dyslexia that are most noticeable in young learners, Stanovich in particular has repeatedly questioned the discrepancy approach persistently used to measure dyslexia, insisting that when aptitude-achievement is used as the benchmark comparator, such a 'diagnosis' fails to properly discriminate between attributing poor reading abilities to dyslexia or to other typical causes (1988, 1991, 1993, 1996, 1999, 2000). Elliott & Grigorenko (2014) bring this into the contemporary context in their argument that identifying dyslexia is problematic to the extent that assessments of it may be so flawed as to be irrelevant or at best, academically counter-productive. Notably, it has also been shown that students with dyslexia in higher education may not be a homogeneous group due to the likelihood that several subtypes of dyslexia or dyslexia-like profiles may exist and hence that any identification approaches adopted, need to be designed to respond accordingly (Tops et al, 2012). These issues are explored later where the discussion specifically expands on the problems and suggested solutions surrounding the determination of the extent of a dyslexic individual's dyslexia - that is, how to assess the severity of dyslexia, or to more properly resonate with the stance of this project, to find out more about the levels of incidence of dyslexia-like characteristics, that is, dyslexia-ness, in the learning and study profiles of university students and to understand how useful it may be to determine these in academic contexts. Hence, given the persistent debate surrounding the nature of dyslexia and which aspects of the syndrome might be measurable and for what purpose, assigning a metric to establish a worthwhile appraisal of dyslexia, dyslexia-like characteristics or dyslexia-implied study profiles in learning context is ambitious. It is Stanovich's view that domain-specific difficulties - for example, finding reading challenging, struggling with arithmetic - may be comorbid in many cases, but it is only helpful to group such difficulties under an umbrella term - such as 'learning disabilty' - after an initial domain-specific classification has been established (Stanovich, 1999, p350). This is significant not least because this argument adds weight to the adoption of a factorial view of dyslexia, especially in academically capable adult dyslexics where many of the early-years' learning difficulties may have been displaced by strategically developed learning solutions but which may expose other dyslexia 'factors' as more influential in the learning processes that are commensurate with study at university.

Finally and to complete this opening sub-section, it must be added that it seems clear that the Equality Act 2010 is built on a recognition of the social model of disability being one which views society as the disabling factor when people are physically impaired or different from most other members of society. The Equality Act considers dyslexia to be one of a family of unseen or hidden impairments which are counted as disabilities and despite the clear intentions of the Act to focus on inclusion and access, dyslexia has tacitly remains attributed to the individual not least through a persistence to diagnose it which might be argued is more consistent with the now outdated medical model of disability which assumes that disability is the fault of the disabled person rather than resulting from situations and circumstances in society that are not adjusted to account for different abilities, either physical or hidden. Much of the research evidence explored in this thesis persists in referring to a diagnosis of dyslexia whereas the contemporary view about dyslexia in learning environments is that it is the structures and systems of delivery which should be considered as the disabling factor and that as long as learning outcomes that assess intellect and academic aptitude remain based on high levels of literacy, learning barriers attributable to even a more positively-focused social construction of dyslexia are likely to remain, no matter how the syndrome is defined (Cameron & Billington, 2015). One of the significant outcomes of this study reports on how students learned of their dyslexia to try to find out more about the impact of being diagnosed and how this may be correlated with levels of academic confidence.

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Theoretical standpoints of dyslexia

It is pertinent to this project that a brief overview of some of the most important theories about what dyslexia is and its aetiology is presented. However, this section of the literature review is not intended to be a discussion or a critical review of the theories but more so aims to provide a backdrop of the main ideas about dyslexia as a framework that, together with the theoretical underpinnings of academic confidence presented below (sub-section ##), support the objectives of this research project.

Theories about dyslexia fall into several, broad categories: Attributing dyslexia to phonological skills and awareness differences is widely researched and supported not least due to revelance in explaining reading difficulties in children. Explaining dyslexia as an outcome of visual differences or irregular visuo-attentional processing appear at the outset to be quite different and sometimes rather specialized but these theories have also attracted substantial support. A more recent focus considers dyslexia as an example of natural human neurodiversity by placing it along a spectrum which is said to include, for example, autism and ADD (attention deficit disorder). While other theories have tried to blend some of the well-substantiated explanations into a more comprehensive framework for understanding the nature and aeitiology of dyslexia by taking a neuro-biological standpoint, or more to consider it as a multifactorial syndrome that presents a wide range of characteristics, attributes and differences both in learning and study behaviours and more widely in everyday functioning. These will be taken in turn in an attempt to crystalize the most important features of each into short overviews which try to briefly illustrate their theoretical roots and thelr position in the domain of learning and teaching, especially in higher education contexts.

1. Dyslexia is a phonological processing disturbance which offers the explanation for reading difficulties as resulting from impairments in forming grapheme-phoneme correspondence, that is, understanding the connections between the forms of letters and the corresponding sounds that they represent. Hence that the ability to blend or disassemble letter combinations, i.e., syllables and words, into or from their corresponding speech sounds, becomes difficult (Brady & Shankweiler, 1991). In the phonological-core variable-difference framework, Stanovich (1988) argued that the primary difference between dyslexic and non-dyslexic individuals is evidenced by a deficiency in the cognitive dimension where phonological skills are located. This was said to explain differences in causes for retarded reading skills between young people with a dyslexia and others who were more of the 'garden variety' of poor readers (ibid, p590), a term originally coined by Gough & Turner (1986) in a study about decoding, reading and reading disabilities. The idea is based on the argument that an individual with dyslexia has a cognitive deficit that is by-and-large specific to reading and that were deficits to extend more widely into other cognitive areas of functioning, then such an individual would not be dyslexic but rather, would be a 'garden variety' poor reader. The most important point is Stanovich's contention that in dyslexia the deficit is vertical in respect to the individual's inherent cognitive powers and hence is domain-specific rather than is of a more horizontal nature which would be presented by extending across several cognitive domains such as attention and concentration, or visuo-spatial skills for example. This standpoint goes some way to explaining why much of the earlier dyslexia research is frequently rooted at the word-recognition level of phonological processing abilities. These are abilities which include, for example, difficulties in phonological decoding, inefficiencies in short-term memory processes, or in translating the written representation of phonemes into their correct sound segments, for example in properly distinguishing the vowel sound differences that are centrally located in (English) words such as boat, book, boot. This difficulty impacts progressively when children advance from learning the individual sounds of letters and short letter combinations into blending these into words and hence challenges the development of reading skills, indicating that the link between phonological processing and acquisition of reading skills is causal (Wagner & Torgesen, 1987), although later research suggested that this relationship may be bi-directional, that is, that it may equally be the actions of learning to read which enable phonological awareness (Wagner et al, 1994). However the most important point is that although phonological deficits may also occur in non-dyslexic poor readers, for them, deficits may also extend into other domains aswell (Stanovich, 1994).

But why do phonological processes impact so much on reading? It is beyond the scope of this thesis to enter into a lengthy discussion about the components and processes that drive the acquisition of reading skills but the core idea of 'phonics' as a learning-to-read procedure is that it teaches children to match up the sound components of words with individual letters or letter groups, and consequently is also directly related to the simultaneous acquisition of spelling competency. For example, children will learn that the sound k can result from a variety of letter or spelling sources: c, k, ck, or ch (in English at least). In reverse, being able to spot letters and letter combinations in new words that are being learned enables them to decode the word into its component sounds and hence reconstruct the sound of the complete word. So it is clear to see that disturbances that affect any or all of these letter-sound coding-decoding processes will impact on a child's ability to decypher text into speech, or to convert speech into writing. Essentially, this is the core of phoneme-grapheme-phoneme correspondence so interference in this correspondence process is likely to be evidenced primarily in the extent to which childrens' reading, spelling and writing skills develop or fail to develop, principally in comparison to their peers and to expected levels of progress when taking into account other significant factors such as their inherent academic ability or socio-environmental or cultural factors. Hence a key advantage of considering dyslexia as principally identifiable through core phonological deficits (Stanovich, 1986) is that it is relatable to what is commonly understood about the normal acquisition of reading skills (Snowling, 1998) and corresponding competencies in spelling and writing. Thus assessing individuals' capabilities in these key literacy skills through properly-developed, well-established norm-referenced, procedures can be significant contributors to a dyslexia-identifying process because the primary problem of reading-impairment in dyslexia is one of word recognition caused through weak phonological coding competencies (Stanovich, 1996).

However, understanding how phonological skills, reading writing and spelling, and dyslexia are interrelated is not in research stasis. Although it is fair to say that ideas continue to develop and evolve rather then emerge, such evolutions are incrementally advancing what is known about how reading and other literacy skills are acquired in the first place, and how these skills acquisitions might be adversely affected by disturbances that are inherent in some individuals, either attributable to dyslexia or to something else. As if this may not be challenging enough, it is compounded by rightly taking account of socio-environmental factors that have been shown to significantly impact on the development of literacy skills in early years and finding out more about how these factors need to be accounted for in experimental design and research outcomes. For example, because pre-literate early learners' phonological skills develop out of auditory experiences, it follows that immersion in high-quality oral experiences at home and pre-school is likely to enrich and more readily enable these skills (Goswami, 2008) and hence conversely, it is reasonable to suppose that social disadvantage or deprivation is at the very least likely to retard the typically expected development of phonological skills and hence competencies in literacy in early-years learners. Amongst many, three important factors can be distilled as pertinent to this thesis: firstly that there is evidence that for the intellectually capable at least, some individuals are not dyslexic enough for early-learning phonological deficits to have had a lasting impact on their literacy skills and that it may be other characteristics of their dyslexia which emerge as debilitating in later learning (Ramus & Szenkovits, 2008); secondly that some adults with dyslexia who had significant phonological deficits as children appear to have recovered when these skills are re-assessed in adulthood (Goswami, 2003) either through the development of strategic compensations or that their dyslexia has gone away, which, on the basis of dyslexia being understood as a neurobiological condition or even as a neurodiverse situation, seems unlikely; and lastly, that renewed interest in viewing dyslexia as a multidimensional condition (discussed below) as a way to explain the diversity of behavioural symptoms and also to bind together some of the more significant theories is particularly enabling progress to be made in understanding how dyslexia impacts on how adults engage with learning in higher education contexts and how to deal with it.

2. Dyslexia results from visual disturbances which render print in particular, difficult to access and hence to process. This can be due for example, to instabilities in binocular vision which may create issues in visual tracking both across lines of text and also from line to line (Bellocchi et al, 2013). Whilst it might be supposed that such physiological disorders may appear unrelated to cognitive functioning from the phonological processing point of view of dyslexia, clearly issues in following printed text accurately will make the reading and comprehension of it difficult and hence may present similar symptomh of poor reading. A considerable body of research about the vision differences of individuals with dyslexia tends to argue that these are more likely to be the most significant underlying causes of the dyslexia in deference to the syndrome being attributable to linguistic processing problems (Kirby et al, 2011) although it appears that studies start with the premise that their participants are dyslexic and then attribute this to various issues with their visual processing. However this may indicate a misunderstanding about how visual disturbances may be a factor in a dyslexic profile, and indeed, not necessarily a component in all dyslexic profiles. Stein & Walsh (1997) considered that a major issue in individuals with dyslexia is the ability to process fast incoming sensory information effectively from whichever sensory domain it comes from, indeed pointing out that before the phonological processing theory of dyslexia was proposed, dyslexia or word-blindness (Hinshelwood, 1896, Pringle-Morgan, 1896) was thought to be primarily a visual processing defect. It is beyond the scope of this thesis to expound the details of the visual processing system of the human brain other than to summarize that magnocellular cells, or M-cells, are part of the visual cortex of the brain which enables the experience of vision, but the magnocellular theory of dyslexia has emerged as one of the major theories. Principal amongst exponents of this explanation for dyslexia, Stein's extensive expertise in brain physiology places his research into finding causes for atypical development of the magnocellular system at the forefront of this theory of dyslexia. With arguments based on the premise that because the visual magnocellular system is involved in controlling the timing and sensitivity of eye movements, it follows that weak or abnormal development of this sub-structure of the brain will account for some reading challenges, especially in the early development of reading skills where clear perception of the orthography of a written language is key to comprehending the relationships between words and their sounds and meanings (Stein, 2001). But the greater picture that relates dyslexia to visual disturbances through the magnocellular theory remains controversial although research building on the earlier foundations of Stein continue to indicate that visuo-attentional processing issues may be at least one of the components of developmental dyslexia (Bellocchi et al, 2013). However visual differences described as jumping letters, fizzing text and dancing lines although common in many individuals with dyslexia, is equally absent in others (Shovman & Ahissar, 2006) with another study reporting that in assessments of visual target detection, dyslexic readers' performance showed no difference in comparison to non-dyslexic readers (Hawelka & Wimmer, 2007).

The issue may be further conflated because other less fundamental visual disturbances can also impair access to print. Of these, visual stress (ViS), scotopic sensitivity or Meares-Irlen Syndrome (MIS) may be an example of a distinct condition that sometimes occurs alongside, rather than is an indicator of dyslexia. Although apparently an optical problem where heightened sensitivity to lighting glare or contrast differences become accentuated in some circumstances are typically presented, other issues which can make reading challenging include restricted fields of vision which make only small areas of text become properly in focus, or challenges in maintaining focus on text for a sufficient time to properly enable comprehension (Irlen & Lass, 1989). Visual stress has been shown to be more of a visual processing issue rather than an optical dysfunction which can occur widely rather than specifically amongst individuals with dyslexia (Wilkins, 1995). However claims that MIS may have higher levels of prevalence amongst individuals with dyslexia than in the general population (Singleton & Trotter, 2005) are difficult to verify not least because evidence more usually points towards these being comorbid conditions rather than causally related (Kruk et al, 2008). Evans & Kriss (2005) supported this comorbidity idea but found that there was only a slightly higher prevalence of MIS in the individuals with dyslexia in their study in comparison to their control. Another recent study exploring dyslexia in a substantial sample of French schoolchildren (n=275) found that those who presented comorbid phonological and visual deficits did not show a more significant reading disability than those with phonological deficits alone (Saksida et al, 2016). However assessments of visual stress have been frequently included in dyslexia screening tests in recent years (Nichols, et al, 2009) and their use is common in educational contexts to ameliorate vision differences, notably in universities (Henderson et al, 2014). Placing tinted colour overlays on to hard-copy text documents and use of assistive technologies that create a similar effect for electronic presentation of text to relieve some of the symptoms of visual stress have been long-standing recommendations in students' Disabled Students' Allowance Assessment of Needs, indicated by anecdotal evidence at least. But evidence that this solution for remediating visual stress is more useful for those with dyslexia than for anyone else who experiences MIS or ViS is variable (eg: Henderson et al, 2013, Uccula et al, 2014). Ritche et al (2011) found that coloured overlays had no significant or immediate effect on reading ability in poor readers although their sample was small. However their conclusions were endorsed by a significant review of a substantial number of studies which concluded that apparent improvements in reading fluency as a result of the use of coloured overlays may be more likely due to placebo, Hawthorne and novelty effects (Griffiths et al, 2016). Even more significantly, one study found that use of overlays can actually be detrimental to reading fluency, particularly in adults (Denton & Meindl, 2016). So although the relationship between dyslexia and visual stress remains unclear, there is evidence to indicate that there may be an interaction between the two conditions which can have an impact on the remediation of either (Singleton & Trotter, 2005) and even though measurable improvements in reading fluency in individuals with dyslexia through use of coloured overlays or assistive technology applications that do the same may be difficult to attribute to anything other than the placebo effect, if students feel that they are gaining benefits then there is an argument to support their continued use.

3. Dyslexia is a rapid auditory processing disturbance and that the specificity of the difficulties in phonological awareness and processes are secondary to more fundamental issues with auditory processing. Pasquini et al (2007) outlined several auditory impairments that had been suggested as contributing to phonological processing difficulties and that as a result, offer another dimension to explanations for reading difficulties. These were auditory impairments most specifically related to deficits in the perception of short or rapidly varying sounds. Early work examining auditory discrimination capabilities between reading-impaired and control children had found a strong correlation between errors in nonsense word reading (to assess phonics skills) and errors in responding to rapidly presented auditory information (Tallal, 1980) which led to an hypothesis that some reading difficulties may be linked to low-level auditory perception disturbances, affecting the ability to learn to use phonics skills. Subsequent studies also found evidence amongst dyslexic children for poor auditory discrimination of certain sound contrasts in phonemes such as '~ba~' and '~pa~' (Adlard & Hazan, 1998, Serniclaes et al, 2004). But the relationship between auditory differences, whether these be classified as impairments, deficits or dysfunctions, and dyslexia remains a debated topic although it is reasonable to suppose that firstly, individuals who present with hearing challenges are likely to see these impact on their phonological awareness; but secondly that care must be taken to understand the distinction between auditory impairments and auditory processing impairments where the first is concerned with the physical capabilities to hear sounds and the second is about how accurately acquired acoustic information is subsequently interpreted by the brain. However, although both seem distinctly but equally likely to impact on the development of phonological skills and hence reading abilities (Witton & Talcott, 2018) is it beyond the scope of this study to consider these more deeply.

Cerebellar Deficit Theory of dyslexia4. Dyslexia results from a mildly disfunctional cerebellum which presents as a number of cognitive difficulties and this explanation for dyslexia is often referred to as the cerebellar deficit theory (CDT). Having emerged from earlier research which was grounded in an automatization deficit theory where individuals with dyslexia were found to have reduced performance in comparison to controls on tasks where balance had to be maintained whilst undertaking another task ((Nicholson & Fawcett, 1990), the theory was extended to include issues related to time estimation that were said to be reduced in dyslexic children (Nicholson et al, 1995). Soon after these ideas were consolidated into an hypothesis for the cause of developmental dyslexia which argued that disorders of cerebellar development, presenting as reading and writing difficulties, may be a factor in the explanation of dyslexic learning differences. (Nicholson et al, 2001). This idea is interesting, not least because it attempts to relate the major behavioural symptoms of dyslexia in children at least, to issues with automaticity in linguistic capabilities which need to be refined to enable fluent reading - and associated comprehension - writing, and spelling. There is not the scope in this thesis to discuss the theory in detail but the process chain represented (right (ibid, p510)) provides a compelling overall summary of the logic of the theory, offering explanations in part at least for the higher than normal predisposition towards weaker motor control competencies sometimes observed in dyslexic children (Fawcett & Nicholson, 1995). Whether this is evidenced by poor hand-writing in children with dyslexia may be uncertain, where although one study evidenced issues with handwriting competenciesin dyslexic children (Mattlew, 1992) another study found no link between slow handwriting and dysgraphia (Hanmstra-Bletz & Blote, 1993) and a later study identified that one reason why dyslexic children appear to be slower writers than their non-dyslexic peers could be attributed to them pausing more often during their writing proceses which was found to be related to their spelling competencies (Sumner et al, 2013). This is a link not established in Nicholson & Fawcett's (2001) model. The CDT process chain does however, also provide an acknowledgement of the phonological awareness issues associated with dyslexia by including these into the theoretical representation through what is termed the 'word recognition module' as a precursor to reading and spelling. Critics of the theory have had difficulty in reproducing the earlier evidence of compromised automaticity in the dual-task balancing experiment with children with dyslexia where results suggested a confounding factor between dyslexia and ADHD and that this may have unknowingly compromised earlier findings (Wimmer et al, 1999). Further, Ramus et al (2003) were only able to provide partial support for the cerebellar deficit theory finding that only half of the dyslexic children in their study presented any significant motor control challenges and that no evidence was found which linked motor skills and phonological and reading skills. However, their study did concede that those with dyslexia as well as those with other developmental disorders of which ADHD can be considered as one, may evidence greater challenges in activities that require finer motor control skills than may be witnessed in children who are not affected by such disorders.

5. Dyslexia is a manifestation of natural human neurodiversity: An alternative viewpoint about the nature of dyslexia constructs the syndrome in the context of of neurodiversity. The BRAIN.HE project (2005), now being revised but with many web resources still active and available, hailed learning differences as a natural consequence of human diversity. Pollak's considerable contribution to this thesis about dyslexia, both through the establishment of BRAIN.HE and notably drawn together in a collection of significant papers (Pollak, 2009), expounds the idea that dyslexia is amongst so-called 'conditions' on a spectrum of neuro-diversity which includes, for example, ADHD and Asperger's Syndrome. Particularly this view supports the argument that individuals with atypical brain 'wiring' are merely at a different place on this spectrum in relation to those others who are supposedly more 'neurotypical'. The greater point here is elegantly put by Cooper (2006), drawing on the social-interactive model of Herrington & Hunter-Carch (2001), and this is the idea that we are all neurodiverse and that it remains society's intolerance to differences that conceptualizes 'neurotypical' as in the majority. This may be particularly apparent in learning contexts where delivering the curriculum through a largely inflexible literacy-based system discriminates against particular presentations of neurodiversity (eg: Cooper, 2009). But one of the most significant features of the neurodiversity approach towards understanding dyslexia is a fundamental recognition of the syndrome's strengths in many areas of human functioning as well as acknowledging weaknesses in others. Armstrong (2015) argues that this means taking a more judicious approach to the identifying and labelling of cognitive or mental differences as disorders or disabilities is well overdue and that especially in the domain of education and learning, curriculum provision should be adapted in ways that enable and empower the neurodiverse student to flourish rather than be identified as different from their peers not least through removal from mainstream into differentiated learning situations (Armstrong, 2012). It is clear to see how this construction of dyslexia fits with the concepts of Universal Design for Learning, outlined earlier.

6. Describing dyslexia using a multifactoral approach - recent thinking that has emerged out of earlier ideas

dimensionsA significant body of recent work has attempted to understand dyslexia using a multifactorial approach, largely built on an early study which argued that attempts to understand the aetiology of dyslexia using the phonological deficit model, or alternatively where the observed symptoms were physiological and principally vision-related, were simplistic and a more comprehensive perspective based on the acceptance that dyslexia may be a variable rather than a determined learning circumstance may be a better model (Castles & Colheart, 1993). Although this study focused on reading deficiencies in children and took no account of wider differences in learning approaches that are now known to be apparently associated with dyslexia in adult learners, the study was important because even within the scope of this focus it appeared to identify two distinct subtypes of reading difficulties with one accounted for by deficits in whole-word recognition whilst the other by deficits in gaining a good grasp of letter-to-sound rules (ibid, p170). Where this is key to this subsection is that its conclusion was that individuals, (that is, children, it is assumed) who present developmental dyslexia do not form a homogeneous group and that different varieties of dyslexia are likely to exist, all distinctly characterised by a different blend of 'deficits' in comparison to the 'norm', of which this study claimed to have identified two varieties when dyslexia is considered as a reading disorder. A later study, which although did not appear to draw on this work by Castles & Colheart but where the outcome certainly adds value to their work, took a logical deductive approach to argue that dyslexia is a multifactorial condition where any number or combination of causes can lead to the same outcome and therefore that dyslexia should be best considered as a multiple deficit syndrome (Pennington, 2006). One study that was considered in a brief review of prior research on dyslexia as a multiple deficit syndrome identified it as being characterised by a weighted profile of deficits (Vellutino et al, 1991) which is pertinent to my project reported in this thesis because my research design, described in a later section, also adopts the weighted profile approach to describing the blend of dimensions which constitute a learning and study behaviour profile of university students to which can be ascribed a level of dyslexia-ness.

But the focus of this sub-section is to report on more recent, related work which is especially pertinent to the focus of this current project, reported in this thesis. Firstly, one study of dyslexia in French schoolchildren highlighted that it may be possible to identify dyslexia on the basis of several, apparently independent cognitive variables without assessing reading or spelling deficits (le Jan et al, 2011) by building a predictive, multivariate model of variables drawn from cognitive categories which included memory, visual-attention span, selective attention and auditory components. This is interesting because it detaches some of the basic literacy-skill dimensions from an identification process for dyslexia and concentrates instead on alternative attributes of the syndrome. This is arguably the most appropriate focus for dyslexia in higher education where, in generally academically capable university students, early literacy issues are likely to have been conquered, to an extent at least, either through individual strategic management of them or through use of assistive technologies. Secondly, studies in The Netherlands with Dutch university students described dyslexia (as opposed to diagnosed it) in adult learners at university using five factors determined through a principal component analysis of a wide range of dyslexia dimensions (Tamboer et al 2016). This is pertinent to my project firstly because it shows how useful factor analysis can be as a mechanism for identifying families of independent dimensions that together might be an effective identifier of dyslexia in certain circumstances - the process had also previously been used to identify latent variables (i.e. factors) in a study exploring phonological and visual-attention differences in French and English children (Bosse et al, 2007) and on differences in rapid automized naming tasks in Italian children (Di Filippo & Zoccolotti, 2012); and secondly that in higher education contexts, self-report questionnaires can serve as reliable identifiers of dyslexia in university students (Tamboer et al, 2014), a fact also suggested by Chanock et al, (2010) in their appraisal of a standard battery of diagnostic tests for dyslexia which they had found to be lacking in both sensitivity (correctly detecting dyslexia in known dyslexic students) and specificity (detecting dyslexia correctly in non-identified students) where their own, self-report questionnaire performed better for both parameters. As will be described later in this thesis, both of these elements of research design - using a self-report questionnaire to gauge dyslexia and principal component analysis of dyslexia dimensions - are key to addressing the research hypotheses being examined in this project.

Additional, interesting features also emerge from Tamboer's (2016) study, not least their interpretation about how to measure the severity of dyslexia and why to do so might be meaningful. Dyslexia severity was determined through a logistical regression analysis that classified the students in their sample (n=446) without considering which factors of dyslexia were to be taken as more significant than others. In this way, it was possible to sub-divide their sample into three, distinct subgroups being students with dyslexia, students with a very low likelihood of dyslexia and thirdly, students who brought with them no formal diagnosis of dyslexia but who were presenting many of the characteristics of dyslexia typically associated with formally identified dyslexic university students. This also resonates with the research design in my project where my interpretation of 'severity of dyslexia' is as a 'level of dyslexia-ness', and my study also relies on establishing the three subgroups defined similarly: that is, dyslexic students; non-dyslexic students; and what I have termed quasi-dyslexic students. Finally, it is apposite to report the nature of the five factors established in Tamboer's studies due to the similarities between these, and as will be reported later in this thesis, the factor analysis applied to the data collected in this project which also identified five factors of dyslexia that made sense in university-learning contexts. The five factors were distinguished as spelling; phonology; short-term memory; confusion; and complexity; determined through a reduction of 17 dyslexia dimensions in Tamboer's study, whereas for the data collected in my highly similar sample of university students (n=166) the factor analysis process reduced 20 dimensions into the five factors that I have designated as: reading, writing, spelling; verbalizing and scoping; working memory; organization and time-management; and thinking and processing. Although not taking the idea far, Tamboer's study also mentioned the usefulness of factor profiles as a means to view the deficits of dyslexic individuals in relation to non-dyslexic norms where a decision rule set by establishing a percentile-based boundary value appears to have been set as a condition for categorising respondents in the survey as either dyslexic or not from the pool of quasi-dyslexic students. Although this idea does not appear to have been developed it is interesting as it resonates with the earlier research design intention of this current study to use profiling as a dyslexia discriminator, which is described in detail in the Research Design section of this thesis and hence implies that there may be merit in developing this idea as part of a future study.

Finally, it is significant to note the interest of Tamboer and colleagues in relating the factors of dyslexia that their studies had determined with other constructs because adopting a similar approach is the core of the research design in this current study. Tamboer's interest was in relations between dyslexia and intelligence whereby the factor structure of dyslexia that their study had established was inter-related with factors of intelligence derived from the well-established Structure-of-Intellect Model (Guilford, 1988) additionally supported by the Raven's Matrices evaluations of non-verbal, fluid intelligence. It is not important to report in detail and comment on this here because it is considered peripheral to the focus of this study but the greater point to be made is that this approach of looking at the inter-relationships between factors of dyslexia and factors of another construct also sets a precendent for the research design approach in this current study which sets out to do the same where factors of academic confidence, operationalized through the Academic Behavioural Confidence metric are to be the comparator variable in this project.

Further work consolidated these Dutch studies into a dyslexia screening tool designed for use with university students or more widely with adult learners (Tamboer et al, 2017) which built on the power of factor analysis to generate components of dyslexia which appear to be stable and robust discriminators, and also strongly relied on the contribution of a self-report questionnaire to the final outcome of the screener which was reported to have had a high construct validity and a predictive validity that was even higher than that of the screening tool's tests (ibid, p167). Both of these findings auger well for the research design for this current project.

dyslexia typo
dyslexia typo translated

mouseover iconSignificant through its similar focus and also arising out of work in The Netherlands with Dutch university students are other studies which have searched for better screening tests for dyslexia in higher education contexts. Notable amongst these, Tops et al (2012) conducted a study which took the novel approach of pairing dyslexic and non-dyslexic students as the means to establish TEST and CONTROL group data and administered a large number of both verbal and non-verbal tests to establish comparisons across the student-pairs. The aim was to discover which tests were the most valuable to include in a dyslexia screener by having the most effective discriminative power. Where this is interesting and pertinent to this current study is twofold: firstly, and contrary to the findings of Tamboer's studies reported above, Tops et al arrived at just three sub-tests in their proposal for an effective screener which were all components of reading-writing skills, being word reading, word spelling, and phonological awareness; secondly, the research analysis processes of this study also add substance to the research design of this current project notably because analysis interest was focused on a correlation matrix of effect sizes which is similar to the process that has been developed in this current study as a means to understand more clearly the significance of interrelationships between factors of dyslexia and factors of academic confidence. Hence Tops et al's study sets a useful data-analysis precedent and although the sophistication of their statistical processes stretches beyond my capabilities as a researcher at this current time it nevertheless indicates that the approach I am adopting is broadly appropriate, if less comprehensive. Finally it should be acknowledged that Tops et al were at pains to point out that although the three tests their study was proposing as sufficient to provide the necessary discriminative power for identifying a possible dyslexia in a university student, this was not suggesting that these were the only areas where significant differences between dyslexic and non-dyslexic students were apparent in higher education contexts, nor was it pointing to the causes of dyslexia, rather the focus was on the predictive capacity of the screener which after all, was the aim of the research. One last point worthy of mention due to its pertinence with the overall stance of this current project was the observation that tacitly appeared to be questioning the relevance of a dyslexia-identifying process at university by leaving open the purposeful value of such a process, especially since their study further exposed the reality that a substantial proportion of students with dyslexia in their datapool had attributes and characteristics (i.e. deficits) which deviated significantly from the general pattern (ibid, p28). Not only is this concurrent with the continuing challenges that prevail in establishing a concrete definition of what we mean by 'dyslexia', but it also alludes to the idea that rather than continue to focus on identifying individuals whose profiles are atypical with a view to enabling compensations and adjustments to mediate between their learning differences and the academic challenges of university study, a more worthy focus would be to adjust their learning environment in ways which would enable them to be more readily accommodated.

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Thus the major theories of developmental dyslexia have been summarized but before moving forward to a wider discussion about the impacts of dyslexia on individuals and particularly the ways in which the syndrome affects their capabilities to engage with learning effectively, it is useful to report and reflect on the contribution made to the wider discourse on dyslexia by Uta Frith because her work has been significantly influential on much subsequent research.

In an important causal modelling framework, Frith (1999) endorsed equally important earlier work which argued that dyslexia was more than an explanation for reading difficulties because it should be more accurately considered as a syndrome which is characterized by a wide diversity of symptoms, indicators, dysfunctions, differences and challenges. These are typically exposed when individuals both young and not-so-young engage in learning. In taking on board the notion that dyslexia is a neurobiological condition, Frith suggests that this means blending together three broad churches of theoretical postulation being that firstly, dyslexia is a biological condition because it has a basis in the brain and that there are genetic, heritable factors which, to some extent, demarcate the dyslexic brain from the non-dyslexic one (Pennington, 1990, Ohlson et al, 2014. Swagerman et al, 2017); secondly that dyslexia is representative of cognitive differences which are frequently demonstrated by measurable anomalies in information-processing capabilities in comparison to standardized norms, for example in assessments of working memory (e.g.: Jeffries & Everall, 2004) although it has been shown that understanding the impact of dyslexia on working memory is complex not least because it depends on which domains of memory capabiities are assessed (Pickering, 2012), or of phonological skills (e.g.: Rack, 2017) when compared with the range of competencies observable in the majority of people; thirdly that dyslexia may be evidenced principally by behavioural differences, not least retardation in early reading skills in comparison to peers and by weak spelling competencies for example (e.g.: Stanovich, 1994). But these three levels of Frith's causal modelling framework are suggested to be if not bound together, then at least linked by environmental factors which can both contribute to and be influenced by each or all of the biological, cognitive and behavioural factors. For example, in supporting a university student to develop an effective strategy for searching more systematically for information resource, although this may become a mechanism to facilitate greater methodical effectiveness it will be a remediation of the symptom of being muddled and disorganized rather than a 'cure' for the underlying difficulty that may have its roots in the student's dyslexia. However in another example of say, a child who is a poor reader, this may be so due to elements in the child's socio-cultural background which may have retarded the typical early comprehension of the alphabet. This is an environmental factor and may be nothing to do with dyslexia at all. The most important idea to emerge out of Frith's analysis is that to focus on any one of the three levels to the exclusion of the others in an attempt to explain dyslexia would be erroneous and unscientific, flying in the face of substantial evidence accumulated from a range of studies of dyslexia at all three levels, some of which have been expounded in the sub-sections above.

Ramus (2004) extends Frith's three-level causal modelling framework by carefully reconsidering earlier neurobiological data to suggest that not only can the model be used to bring together the phonological and the magnocellular theories of dyslexia but that it may also be applicable to other functional differences observed for example in developmental dyscalculia and in ADHD. Fletcher et al (2007) appears to have adapted Frith's model to visualize the competing/contributory factors that can constitute a dyslexic profile by focusing on not only the integratability of Frith's earlier three factors but also heightening the bidirectional relationahip between neurobiological and environmental factors. This is an interesting adaptation because Fletcher's adjustments of Frith's model indicate the view that cognitive processes and behavioural and psychosocial factors are within the envelope of the neurobiology of dyslexia with the environment as more distinctly related. Although this is a only a subtle re-interpretation, Frith's original model implies these to be sequentially organized strata which were each placeholders for the various component parts of a dyslexia causation process. For example in describing dyslexia as a phonological deficit, the causal chain starts in the 'biological' layer by suggesting a left brain hemisphere disconnection as the root, leading to a phonological deficit in the cognitive layer which generates poor phoneme awareness as one of the behavioural characteristics. Embracing this causal chain are environmental factors such as teaching methods and literacy values (Frith, 1999, p203). But Fletcher's interpretation is also useful because it directly indicates the outcome of the causal factors as academic skills deficits that can be observed in dyslexic learners.


fletcher's visualization of dyslexia

[adapted from Fletcher, 2007, p3]

As a final, concluding remark it is of note that attempts have been made to compare and contrast the competing theories of dyslexia with an intention to explore whether they may be conjoined into a single, broad explanation for dyslexia rather than to favour one theory at the expense of the others. For example, Ramus et al (2003) conducted an intriguing case study with a small group of 17 dyslexic university students matched against a control group of 17 further students with no indications of dyslexia. The aim of the study was to administer a wide battery of assessments that attemped to evaluate dyslexia from all of the major theoretical perspectives to explore associations or disociations which may imply causal relationships between the array of characteristics widely observable in individuals with dyslexia. A significant factor of the research design was the deliberate intention to recruit academically capable adults as the research participants based on the argument that although such individuals are not likely to be representative of the wider population of adults with dyslexia, by virtue of their intelligence, likely resourcefulness and possible social privilege they may have benifited from good quality help with any early reading difficulties but particularly that they may be least likely to accumulate multiple disorders. Hence it would be more likely that it clear cases of the different subtypes of dyslexia may be identifiable. The tests were extensive and were devised to generate a comprehensive neuropsychological profile of all of the research participants by cataloguing the outcomes of psychometric, phonological, auditory, visual and cerebellar evaluations. Although all of the outcomes of the study were interesting, some may be considered more significant than others. For example, no significant relationship was found between auditory and phonological deficits despite a strong correlation between these domains' data. In the dyslexic group a greater diversity of outcomes was recorded in auditory assessements whereas more uniform results were obtained across the group in the phonological tests the conclusion about which was that auditory performance is not a predictor of phonological performance. The conclusions of the study re-affirmed the widely held view that the most significant issue for individuals with dyslexia is in phonological skills with impaired capabilities being observed in all of the students with dyslexia and although the incidence of deficits in all of the other components were variously observed in the dyslexic students in the sample, it was suggested that these are not so much causes of phonological deficits but more likely may be aggravating factors. Thus this study is considered important partly due to the significance of its research design as a comparator of the major theories of dyslexia but also because the study deliberately took dyslexia in university students as the focus, which resonates with the project being reported in this thesis.


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Equitability in learning systems - accommodating dyslexia?

The framework of traditional human learning experiences in curriculum delivery environments remains almost entirely 'text-based'. This is despite the last decade or two of very rapid development of alternative, technology or media-based delivery platforms that have permeated western democracies and much of the alternative and developing worlds (UNESCO, 2017). This 'new way' is embraced by an information society that sees news, advertising, entertainment and 'gaming', government and infrastructure services, almost all aspects of human interaction with information being delivered through electronic mediums. And yet formal processes of education by and large remain steadfastly text-based, especially at tertiary levels and which, although now broadly delivered electronically, still demand a conventional ability to properly and effectively engage with the printed word to consume knowledge and also to create it or to demonstrate understanding. This persistently puts learners with dyslexia - in the broadest context - and with dyslexia-like learning profiles at a disadvantage, especially where the concept of dyslexia attracts negatively connotated labels associated with difficulty and disorder because these imply deficit and hence is inherently unjust. Cavanagh (2013) succinctly highlights this tardiness in the delivery of education and learning to keep up with developments in information diversity and candidly observes that the collective field of pedagogy and andragogy should recognize that, rather than learners, it is curricula that is disabled and hence, needs be fixed.

Cavanagh is one of the more recent proponents of a forward-facing, inclusive vision of the barrier-free learning environment which is the Universal Design for Learning (UDL) (Rose & Meyer, 2000). UDL is attempting to tackle issues of justice in learning in ways that would declare dyslexia as at worst, a learning difference amongst a plethora of others, rather than a learning deficit, difficulty or disability and as such, is aligned with the construction of dyslexia as an example of neurodiversity outlined earlier. With its roots in the domain of architecture and universal accessibility to buildings and structures, the core focus of UDL is that the learning requirements of all learners are factored into curriculum development and delivery so that every student's range of skills, talents, competencies and challenges are recognized and accommodated without recourse to any kind of differentiated treatment to 'make allowances'. Hence it becomes the norm for learning environments to be much more easily adaptable to learners' needs rather than the other way around. This will ultimately mean that the text-related issues, difficulties and challenges that are undoubtedly due to deficits in some individuals and which can adversely impact on their successful engagement with conventional learning systems will cease to have much impact in a UDL environment. There is an increasing body of evidence to support this revolution in designing learning in this way where attention has been drawn to the learning-environment challenges facing different learners, ranging from equitable accommodation into the exciting new emphasis on developing STEM education (eg: Basham & Marino, 2013) to designing learning processes for properly including all students into health professions courses (eg: Heelan, et al, 2015). However other measures remain necessary to ensure an element of equitability in learning systems that fail to properly recognize and accommodate learning diversity although one route that outwardly seems attractive draws on the idea that matching teaching approaches to students' learning preferences has merit. Extensive earlier, and recently revisited research on learning styles has demonstrated that when teaching styles are aligned with student learning styles, the acquisition and retention of knowledge and more so, how it is subseqently re-applied, can be more effective and fosters better learning engagement (Felder, 1988, Zhou, 2011, Tuan, 2011, Gilakjani, 2012) and that a mismatch between teaching and learning styles can cause learning failure, frustration and demotivation (Reid, 1987, Peacock, 2001). For example, learning preferences skewed towards knowledge being presented visually is demonstrable in many dyslexic learners (Mortimore, 2008) although other studies in recent years have explored relationships between dyslexia and learning styles with mixed conclusions. For example, in the cohort of 117 university students with dyslexia in Mortimore's earlier (2003) study, no link was established between any preference for visuo-spatial learning styles and dyslexia which is not consistent with other research suggesting that one of the characteristizing aspects of dyslexia can be elevated visuo-spatial abilities in certain circumstances (Attree et al, 2009), not in learning it seems. In higher education, professional practice support for dyslexic students regularly advocates and provides assistive learning technologies such as concept-mapping tools on the basis of evidence that these are designed to make learning more accessible for those with visual learning strengths (Draffan et al, 2014). This continues to be a central provision of technology support for dyslexic students in receipt of the (UK) Disabled Students' Allowance although some evidence suggests that some alternative means to provide easier access to learning for dyslexic students appears to have equal learning value to both dyslexic and non-dyslexic students (Taylor et al, 2009). Indeed whether it is desirable to integrate student learning style preferences, however these may be categorized or defined, into pedagogic design has attracted mixed support, although as the turn of the century has witnessed a move towards a mass higher education system there has been renewed interest in learning styles, not least as means to accommodate the much wider diversity of student communities at university (Smith, 2002). The later advent of social media as a learning device or at least as a learning enabling device has perhaps scaled down interest in analysing learning styles per se because the more personal nature of accessing learning resources that is permitted in, for example, multimodal, mobile cloud computing technologies that may be accessible through social media portals for example, has enabled students with dyslexia to engage with their learning resources in ways to suit their individual learning preferences (Alghabban et al, 2017). Hence adapting teaching to suit learners is being achieved without recourse to finding out in detail how learners prefer to learn. This approach to presenting more personalized learning experiences is enshrined in what may be considered as perhaps UDL in the technology age through the advent of Smart Learning Environments (SLEs). The ethos of UDL is captured by a recent definition of a SLE as a learning place which features widespread incorporation of innovative technologies to permit greater flexibility, adaptation, engagement and feedback for learners (Spector, 2014) and these are learning environments which, by turning around the idea of curriculum delivery into curriculum uptake, foster student engagement at a highly personalized level. For those with learning differences in whatever form, this approach is likely to ameliorate many of the current challenges faced by such learners (Lenz, 2016) and hence make learning fairer and more equitable so that 'difference', 'disorder', 'difficulty', 'deficit' will have much reduced relevance in a such a learning environment.


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Labels, categories, dilemmas of difference, inclusivity

The issue of difference has a long history both in education and in society more generally. Amongst communities of learning, educationalists and practitioners have agonized for decades about how to best deal with learners who are 'different' without stigmatizing them on the basis of their difference. It must surely be universally agreed that education and learning should inhabit a space in which prejudice is absent, in which everyone is treated fairly and non-judgmentally, where discrimination is not tolerated and especially where equality of opportunity underpins educational provision. But identifying a trait of difference where this is established by a dominant, majority group will risk emboldening a conceptual separation based on that trait; whereas conversely, non-identification of minority groups or a non-acknowledgement of difference equally risks discrimination through the application of majority norms and perspectives without regard for the possible alternative needs of a minority (Minow, 1985). Such is the dilemma of difference which has driven the inclusion/exclusion debate in education since it was recognized that not all learners learn in the same ways and hence that traditional, conventional teaching and curriculum delivery may not be suited to everyone in the classroom. However inclusion is variously conceptuatlised in educational contexts (Messiou, 2017) ranging from being primarily concerned with disability and special education needs to defining inclusion as an objectively standardizing approach to not only education but to society more generally through the adoption of principles and values such as equity and respect for diversity (Ainscow et al, 2006). Messiou focuses on Ainscow et al's principled approach to defining inclusion adding that in practice, this means more than talking about the facilitation of active involvement and participation in learning contexts because it should embrace the wider concepts of presence and achievement as well as 'where' and 'how' children are educated. In other words, focusing on all students rather than on differentiated groups (op cit), which implies that to do otherwise may lead to marginalization and feelings of 'otherness' (French & Herrington, 2008, Mortimore, 2012).

That is, as essential for establishing righ categorizationIn the face of this being quite a convincing social justice perspective on inclusivity in education, by taking a reactionary and perhaps angry standpoint it might be argued that there is an alternative, well-rehearsed polemic that has sought to justify the categorization of learners as a convenient exercise in expediency. ts to differentiated 'support', this being considered the most efficacious form of intervention as a mechanism which outwardly at least, is supposedly designed to meet the different learning needs of minority groups (Elliott & Gibbs, 2008). This is support which aims to metaphorically shoe-horn a learner labelled with 'special needs' into a conventional learning box and in higher education contexts at least, this will be through the application of 'reasonable adjustments' to curriculum access as a remediative process to compensate for learning challenges purportedly attributed to these individuals' apparent learning disabilities. Outwardly, this is neat, usually well-meaning, ticks boxes, appears to match learner-need to institutional provision, and ostensibly 'fixes' the learner in such a way that the academic playing field is levelled so as to reasonably expect such learners to 'perform' in a fair and comparable way with their peers. Richardson (2009) reported on analysis of datasets provided by HESA that this appears to work for most categories of disabled learners in higher education, also demonstrating that where some groups did appear to be under-performing, this was due to confounding factors that were unrelated to their disabilities. However even setting aside the undesirability of such solutions in the context of a properly inclusive practice, such accommodations may even have positively discriminated learners who present 'differences' leading to unfair academic advantage because the 'reasonable adjustments' that may have been made were somewhat arbitrarily determined and lack scientific justification (Williams & Ceci, 1999). telling liesIndeed it has also been reported that some students, witnessing their friends and peers in possession of newly-provided laptops, study-skills support tutorials and extra time to complete their exams all of which has been provided through support funding, go to some lengths to feign difficulties in order to gain what they perceive to be an equivalent-to-their-friends, but better-than-equal academic advantage over others not deemed smart enough to play the system (Harrison et al, 2008, Lindstrom et al, 2011).

But there is some argument to suggest that contrary to dyslexia being associated with persistent failure (Tanner, 2009), attaching the label of dyslexia to a learner - whatever dyslexia is - can be an enabling and empowering process at university exactly because it opens access to support and additional aids, especially assistive technology which has been reported to have a significantly positive impact on study (Draffan et al, 2007). It has been demonstrated that the psychosocial impacts of being designated as dyslexic have led some individuals to embrace their dyslexia and to identify and use many personal strengths in striving for success, in whatever field (Nalavany et al, 2011). Outwardly this seems to be strongly aligned with the neurodiversity approach however Grant (2009) points out that neurocognitive profiles are complicated and that the identification of a specific learning difference might inadvertently be obfuscated by a diagnostic label, citing dyslexia and dyspraxia as being very different situations but which share many, perhaps confusing similarities at the neurocognitive level. Ho (2004) argued that despite the 'learning disability' label being a prerequisite for access to differentiated provision in learning environments and indeed, civil rights protections, these directives and legislations have typically provided a highly expedient route for the tacit adoption by official channels and processes of the medical model of learning disabilities - being the one where disability is considered as the disabled individual's own fault - and hence pay less attention or even ignore completely other challenges in educational systems. So one conclusion that may be drawn here is that as wherever schools, and subsequently universities persist in relying heavily on reading to impart and subsequently to gain knowledge, and require writing to be the principal medium for learners to express their ideas and hence for their learning to be assessed, by explaining the poor performance of some groups of learners by pathologizing them may enable institutions to avoid examining their own failures (Channock, 2007). Although this might be viewed as a stinging appraisal of well-intentioned attempts to accommodate differences it cuts to the quick of how properly the agendas of inclusivity may be both designed and implemented in learning institutions to truly provide equitable learning opportunities for all.

stigmaOther arguments focus on stigmatization associated with 'difference': On the disability agenda, many studies examine the relationship between disability and stigma with several drawing on social identity theory, this being the construct originally theorized by Tajfel & Turner (1979) who suggested that part of an individual's concept of who they are, their self-identity, comes from their sense of belonging to a particular group, hence their social identity. Moreover, that as part of their group, individuals align themselves with group identity, norms, attitudes and behaviours (Tajfel, 1982). For example, Nario-Redmond et al (2012) in a study about disability identification outlined that individuals may cope with stigma by applying strategies that seek to minimize stigmatized attributes but that often this is accompanied by active membership of stigmatized groups in order to enjoy the benefit of collective strategies as a means of self-protection. A much earlier study identified the self-protective dimension of group attachments especially where the group is representative of individuals marginalized by the wider society as a result of their difference, whether this be through disability or any other minority characteristic judged to be worthy of exclusion by the wider, conformist majority (Crocker & Major, 1989). Social stigma itself can be disabling and the social stigma attached to disability is particularly so, not least due to a historical attribution of disabilty to the individual themselves - that is, adopting the medical model of disability which considers a disabling condition pathologically (Burch & Sutherland, 2006). Indeed, there is a significant body of research that identifies disadvantages in all walks of life that result from the stigmatization of disabilities (eg: McLaughlin, et al, 2004, Morris & Turnbill, 2007, Trammel, 2009). Specifically in an education context, there is not necessarily a connection between labels of so-called impairment and the categorization of those who require additional or different provision (Norwich, 1999) and even when 'disability' is arguably softened to 'difficulty' or even more so to 'difference', the picture remains far from clear. One study suggested that stigmatization may already exist in advance of labelling, or even in the absence of labelling at all (Riddick, 2000) tacitly implying that marginalization is socially constructed ahead of an identification or a diagnosis and that either identifying, or not, can be equally detrimental. What could illuminate more clearly the dilemma of difference?

Sometimes the stigma is more associated with the additional, and sometimes highly visible, learning support - students accompanied by note-takers for example - designed to ameliorate some learning challenges (Mortimore, 2013) with some studies reporting a measurable social bias against individuals with learning disabilities who were perceived less favourably than their non-disabled peers (eg: Tanner, 2009, Valas, 1999,). This was not the least also evidenced from the qualitative data that has been collected in this current research project which will be presented later in Section 4. But similar evidence relating to this kind of social bias was also recorded in a study exploring the disclosure of dyslexia in cohorts of students who successfully entered university to train as nurses which highlighted the unease of these student-nurses about their local learning communities becoming aware of their dyslexia (Morris & Turnbill, 2007), although it is possible that as a conclusion, this may have been confounded by nurses' awareness of workplace regulations relating to fitness to practice and how their dyslexia may very significantly reduce their likelihood of gaining employment. It has also been recorded that the dyslexia (learning disability) label might even produce a differential perception of future life success and other attributes such as attractiveness or emotional stability despite such a label presenting no indication whatsoever about any of these attributes or characteristics (Lisle & Wade, 2014). Perhaps the most concerning, is evidence that parents and especially teachers may have lower academic expectations of young people attributed with learning disabilities or dyslexia based on a perceived predictive notion attached to the label (Shifrer, 2013, Hornstra et al, 2014) and that in some cases, institutional processes have been reported to significantly contribute to students labelled as 'learning-disabled' choosing study options broadly perceived to be less academic (Shifrer et al, 2013).

pseudoscienceStanovich has written extensively on dyslexia, on inclusivity and the impact of the labelling of differences. His position is principally two-fold: Firstly to fuel the debate about whether dyslexia per se exists, a viewpoint that has emerged from the research and scientific difficulties that he claims arise from attempts to differentiate dyslexia from other poor literacy skills; and secondly that given that dyslexia in some definition or another is a quantifiable characteristic, argues strongly that as long as the learning disability agenda remains attached to aptitude-achievement discrepancy measurement and fails to be a bit more self-critical about its own claims, (Stanovich, 1999), its home in the field of research will advance only slowly. Indeed a short time later the learning disabilities field was described as 'not ... on a scientific footing and continu[ing] to operate on the borders of pseudoscience' (Stanovich, 2005, p103). His position therefore fiercely advocates a more inclusive definition of learning disabilities as being one which effectively discards the term entirely because it is 'redundant and semantically confusing' (op cit, p350) a persistent argument that others echo. Lauchlan & Boyle (2007) broadly question the use of labels in special education, concluding that aside from being necessary in order to gain access for support and funding related to disability legislation, the negative effects on the individual can be considerable and may include stigmatization, bullying, reduced opportunities in life and perhaps more significantly, lowered expectations about what a 'labelled' individual can achieve (ibid, p41). Norwich (1999, 2008, 2010) has written extensively about the connotations of labelling, persistently arguing for a cleaner understanding of differences in educational contexts because labels are all too frequently stigmatizing and themselves disabling, referring to the 'dilemma of difference' in relation to arguments 'for' and 'against' curriculum commonality/differentiation for best meeting the educational needs of differently-abled learners. Armstrong & Humphrey (2008) suggest a 'resistance-accommodation' model to explain psychological reactions to a 'formal' identification of dyslexia, the 'resistance' side of which is typically characterized by a disinclination to absorb the idea of dyslexia into the self-concept, possibly resulting from perhaps more often, negatively vicarious experiences of the stigmatization attached to 'difference', whereas the 'accommodation' side is suggested to take a broadly positive view by making a greater effort to focus and build on the strengths that accompany a dyslexic profile rather than dwell on difficulties and challenges.

diversityMcPhail & Freeman (2005) have an interesting perspective on tackling the challenges of transforming learning environments and pedagogical practices into genuinely more inclusive ones by exploring the 'colonizing discourses' that disenfranchise learners with disabilities or differences through a process of being 'othered'. In other words how difference or disability is a separatist construction that is then the submissive party in societal power and control relationships. Their conclusions broadly urge educationalists to have the courage to confront educational ideas and practices that limit the rights of many student groups (ibid, p284). Pollak (2005) reports that one of the prejudicious aspects of describing the capabilities of individuals under assessment is the common use of norm-referenced comparisons. This idea is inherently derived from the long-established process of aligning measurements of learning competencies to dubious evaluations of 'intelligence', standardized as these might be (for example Wechsler Intelligence Scale assessments to identify just one), but which fail to accommodate competencies and strengths which fall outside the conventional framework of 'normal' learning capabilities - that is, in accordance with literacy-dominant education systems.

Norwich (2013) also talks more about 'capabilities' in the context of 'special educational needs', a term he agrees, is less than ideal. The 'capability approach' has its roots in the field of welfare economics, particularly in relation to the assessment of personal well-being and advantage (Sen, 1999) where the thesis is about individuals' capabilities to function. Norwich (op cit) puts the capability approach into an educational context by highlighting focus on diversity as a framework for human development viewed through the lens of social justice which is an interesting parallel to Cooper's thesis on diversity taken from a neurological perspective as discussed above. This all has considerable relevance to disability in general but particularly to disability in education where the emphasis on everyone becoming more functionally able (Hughes, 2010) is clearly aligned with the notion of inclusivity and the equal accommodation of difference because the focus is inherently positive as opposed to dwelling on deficits, connecting well with the principles of Universal Design for Learning outlined above.


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Impact of dyslexia being identified

Exploring the immediate emotional and affective impact that the process of evidencing and documenting a learner's study difficulties has on the individual under scrutiny is a pertinent and emerging research field. (Armstrong & Humphrey, 2008). Perhaps as an indication of an increasing awareness of the value of finding out more about how an individual with dyslexia feels about their dyslexia, there have been relatively recent research studies that relate life or learning histories of individuals with dyslexia. For example, Dale & Taylor (2001) found that one group of adult dyslexic learners attending a focus group seeking feedback about a short adult learning study-skills awareness course were citing the non-recognition of their dyslexia in earlier schooling as inherently disabling; Burden & Burdett (2007) asked 50 adolescents to construct mind-picture images of what dyslexia meant to each of them to explore the affective dimension of dyslexia where metaphor emerged as a powerful tool in enabling these teenagers to express how they felt about their dyslexia with the outcome that most described it as an insurmountable barrier in learning contexts at least; Evans (2013) explored how student nurses constructed their dyslexic identity finding that being made to feel stupid was linked to dyslexia both in historical learning contexts as well as in their current learning interactions which in these individuals' circumstances widely led to their dyslexia not being disclosed in their workplaces; Cameron & Billington (2015) looked at how a small group of university students with dyslexia constructed their dyslexic identity with significant themes emerging: firstly how these students perceived had internalized the power of assessment gradings as markers of worth to interact with the status of their dyslexic label; secondly about the tensions between the idea of high levels of literacy being aspirational and acknowledging their challenges in reading, writing and spelling; and lastly an uncertainty about whether or not dyslexia was a morally valuable label to be given. In a similar, higher education context, Cameron's later (2016) study exploring the day-to-day experiences of students with dyslexia at university identified several consistent themes amongst these students. These included challenges in translating thoughts into coherently expressed ideas, especially when presenting these to peers and lecturers where feelings of not being good enough through being not properly understood increased negative feelings of self-worth, and also about not feeling welcome in academic learning spaces due to experiences of being perceived by peers as 'different'. One intriguing study attempts to tease out meaning and understanding from these through the medium of social media (Thomson et al, 2015) where anonymous 'postings' to an online discussion board hosted by a dyslexia support group resulted in three, distinct categories of learning identities being established: learning-disabled, differently-enabled, and societally-disabled. It was observed from these postings that while some contributors took on a mantle of 'difference' rather than 'disability' hence expressing positiveness about their dyslexia-related strengths, most appeared to be indicating more negative feelings about their dyslexia, with some suggesting that their 'disability identity' had been imposed on them (ibid, p1339) not the least arising through societal norms for literacy.

It may be through a collective study (in the future) of others' research in this area that conclusions can be drawn relating to the immediate impact on individuals when they learn of their dyslexia. However in the absence of any such meta-analysis being unearthed so far, even a cursory inspection of many of the learning histories presented in studies that have been explored to date generally reveals a variety of broadly negative and highly self-conscious feelings when individuals learn of their dyslexia. Although such reports strongly outweigh those from other learners who claimed a sense of relief that the 'problem' has been 'diagnosed' or that an explanation has been attributed to remediate their feelings of stupidity as experienced throughout earlier schooling, it is acknowledged that there is some evidence of positive experiences associated with learning about ones dyslexia, as reported earlier. This current project aims to be a contributor to this discourse as one facet of the questionnaire used to collect data sought to find out more about how dyslexic students learned about their dyslexia. A development feature of the project will co-relate the disclosures provided to respondents' narratives about how they feel about their dyslexia where this information has also been provided. As yet, a methodology for exploring this has still to be developed and this process may also be more likely to be part of the future research that it is hoped will stem from this current project.


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To identify or not to identify? - Is that the question?


So a dilemma arises about whether or not to (somehow) identify dyslexic learning differences. On the one hand, there is a clear and strong argument that favours progressively changing the system of education and learning so that difference becomes increasingly irrelevant; whilst on the other, the pragmatists argue that taking such an approach is idealistic and unachievable and that efforts should be focused on finding better and more adaptable ways to 'fix' the learner. In the short term at least the pragmatists' approach is the more likely to persist but in doing so, constructing an identification process for learning differences that attributes positiveness onto the learning identity of the individual rather than burdens them with negative perceptions of the reality of difference would seem to be a preference. This is important for many reasons, not the least of which is that an assessment/identification/diagnosis that focuses on deficit or makes the 'subject' feel inadequate or incompetent is likely to be problematic however skilfully it may be disguised as a more neutral process. Not least and despite some evidence to the contrary this may be due to the lasting, negative perception that an identification of dyslexia often brings, commonly resulting in higher levels of anxiety, depressive symptoms, feelings of inadequacy and other negative-emotion experiences which are widely reported (eg: Carroll & Iles, 2006, Ackerman et al, 2007, Snowling et al, 2007). This is especially important to consider in the design of self-report questionnaire processes where replies are likely to be more reliable if the respondents feel that the responses they provide are not necessarily portraying them poorly, particularly so in the self-reporting of sensitive information that may be adversely affected by social influences and which can impact on response honesty (Rasinski et al, 2004).

But identifying dyslexia through a binary process is not especially helpful because dyslexia is most recently being constructed as a multifactorial or multidimensional situation, as outlined above in sub-section ##, where dyslexic individuals present a wide range of characteristics and attributes that reflect both skills and talents as well as difficulties and challenges, all to varying degrees. Hence devising a process for gauging the level of dyslexia that an individual may present can have value in an educational context because it might encourage a better alignment of learning strategies to learning strengths whilst at the same time identifying ideas for lessen the impact of difficulties and weaknesses, especially in literacy-based learning activities where the dyslexic student, intellectually capable as they are, may still experience some challenges when engaging with an academic environment. It is the stance of this project that gauging dyslexia as the severity of dyslexia is not appropriate because one of the underlying strands of the study is to try to approach the dyslexic condition with a positive outlook. To contextualize the level of dyslexia as the severity of dyslexia implies the opposite, as the argument thence has tried to present, not least because to do so aligns dyslexia with the deficit/discrepancy model and worse, when dyslexia is diagnosed, alludes to it being a disabling illness which needs treatment much in line with the now outdated medical model of disability. However it has already been established (above) that in the current climate, labelling a learner with a measurable learning challenge such as dyslexia, which under the terms and descriptors of the Equality Act 2010 is classified as a disability (in the UK), does open access to learning support services which are designed to broadly scaffold the reasonable adjustments and other accessibility constructs that are offered by higher institutions to comply with disability legislation to try to ensure equal learning opportunities for disabled students. So this at least is one justification for devising mechanisms for assessing firstly whether an individual is dyslexic or not, but also determining the extent of the dyslexic learning differences in order to establish the required range of learning support provisions to enable this student to function more equally in the predominantly non-dyslexic learning environment of university.

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Measuring dyslexia - "how 'dyslexic' am I?"

It might be thought that 'measuring dyslexia' is a natural consequence of 'identifying dyslexia' and although the commonly used dyslexia screening tools such as the Lucid Adult Dyslexia Screener (LADS) software application or the Dyslexia Adult Screening Test (DAST) offer comprehensive outputs from a range of tests and assessments, these all require interpretation and in UK universities this is usually the task of a Disability Needs Assessor. An indication of dyslexia that results from a screening generally results in a recommendation for a 'full assessment' which, in the UK at least, has to be conducted by an educational psychologist. However even such a comprehensive and daunting 'examination' does not produce much of a useful combined measurement to describe the extent of the dyslexic difference identified, with the collective outputs from the batteries of assessments generally generating broad descriptors of 'mild', 'moderate' or 'severe' to indicate how dyslexic and individual is. So even though these assessment tools do provide scores obtained on some of the tests that are commonly administered, these are generally of use only to specialist practitioners and not usually presented in a format that is very accessible, especially so to the student who is being assessed. For example, in this researcher's own experience of working with students with dyslexia at university one student recounted that on receiving the assessment indication of his dyslexic learning difference he asked how dyslexic he was, to be told that is was mild to moderate, leaving him none-the-wiser (respondent #9, Dykes, 2008, p95). And so in addition to facilitating a route towards focused but differentiated study skills support interventions this idenfitying or assessment process is an essential component for any claim to the Disabled Students' Allowance (DSA) although ironically the assessment has to be financed by the student and is not recoverable as part of any subsequent award. This in itself may be a barrier to formal assessment given that it is acknowledged that university communities are likely to include a significant proportion of unidentified dyslexic students (Tops et al, 2012, Lindgren, 2012, Belger & Chelin, 2013), hence those who may have become aware that it could be dyslexia which may account for their difficulties may be disinclined to incur the costs of an assessment to confirm their apprehension.

Certainly for school-aged learners, identifying dyslexia is rooted in establishing capabilities that place them outside the 'norm' in assessments of competencies in phonological decoding and automaticity in word recognition and in other significantly reading-based evaluations as has been broadly outlined above. Sometimes these include assessments of working memory such as the digit span test, which has relevance to dyslexia because working memory abilities have clear relationships with comprehension. If a reader gets to the end of a long or complex sentence but fails to remember the words at the beginning long enough to connect with the words at the end then clearly this compromises understanding. All of these identifiers carry useful, quantifiable measures of assessment although they are discretely determined and not coalesced into an overall score or value but at early-learning levels have proved to be sufficient in enabling educators to establish a dyslexia in children. However evidence suggests that identifiers used for catching the dyslexic learner at school do not scale up very effectively for use with adults (eg: Singleton et al, 2009). This may be especially true for the academically able learners that one might expect to encounter at university who can, either actively or not, mask their difficulties (Casale, 2015) or even feign them if they perceive advantage to be gained (Harrison et al, 2008). However, recent studies continue to reinforce the idea that dyslexia is a set of quantifiable cognitive characteristics (Cameron, 2016) but which extend beyond the common idea that dyslexia is mostly about poor reading, writing and spelling, certainly in the university environment although it is acknowledged that difficulties associated with compromised literacy skills checklist because dyslexia in one form or another persists into adulthood (Hanley, 1997, Elbro et al, 1994, Kirby et al, 2008) and this is especially apparent in studies that focus on the impact of phonological awareness on reading ability (Shaywitz et al, 1999, Svensson & Jacobson, 2003). It is also evident that identifying dyslexia in adults is more complicated than in children, especially in broadly well-educated adults attending university because many of the early difficulties associated with dyslexia have receded as part of the progression into adulthood (Kemp et al, 2008, Undheim, 2009) either as a result of early support or through self-developed strategies to overcome them. Such individuals have come to be regarded as 'as compensated adult dyslexics' in some studies (eg: Lefly & Pennington, 1991), at least in regard to their phonological processing skills and hence reading, writing and spelling abilities. The research is far from conclusive about the reasons for dyslexia compensation, so it is of significant interest to note that very recent research concerning the abilities of dyslexic university students to overcome the persistent phonological deficits which have essentially characterized the identification of their dyslexia, suggests that this may be achieved through their development of morphological knowledge in reading (Cavalli et al, 2017). In linguistics, morphology is about the structure of words in terms of morphemes where these are the smallest indivisible elements of words which take or indicate meaning, for example in 'unhappy' the 'un' indicates 'not', or in 'teacher' the 'er' indicates one who teaches. Hence morphemes are more related to meaning whereas phonemes are related to auditory correpondences in work construction. Many languages but particularly English, tend to be comprised of morphemes as well as phonemes and this may explain why although phonological awareness may be a good indicator of reading skills it is not infallible because sensitivity to each of these word units might be significant in decoding abilities (Singson et al, 2000). The Cavalli et al study revealed that in the higher-functioning adults that comprised their sample of university students (n=40) there was a significant disassociation between the development morphological abilities and phonological ones and that the magnitude of this disassociation correlated with reading ability (ibid). This result was in keeping with an earlier study (Martin et al, 2014) which also suggested that this development in strong morphological awareness could be a significant compensation in the development of literacy skills for dyslexic students with both of these recent studies building on an increasing body of research that is exploring which aspects of the reading ability required in university students have been compensated in those with dyslexia and how this compensation has been executed (eg: Parrila & Georgiou, 2008). What emerges from this paragraph's overview is evidence that at university, other dimensions of dyslexia that are aside from reading ability and phonological processing may be more characteristic of many dyslexic university students' learning needs because these adults have often developed strong strategies for dealing with earlier reading weaknesses and hence, identification and assessment processes that have literacy and decoding skills at their core are not so much less relevant than such tests may have been for earlier-years learners but that other issues that may be related to their dyslexia are likely to be more sigificant in university learning contexts.

The last two decades or so have seen the development of a number of assessments and screening tests that aim to identify – but not specifically to measure - dyslexia in adults and particularly in higher education contexts as a response to the increasing number of students with dyslexia attending university (HESA, 2018, Dobson, 2018). An early example of a screening assessment for adults is the DAST (Dyslexia Adult Screening Test) developed by Nicholson & Fawcett (1997). This is a modified version of an earlier screening tool used with school-aged learners but which followed similar assessment principles, that is, being mostly based on literacy criteria although the DAST does include non-literacy based tests, namely a backward digit span test a non-verbal reasoning test and a posture stability test – which seems curiously unrelated although it is claimed that its inclusion is substantiated by pilot-study research. Limitations of the DAST to accurately identify students with specific learning disabilities have been evidenced, for example Harrison & Nichols (2005) felt that their appraisal of the DAST indicated inadequate validation and standardization. Computerized screening tools have been available for some time, such as the LADS (Lucid Adult Dyslexia Screening, (Lucid Innovations, 2015)) which claims to generate a graphical report that collects results into a binary categorization of dyslexia as the individual being 'at risk' or 'not at risk'. Aside from being a coarse discriminator, 'at risk' implies that dyslexia is being viewed through the lens of negative and disabling attributes and also it is unclear what an identified individual is 'at risk' of, possibly implying that an individual can develop further or worsening dyslexic characteristics if the condition remains unidentified? The screening test comprises 5 sub-tests which measure nonverbal reasoning, verbal reasoning, word recognition, word construction and working memory (through the backward digit span test) and indicates that just the final three of these sub-tests are dyslexia-sensitive. The reasoning tests are included based on claims that to do so improves screening accuracy and that results provide additional information 'that would be helpful in interpreting results' (ibid, p13), that is, provides a measure of the individual's 'intelligence' - which, in the light of Stanovich's standpoint on intelligence and dyslexia mentioned earlier, is of dubious worth and might be an indication that the authors of the screening test are of the opinion that there is an associative relationship between intelligence and dyslexia, an idea which has been repeatedly debunked. For example Gus & Samuelsson (2002) argued that there is no clear, causal relationship between intelligence level and decoding skills not least because intelligence is a 'fuzzy concept' which can be assessed in a wide variety of ways.

studentsWarmington et al (2013) responded to the perception that dyslexic students present additional learning needs in university settings in comparison with earlier-years learners also stating that as a result of the increased participation in higher education in the UK more generally there is likely to be at least a corresponding increasing in the proportion of students who present disabilities or learning differences. The Warmington et al study quotes HESA (Higher Education Statistics Agency) figures for 2006 as 3.2% of students entering higher education with dyslexia. A very recent enquiry directly to HESA elicited data for 2013/14 which indicated students with a learning disability accounting for 4.8% of the student population overall (Greep, 2017) this figure also accounting for 48% of students disclosing a disability. Although HESA does not identify dyslexia specifically, Greep stated that HESA is of the opinion that dyslexia is by far the most numerous category of learning disability, which makes students with dyslexia the biggest single group of students categorized with disabilities at university, such that they are currently labelled (ibid). It is also of note that the HESA data is likely to be an under-reporting of students with a specific learning difficulty (that is, dyslexia) because where this occurs together with other impairments or medical/disabling conditions this is reported as a separate category with no way of identifying the multiple impairments (ibid). At any rate, both of these data are consistent with the conclusions that the number of students with dyslexia entering university is on the rise. Given earlier mention above about dyslexia being first-time identified in a significant number of students post-entry it is reasonable to suppose that the actual proportion of dyslexic students at university is substantial which will also include many unidentified dyslexic students. Indeed, this research is relying on finding 'hidden' dyslexics in the university community in order to address the research questions and hypothesis.

The York Adult Assessment-Revised (YAA-R) was the focus of the Warmington et al study which reported data from a total of 126 students of which 20 were known to be dyslexic. The YAA-R comprises several tests of reading, writing, spelling, punctuation and phonological skills that is pitched most directly to assess the literacy-related abilities and competencies of students at university (ibid). The study concluded that the YAA-R has good discriminatory power of 80% sensitivity and 97% specificity but given that the focus of the tests is almost entirely on literacy-based activities, it fails to accommodate assessments of the wide range of other strengths and weaknesses often associated with a dyslexic learning profile that are outside the envelope of reading, writing and comprehension. A similar criticism might be levelled at the DAST as this largley focuses on measuring literacy-based deficits. Indeed, Channock et al (2010) trialed a variation of the YAA-R adjusted in Australia to account for geographical bias in the UK version as part of a search for a more suitable assessment tool for dyslexia than those currently available. Conclusions from the trial with 23 dyslexic students and 50 controls were reported as 'disappointing' due not 'to the YAA-R's ability to differentiate between the two groups, but with it's capacity to identify any individual person as dyslexic' (ibid, p42) as it failed to identify more than two-thirds of previously assessed dyslexic students as dyslexic. Channock further narrates that self-reporting methods proved to be a more accurate identifier - Vinegrad's (1994) Adult Dyslexia Checklist was the instrument used for the comparison. A further criticism levelled at the YAA-R was that it relied on data collected from students in just one HE institution, suggesting that that differences between students in different institutions was an unknown and uncontrollable variable which was not accounted for but which might influence the reliability and robustness of the metric.

Tamboer & Voorst (2015) developed an extensive self-report questionnaire-based assessment to screen for dyslexia in students attending Dutch universities. Divided into three sections: biographical questions, general language statements, and specific language statements, which although still retaining a strong literacy-based focus, this assessment tool does include items additional to measures of reading, writing and copying, such as speaking, dictation and listening. In the 'general language statements' section some statements also referred to broader cognitive and study-related skills such as 'I can easily remember faces' or 'I find it difficult to write in an organised manner'. This seems to be making a better attempt at developing processes to gauge a wider range of attributes that are likely to impact on learning and study capabilities in the search for an effective identifier for dyslexia in university students and is consistent with the Tamboer et al construction of dyslexia as a multifactorial condition. This model is consistent with an earlier self-report screening assessment which in its design, acknowledged that students with dyslexia face challenges at university that are in addition to those associated with weaker literacy skills (Mortimore & Crozier, 2006). In contrast to Channock's findings concerning the YAA-R reported above, Tamboer & Voorst's assessment battery correctly identified the 27 known dyslexic students in their research group - that is, students who had documentary evidence as such - although it is unclear how the remaining 40 students in the group of 67 who claimed to be dyslexic were identified at the pre-test stage. Despite this apparent reporting anomaly, this level of accuracy in identification is consistent with their wider review of literature concluding that there is good evidence to support the accuracy of self-report identifiers (ibid).

measuring dyslexia

Thus, in none of the more recently developed screening tools is there mention of a criterion that establishes how dyslexic a dyslexic student is other than either in coarsely-defined gradations such as 'mild', 'moderate', 'severe', or otherwise by presenting the raw score outcomes for each of a wide range of tests and assessments which are not cohesively bound into an easily-comprehensible value. Elliott & Grigorenko (2014) summarize this issue with a claim that a key problem in the development of screening tools for dyslexia is in setting a separation boundary between non-dyslexic and dyslexic individuals that is reliable and which cuts across the range of characteristics or attributes that are common in all learners in addition to literacy-based ones and especially for adults in higher education. Hence for this project it was felt that none of the existing evaluators would be able to not only accurately identify a dyslexic student from within a normative group of university learners - that is, students who include none previously identified as dyslexic nor any who are purporting to be dyslexic - but also be able to ascribe a measure of the dyslexia to the identification in a more finely graded way - that is, to establish a level of dyslexia-ness. Thus the development of a bespoke tool for gauging dyslexia-ness was considered necessary for this project the design of the which needed to ensure that all students who used it felt that they are within its scope and that it would not reveal a set of study attributes that were necessarily deficit- nor disability-focused. Such tool needs to satisfy the following criteria:

  • dyslexia indexit is a self-report tool requiring no administrative supervision;
  • it is not entirely focused on literacy-related evaluators, and attempts to cover the range of wider academic issues that arise through studying at university;
  • it includes some elements of learning biography;
  • its self-report stem items are equally applicable to dyslexic as to apparently non-dyslexic students;
  • it is relatively short as it would be part of a much larger self-report questionnaire collecting data about the 7 other metrics that are being explored in this research project;
  • it draws on previous self-report dyslexia identifiers which could be adapted to suit the current purpose to add some prior, research-based validity to the metric;
  • the results obtained from it will enable students to be identified who appear to be presenting dyslexia-like attributes but who have no previous identification of dyslexia;

This metric will gauge a range of dimensions across a student's learning profile and will attempt to collectively quantify learning, study, and learning-biography attributes and characteristics which are known to exhibit differences between dyslexic and non-dyslexic individuals into a comparative measure which can be used as a discriminator between students presenting a dyslexic, a quasi-dyslexic and a non-dyslexic profile out of two samples of university students, one group who have declared that they are dyslexic, these will be the Control, and another who have declared no dyslexic learning differences. The measure is a coefficient and hence adopts no units. The tool that has been developed to generate the index value will be referred to as the Dyslexia Index Profiler, and Dyslexia Index will be frequently abbreviated to Dx. The selective literature review so far will have demonstrated unease with the use of the term 'dyslexia' as a descriptor of a wide range of learning and study attributes and characteristics that can be observed and objectively assessed in all learners in university settings, however, in the interests of expediency, the term will be used throughout this study.

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Dyslexia - a summary

This first sub-section of the literature review has attempted to present a thoughtful overview of the syndrome of dyslexia at a sufficient level of detail and to demonstrable understanding to partially provide the theoretical underpinnings of this research project. It commenced by setting out the chosen definition for dyslexia that was considered as best matched the stance of this project and continued by briefly reviewing a selection of the most important theories about what dyslexia is and what its causes may be. It has been acknowledged that dyslexia is fundamentally about the communication skills and competencies of literacy, that is, reading, writing and spelling, especially in early-years learners. But an attempt has been made to demonstrate that a wide diversity of additional characteristics or dimensions can also be associated with the situation and circumstances of dyslexia. It has been shown that in university-level learners, it often these other dimensions which may have a more significant impact on how students engage with their studies at university because earlier literacy difficulties have often been strategically managed or accommodated into a learning profile and identity so as to have a reduced impact on learning that remains literacy-based. A polemic which runs through this discussion takes the position that were education and learning to have a more diverse range and scope in its forms of curriculum delivery and assessment processes and be less rigidly attached to literacy as a skill to be mastered so as to enable a learner to accurately demonstrate their knowledge or express their ideas then individuals with dyslexic learning differences would be at less of a disadvantage in comparison with their peers. A shift towards the wider adoption of the ethos and principles of Universal Design for Learning has been strongly advocated, especially in higher education contexts where firstly there is the scope for pedagogical processes to be more flexible and adaptable given sufficient impetus, and secondly, procedures for assessment could be more thoughtfully and less rigidly designed because they are less bound to nationally-devised outcome performance standards than may be the case at lower levels of teaching and learning. By revising university teaching and learning in this way, students who present learning differences, whether dyslexic or otherwise, or alternative learning preferences or strengths that fall nearer the periphery of those considered as more typical, might be empowered to more effectively demonstrate their academic capabilities.

2. Academic Confidence




Confidence is a robust dimensional characteristic of individual differences (Stankov, 2012). Confidence can be considered as a sub-construct of self-efficacy where self-efficacy is concerned with an individual's context specific beliefs about the capability to get something done (Bandura, 1995). Students who enter higher education or college with confidence in their academic abilities to perform well, do perform significantly better than their less-confident peers (Chemers et al, 2001) and are likely to enjoy their studies more readily (Putwain et al, 2013). If individuals believe that they have no power to produce results then they will not attempt to make them happen (Bandura, 1997) and specifically, when students lack confidence in their capacity to tackle academic tasks they are less likely to engage positively with them (Pajares & Schunk, 2002). Academic confidence can be thought of as a mediating variable - that is, it acts bi-directionally - between individuals' inherent abilities, their learning styles and opportunities presented in the environment of higher education (Sander & Sanders, 2003) and particularly when academic confidence is fostered as part of learning community initiatives, it can be an important contributor to academic success (Allen & Bir, 2012).

Thus, confidence can be regarded as students’ beliefs that attaining a successful outcome to a task is likely to be the positive reward for an investment of worthwhile effort (Moller et al, 2005). Conversely, in those for whom confidence in their academic abilities is weak, these learners can interpret the accompanying anxiety related to academic performance as a marker of their incompetence, although this may be an incorrect attribution and which in turn, may lead to exactly the fear of failure that has generated the anxiety (Usher & Pajares, 2008). Perceptions of capability and motivation, which include judgements of confidence, feature significantly in self-concept theories, in particular, Social Cognitive Theory. This is where beliefs in personal efficacy are thought to be better predictors of academic outcomes than actual abilities or evidence from prior performance, because these beliefs are fundamental in establishing how learners are likely to tackle the acquisition of new knowledge and academic skills and how they will apply these productively, leading to positive and worthwhile outcomes (Pajares & Miller, 1995).

Social Cognitive Theory (SCT) enshrines these ideas and has been developed through decades of research and writing, particularly by Bandura (commencing: 1977). The underlying principle in SCT is to attempt to provide explanations for the processes that drive and regulate human behaviour according to a model of emergent interactive agency (Bandura, 1986). This is a model which attributes the causes of human behaviour to multifactorial influences derived principally from the reciprocal interactions between inherent personal characteristics, the local and wider environment that surrounds the domain of behavioural functioning, and the behaviour itself. As such, considerable interest in SCT has been expressed by educationalists and education researchers seeking to apply and integrate the ideas enshrined in the theory into a clearer understanding of the functions of teaching and learning processes, especially for making these more effective mechanisms for the communicating of knowledge and the expression of ideas, and for interpreting the roots and causes of both academic failure and success.

freud skinner kellyWithin this over-arching theory, the position of self-efficacy (and thus by inference, academic self-efficacy) as a social psychological construct that relates self-belief to individual actions, is a central and fundamental element. Self-belief is a component of personal identity and we might trace some of the roots of Bandura’s theories to earlier work on personal construct theory asserting that an individual’s behaviour is a function of not only the ways in which they perceive the world around them, but more particularly how they construct their world-view in such a way that enables them to navigate a path through it (Kelly, 1955). Along this route from Kelly to Bandura can be found the important, Rogersian ‘person-centred approach’ which takes as its focus the concept of the ‘actualizing tendency’ by which is meant the basic human processes that enable the accomplishment of our potential by developing our capacities to achieve outcomes (Rogers, 1959). We can see the embodiment of this in higher education contexts through institutions seeking to adopt a ‘student-centred’ learning environment where the aim is to shift the focus from a didactic curriculum presentation to systems of knowledge delivery and enquiry which is more co-operative and student self-managed, with varying degrees of success (O’Neill & McMahon, 2005).

These underpinning arguments and theses relating to human functioning have influenced the development of SCT by illuminating the mechanisms and processes that control and regulate the ways in which we behave and operate from a very different perspective to earlier arguments. Typically, those were based on either the psycho-analytic framework of Freud, or the strongly stimulus-response behaviourist principles proposed by Watson (1913), which attracted considerable interest from later psychologists eager to apply these to the learning process. Perhaps the most notable of these had been Skinner (eg: 1950) which externalized behaviour to the exclusion of cognitive processes.

Space and scope does not permit a full documentation of the historical development of all these competing theories in the narrative that follows. So the focus will be on exploring Social Cognitive Theory as a highly influential late-twentieth century proposition that took a fairly radical new approach in its suggestions about how human behaviour is controlled and regulated by how we think, what influences these thought processes, and how these are transformed into consequential behavioural actions. As a bridge to the construct of academic self-efficacy and the sub-construct of academic confidence, this sub-section will continue with a brief review of the work Zimmerman, Schunk and Pajares whose research has been instrumental in relating SCT into educational contexts, concluding with a review of academic confidence, but especially academic behavioural confidence through the research and development work of Sander and others.


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Key Research Perspectives



Bandura - Social Cognitive Theory (SCT), and the self-efficacy component of SCT in learning contexts

In social cognitive theory (SCT), learning is taken to be the process of knowledge acquisition through absorbing and thinking about information (Stajkovic & Luthans, 1998). The influence of Bandura’s original (1977) and subsequent work in developing social cognitive theory has had a major influence on education researchers. This is because many of the components in SCT have been shown to significantly impact on understanding learning processes more clearly by adopting a more social construction of learning – that is, learning behaviour is considered, explored and theorized within the context of the environment where the learning takes place (Bredo, 1997). This is in contrast to behaviourist or experiential constructions, both of which have been popular at times and should be duly credited for their contribution to the continuing evolution of theories about the psychology of education and learning. Indeed the most recent ‘construction’ to explain learning claims greater pertinency in the so-called ‘digital age’ by arguing that all previous theories about learning are becoming outdated because they are all antecedent to the technological revolution that is now pervasive in most, more modern places of learning.

connectivismbwBriefly, this latest thesis is known as connectivism, (Siemens, 2005) and the idea is that the personal learning spaces of individuals now extend beyond conventional learning environments and places of study because informal learning is becoming a more significant construction in educative processes (ibid, p1) - that is for example, through communities of practice, social (learning) networks, open access to data and information repositories, work-based and experience-creditable learning and indeed, MOOCs. Significantly, connectivism is seen by some to be particularly influential in reshaping higher education for the future consumers of its products (Marais, 2011), into what is now being considered as a sociotechnical context of learning (Bell, 2011). However as with all emerging theories, critics argue that this new theory is unlikely to be the theory that explains how learning absorbs, transforms and creates knowledge, even in the new learning environments of e-learning (Goldie, 2016), because fresh ideas take time to be consolidated through critical evaluation and observation of practice, principally through research. Whether or not this emerging learning theory is recognized as such, its principles may be highly attractive to learning providers where, in an uncertain financial climate, many of the costs associated with curriculum delivery are claimed to be significantly reduced (Moonen, 2001) albeit as a result of initial investment in developing and installing new technology systems. It might be considered that ideas behind both Connectivist Theory and Universal Design for Learning are now on converging courses as both more closely service the flexible learning needs of the increasingly diverse learning communities that are emerging in our universities (Rao et al, 2015).


An overview of social cognitive theory

The core of social cognitive theory is about explaining human behaviour in the context of systems of self-regulation. Bandura argues that these systems are the major influences that cause our actions and behaviours. Emanating from his earliest writings, the principal idea is enshrined by a model of triadic reciprocal causation where the three interacting factors of personal influences, the environment, and action-feedback-reaction mechanisms that are integrated into all human behaviours act reciprocally and interactively as a structure that constitutes what is human agency – that is, the capacity for individuals to act independently and achieve outcomes through purposive behavioural actions. In this theory, individuals are neither entirely autonomous agents of their own behaviour nor are they solely actors in reactive actions that are driven by environmental influences (Bandura, 1989). More so, it is the interactions between the three factors that are thought to make a significant causal contribution to individuals’ motivations and actions. The graphic below illustrates the interrelationships between the three factors in the triadic reciprocal causation model and suggests many of the sub-components of each the factors:

triadic reciprocal causation
[ adapted variously from Bandura, 1977, 1982, 1989, 1991, 1997 ]

These are tied up with forethought based on past experiences and other influences - many being external - that precedes purposive action. This is to say that within the context of belief-systems, goal-setting and motivation, we all plan courses of action through tasks and activities that are designed to result in outcomes. None of our actions nor behaviours are random, despite evidence in earlier theories to the contrary which appeared to have demonstrated that such random behaviours are externally modifiable through stimuli of one form or another (eg: Skinner, 1953) or as more casually observed through the apparently variable and unpredictable nature of human behaviour. By thinking about future events in the present, motivators, incentives and regulators of behaviour are developed and applied. Bandura constructs his theory of the self-regulative processes around three core concepts: that of self-observationjudgmental processes, and self-reaction. Although a linearity is implied, these concepts are more likely to operate in a cyclical, feedback loop so that future behaviour draws on lessons learned from experiences gained in the past, both directly and through more circuitous processes.

reflective cyclesKey to self-observation is the self-reflective process: In order to influence our own motivations and actions we need to reflect on past performances. This is especially important in learning contexts and has been established as an important guiding principle in the blend of formal and independent learning processes that constitute the curriculum delivery at university in particular, where ‘reflective cycles’ are prevalent in numerous academic disciplines. This is especially so in ones that involve an element of practice development such as nursing and teaching (eg: Wilson, 1996, Pelliccione & Raison, 2009). But the self-diagnostic function can be very important per se, not least because for those who are able and motivated to respond to the information acquired by reflective self-monitoring, behavioural change and/or modification of the respective environment, the potential for improving learning quality can be a valuable outcome (Lew & Schmidt, 2011, Joseph, 2009). At university, this translates into students becoming more capable at making immediate and adaptive changes to their learning and study strategies to displace sometimes deeply entrenched surface- or ‘non-learning’ inertia and hence, change outcomes (Kinchin, 2008, Hay, 2007). Although this may lead to elements of academic dishonesty (Hei Wan et al, 2003), it is more likely that proactive learning innovations will bring higher academic rewards.

Being self-judgmental can be challenging however, especially when doing so has a bearing on perceptions of personal competence and self-esteem because affective reactions (that is, ones that are characterized by emotions) that may be activated can distort self-perceptions both at the time and also during later recollections of a behaviour (Bandura, 1993). But this does not alter the fact that observing one’s own pattern of behaviour is the first of a series of actions that can work towards changing it (ibid). First and foremost is making judgments about one’s own performance relative to standards. These can range from external assessment criteria to those collectively set by social and peer-group influences (Ryan, 2000) where the objective is to establish one’s personal standards with reference to the standards of the comparison group. Even within the framework of absolute standards that are set externally, social comparison has still been shown to be a major factor that individuals refer to for judging their own performance, although these judgements can vary depending on which social comparison network is chosen (Bandura & Jourden, 1991). This seems likely to be highly significant in education contexts and might be taken to indicate that teacher-tutor efforts at raising the achievement standards of individual students should also be applied to the student’s immediate learning-peer-group, the outcome of which would be shared improvement throughout the group which should carry with it the desired improvement of the individual.

But another significant factor that influences self-judgmental processes is the value that individuals attach to the activity that they are engaged in. Bandura (1991) tells us that, not unsurprisingly, individuals are less likely to engage positively with activities that they consider not important to them than with those that are viewed as valuable – for whatever reason – or which may have a significant impact on their futures. This is often challenging in compulsory education where adolescents in particular, tend to be very critical of the value of much of the curriculum learning that they are compelled to engage with (Thuen & Bru, 2000, Fabry, 2010). Not least this is because nationally-imposed curricula remain focused on conveying content to the detriment of developing thoughtful learners (Wiggins & McTighe, 2008), although some evidence shows that teachers who choose to adopt a more dialectic, rather than didactic approach to engaging with teenagers tend to be more successful in overcoming these teaching challenges (Cullingworth, 2014). A legacy of this reluctance to positively participate in learning structures, especially ones that adopt a conventional approach to the delivery of the curriculum, has been found to extend into tertiary level learning (Redfield, 2012) despite the greater degree of individualized self-learning management that exists in university learning structures where it would be expected that students who have chosen to study in a particular discipline are positively inclined to engage with it.

Performance judgements pave the way towards the last of Bandura’s three core components, that of self-reaction which is the process by which standards regulate courses of action. This is about the way in which personal standards are integrated into incentivisation or self-censure which is mostly driven by motivation levels based on accomplishment and the affective reactions to the degree to which success (or not) measures up to our internalized standards and expectations. In many domains of functioning there is abundant research to endorse the well-used cliché, ‘success breeds success’ with plenty of this in learning contexts. For example: supporting evidence has been found in university-industry learning-experience initiatives (Santoro, 2000), in mathematics teaching and learning (Smith, 2000), or in knowledge management and more business-oriented settings (Jennex, et al, 2009, Roth et al, 1994) with all of these studies reporting in one form or another, the positive impact of early- or first-initiative success on later-action success. Zimmerman (1989) reports that one of the most significant factors that differentiates between those who are successful in responding to their self-regulatory efforts and those who are not, is the effective utilization of self-incentives. We might imagine that this may be no-better illustrated than in the writing habits of PhD students who must depend on their own writing self-discipline because there is a much-reduced supervisory element at this level of study in comparison to lower degrees. Hence developing writing incentives as part of the study-research process becomes instrumental to a successful outcome, with the most accomplished doctoral students likely to have developed the expected high-level study strategies early on. Indeed, there is now evidence to report that the process of ‘blogging’ as a means to provide writing incentives to university students is reaping positive benefits not least as online, personal study journals are likely to encourage extra-individual participation and self-reflection, and subsequently increase writing fluency (Zhang, 2009).


Self-efficacy in social cognitive theory and in learning

Based on much of his earlier work developing Social Cognitive Theory, Bandura turned his attention to the application of SCT to learning. The seminal work on self-efficacy (Bandura, 1997) has underpinned a substantial body of subsequent research in the areas of behavioural psychology and social learning theory, especially in relation to the roles that self-efficacy plays in shaping our thoughts and actions in learning environments. Self-efficacy is all about the beliefs we have and the judgements we make about our personal capabilities and these are the core factors of human agency, where the power to originate actions for given purposes is the key feature (ibid, p3). Our self-efficacy beliefs contribute to the ways in which self-regulatory mechanisms control and influence our plans and actions, and hence, the outcomes that are the results of them. Bandura’s arguments and theses about how self-efficacy impacts on effort, motivation, goal-setting, task value, task interest and task enjoyment can be usefully distilled into 9 key points, additionally supported through the work of other researchers as cited. All of these points are highly pertinent in the domain of learning and teaching:

  1. Individuals with a strong self-efficacy belief will generally attribute task failures to a lack of effort whereas those with much lower levels of self-efficacy ascribe their lack of success to a lack of ability (Collins, 1982);
  2. Changes in self-efficacy beliefs have a mediating effect on the ways in which individuals offer explanations related to their motivation and performance attainments (Schunk & Gunn, 1986);
  3. Self-efficacy beliefs also mediate the ways in which social comparisons impact on performance attainments (Bandura & Jourden, 1991);
  4. Those who judge themselves to be more capable tend to set themselves higher goals and demonstrate greater commitment to remain focused on them (Locke & Latham, 1990);
  5. Self-doubters are easily deterred from persisting towards goals by difficulties, challenges and failures (Bandura, 1991);
  6. Conversely (to 5), self-assurance breeds an intensification of effort in the face of adversity or failure and brings with this, greater persistence towards success (Bandura & Cervone, 1986);
  7. Self-efficacy makes a strong contribution towards the ways in which individuals ascribe value to the things they attempt (Bandura, 1991);
  8. Individuals who present high levels of self-efficacy beliefs are more prone to remain interested in tasks or activities, especially ones from which they gain satisfaction by completing them and which enable them to master challenges (Bandura & Schunk, 1981);
  9. Deep immersion in, and enjoyment of pursuits and challenges tend to be best maintained when these tasks are aligned with one’s capability beliefs, especially when success contributes towards aspirations (Csikszentmihalyi, 1979, Malone, 1981);

Thus, self-efficacy is broadly about judging one’s capabilities to get something done and is integrated into many of the self-regulatory mechanisms that enable and facilitate the processes we need to engage in to accomplish things. That is, it is a construct that has functional characteristics and is a conduit for competencies and skills that enable positive outcomes. A function is a determinable mapping from one variable to a related dependent one, hence it is reasonable to suppose that outcome is a dependent function of self-efficacy, and that (academic) self-efficacy belief can be a dependent function of aptitude (Schunk, 1989). A typical, science student might comment:

  • Once I’ve got started on this essay about the role of mitochondria in cell energy factories I’m confident that I can make a pretty good job of it and finish it in time for the deadline”

This student is expressing a strong measure of self-efficacy belief in relation to this essay-writing task. Notice that self-efficacy is domain (context) specific (eg: Wilson et al, 2007, Jungert et al, 2014, Uitto, 2014). For our science student, the challenges of the task have been considered and the evaluation integrated with perceived capabilities – in this case, capabilities about writing an academic essay based on scientific knowledge. Whereas outcome can be more obviously considered as a function of self-efficacy, conversely, self-efficacy belief may also be a function of outcome expectations because the essay writing task has not yet commenced or at least certainly is not completed. The student is projecting a belief about how successful the outcome will be for some point in the future and so it is reasonable to suppose that this may have an impact on the ways in which the task is approached and accomplished. This is an important point, however the bidirectionality of the functional relationship between self-efficacy beliefs and outcome expectations is not altogether clear in Bandura’s writings. In an early paper it is argued that Social Cognitive Theory offers a distinction between efficacy expectations and outcome expectancy:

  • “An efficacy expectation is a judgement of one’s ability to execute a certain behaviour pattern, whereas an outcome expectation is a judgement of the likely consequences such behaviour will produce” (Bandura, 1978, p240).

By including the phrase ‘likely consequences‘ Bandura’s statement seems to be indicating that a self-efficacy belief precedes an outcome expectation and although these concepts seem quite similar they are not synonymous. For example, a student who presents a strong belief in her capacity to learn a foreign language (which is self-efficacy) may nevertheless doubt her ability to succeed (an outcome expectation) because it may be that her language class is frequently upset by disruptive peers (Schunk & Pajares, 2001) and this conforms to the correct sequential process implied in the statement above. The key idea according to Bandura and others such as Schunk and Pajares – who broadly take a similar standpoint to Bandura although acknowledge that the relationships between self-efficacy beliefs and outcome expectancy is far from straightforward – is that beliefs about the potential outcomes of a behaviour only become significant after the individual has formed a belief about their capability to execute the behaviour likely to be required to generate the outcomes (Shell et al, 1989) and that this is suggested to be a unidirectional process – that is, it can not occur the other way around. This is important because it implies that self-efficacy beliefs causally influence outcome expectancy rather than proposes a bidirectional, perhaps more associative relationship between the constructs, or that there are circumstances when they may be mutually influential. Bandura provides a useful practical analogy to argue the point that self-efficacy beliefs more generally precede outcome expectations:

  • "People do not judge that they will drown if they jump into deep water and then infer that they must be poor swimmers. Rather, people who judge themselves to be poor swimmers will visualize themselves drowning if they jump into deep water" (1997, p21).

which is also demonstrated in a simple schematic presenting the conditional relationships between self-efficacy beliefs and outcome expectancies as Bandura sees it (adapted from 1997, p22)

individual behaviour ourcome

However, a wider review of literature shows that the evidence is conflicting, to start with because definitions of construct parameters are not universally agreed. In trying to establish exactly what is meant by an individual’s self-efficacy beliefs, understanding is clouded because the key parameter of ‘capability’, widely used in research definitions, must be relative to the domain of interest but is also necessarily subjective, based on the individual’s perception of their capability in that context. Thus, even in an experiment with a clearly defined outcome that seeks to find out more about participants’ context-based self-efficacy beliefs and their task outcome expectancy, the variability between participating individuals’ perceptions of their capabilities, even in the same context, would be very difficult to control or objectively measure because these are ungradable, personal attributes formed through the incorporation of a diversity of individualized factors ranging from social, peer-group and family influences (Juang & Silvereisen, 2002) to academic feedback reinforcement which can be both positive and negative (Wilson & Lizzio, 2008).

free climber source http://img.wennermedia.com/article-leads-horizontal/mj-618_348_free-climbing-the-coast-of-oman.jpgOf the numerous studies found so far, ‘capability’ is almost universally used in an undefined way with the assumption made that its non-absolute variability is accommodated into the research methodology of the study on the basis of a tacit understanding about what it means. For example Bong, who has contributed substantially to the debate about the position of self-efficacy beliefs in learning situations, conducted several studies exploring academic self-efficacy judgements of adolescent and college learners. The general objectives were to reveal more about the context-specific versus generalized nature of the construct, or how personal factors such as gender or ethnicity affect self-efficacy judgements (Bong, 1997a, 1997b, 1998a, 1998b, 1998c, 2001, 2002), all of which relied on Bandura’s model as the theory to underpin the research. In keeping with Bandura’s definitions of self-efficacy (previously cited) ‘capability’ was used throughout these studies, with perceived capability being specifically measured by gauging research participants’ judgments of their assuredness about solving academic tasks. But nowhere was to be found a meaningful definition of ‘capability’ with studies relying on readers’ understanding of ‘capability’, presumably contextualized into the nature of the research. Indeed one particularly interesting study provided some participants with a short contextual overview to aid their perception of ‘capability’ whereas others were not and the research outcome subsequently showed that self-efficacy ratings were highly influenced by the way in which the notion of ‘capability’ was presented, or not (Cahill et al, 2006). This appears to be a typical feature in the literature and is painting ‘capability’ as a kind of threshold concept (Meyer & Rand, 2003, Irvine & Carmichael, 2012, Walker, 2012) much like ‘irony’, where pinning down a meaning is elusive and rather, depends on the acquisition of a sense of the term through multiple, contextualized examples.

Thus the literature shows that some who are keen to exploit Bandura’s Social Cognitive Theory to support the design and methodologies of their studies may not have paid sufficient attention to this problem of operational definitioning by taking the theory ‘as read’ and without the adoption of a more objective standpoint or stating clearly their perspective. For example, Riggs et al (1994) applied the self-efficacy and outcome expectancy dimensions of SCT to find out more about attitudes to work in an occupational setting. Their study is a pertinent example of one that appears to be grounded in weak conceptual foundations, firstly because a reluctance to properly gain a grasp of the background understanding is perhaps evidenced through the evaluation scales that were developed were said to rely on ‘scrutin[y] by two “expert judges” with Ph.D degrees who had a knowledge of both measurement theory and Bandura’s theories‘ (ibid, p795); and secondly because the main focus of the study was to develop such evaluation scales based on the premise that self-efficacy and outcome expectancy are discrete constructs – which they cited as a central tenet of Bandura’s theory but without a discussion about Bandura’s key claim that self-efficacy beliefs unidirectionally influence outcome expectancy.

nullius in verbaMore disconcerting, is the evidence from several studies which appear to expose a deeper flaw in Bandura’s key argument, concisely summarized by Williams (2010), who seemed equally unsettled by the blind adoption of theory as fact rather than being guided by the spirit of scientific research based on nullius in verba. In his paper (ibid), a case was built through the examination and citation of several examples of research which countered Bandura’s ‘fact’ that self-efficacy beliefs causally influence outcome expectancies in that direction only. Williams summarizes an argument about the causality of self-efficacy beliefs on behaviour that has remained unresolved for three decades, particularly through use of extensive research by Kirsch amongst notable others, which explored the impacts that incentivizing outcome expectancy has on perceptions of capability, that is, self-efficacy beliefs. Williams re-ignited the debate on whether or not self-efficacy beliefs can be attributed as a cause for behaviour without being influenced by expectations of possible outcomes that will result from the behaviour, or even that the complete process can just as likely occur the other way around.

snake charmerKirsch’s (1982) bizarre studies involved enticing participants to approach a (harmless) snake in comparison to them engaging in a mundane and trivial skills exercise. The study clearly demonstrated that by using financial incentives, participants raised their levels of self-efficacy beliefs for both activities but more so for approaching the snake. This indicated that outcome expectancies can influence self-efficacy beliefs. Of particular interest in that research were the conclusions that efficacy ratings may take different values depending on whether they are in relation to non-aversive skills tasks or to tasks related to feared stimulus (ibid, p136). The key point is that for trivial or skills-based tasks, belief in an ability to accomplish them appears fairly fixed and not likely to be altered through incentivizing the tasks – individuals simply stick to the belief about what they are capable of – whereas tasks that are not reliant on a specific skill and particularly those which hold aversion characteristics, i.e. approaching a snake, individuals exhibit efficacy beliefs which can be modified through the offer of incentives because they are tasks that invoke (or not) willingness rather than ability. This is the significant point because ‘willingness’ is driven by an outcome expectancy whereas ability is driven by a self-efficacy belief. Hence Kirsh has shown that the causality linkage between self-efficacy beliefs and outcome expectancy is bidirectional in some circumstances. Similar findings were reported in other research domains, notably in relation to smoking cessation (Corcoran & Rutledge, 1989) and also where actual monetary gains were offered to induce college students to endure longer exposure to pain which, through the randomized nature of the actual rewards, showed that the impact of expected financial gain influenced self-efficacy (Baker & Kirsch, 1991). Indeed, Bandura’s interest in how efficacy beliefs are of a different flavour when associated with aversive or phobic behaviours is evidenced in studies in which his input is apparent, notably in domains which explore the impact on efficacy beliefs of therapeutic treatments proposed for the amelioration of such behaviours (eg: Bandura et al, 1982).

student debtHence, it seems reasonable to suppose that similar relationships may occur in other domains. To put this into a more recent context in university learning, we might reflect on the increasing prevalence of incentivizations that institutions are widely adopting to encourage attendance in the light of aversion to debt resulting from fees increases across the sector in the UK in the last decade. It is of note that the very socio-economic groups targeted by governments as desirable to encourage into university learning through widening participation initiatives, tend to be the most debt-averse and the least likely to have this aversion mediated through financial incentivization (Pennel & West, 2005, Bowers-Brown, 2006) – hence this may be one explanation for the continuing (albeit small) decline in student numbers in UK universities, especially for undergraduates and which is independent to demographic variations in cohort (UCAS, 2017). Indeed, Bandura tells us that ‘people who doubt they can cope effectively with potentially aversive situations approach them anxiously and conjure up possible injurious consequences‘ (1983, p464). For contemporary students, this may be the lasting legacy of substantial student debt and the consequences they perceive this may have on their later lives. Conversely, for those who anticipate an abilty to exercise control over their later financial circumstances and consider the benefits of higher education to outweigh the negative consequences of later debt, aversion towards high student fees and loans are mediated.

We are therefore left with two uncertainties when seeking to use the principles of self-efficacy beliefs to explain individuals’ behaviour: the first is that operational definitions of attributes and characteristics of self-efficacy are difficult to firmly establish, particularly the notion of ‘capability’; and secondly that Bandura’s underlying theory appears not quite as concrete as many researchers may have assumed. This is despite Bandura’s numerous papers persistently refuting challenges (eg: Bandura, 1983, 1984, 1995, 2007). So it seems clear that care must be exercised in using the theory as the backbone of a study if the outcomes of the research are to be meaningfully interpreted in relation to their theoretical basis. In particular, there seems some inconsistency about the operational validity of the self-efficacy<->outcome expectancy relationship in some circumstances, notably ones that may involve attributing the functional relationships between the two constructs into phobic behaviour situations where self-efficacy measures of (cap)ability are obfuscated by the related but distinct construct of willingness (Cahill et al, 2006). Given elements of phobic behaviour observed and researched in the domain of education and learning (eg: school phobias; for some useful summaries see: Goldstein et al, 2003, King et al, 2001,  Kearney et al, 2004), consideration of this facet of self-efficacy belief theory to learning contexts should not be neglected.

In summary, it is useful to compare the schematic above (taken from Bandura, 1997, p22) which illustrates the unidirectional relationship from self-efficacy to outcome expectancies with the the schematic here, modified into our context based on a prior adaption (Williams, 2010, p420) of Bandura’s writings in the same volume (op cit, p43) which apparently suggests that a reversed causality direction can occur.

outcome expectancies to self efficacy



Dimensions of self-efficacy - level/magnitude, strength, generality


Efficacy beliefs in the functional relationship that link self-efficacy through behaviour to outcome expectations (and sometimes reciprocally as we have discussed above) have been shown through a wide body of literature supporting Bandura’s central tenets to be componential and we can think of the level or magnitude of self-efficacy expectations and the strength of self-efficacy expectations as the two primary dimensions. (Stajkovic, 1998). Magnitude is about task difficulty and strength is the judgment about the magnitude: a strong self-efficacy expectation will present perseverance in the face of adversity whilst the converse, weak expectation is one that is easily questioned and especially doubted in the face of challenges that are thought of as difficult, (a sense established above in points 5 and 6). Bandura referred to magnitude and level synonymously and either term is widely found in the literature.

  • MAGNITUDE: ‘whether you believe that you are capable or not …’
  • STRENGTH: ‘how certain (confident) you are …’

The essay-writing example used earlier demonstrates an instance of the capacity to self-influence, and in learning challenges the ways in which an individual reacts to the challenges of an academic task is suggested to be a function of the self-efficacy beliefs that regulate motivation. It also provides an example of academic goal-setting – in this case, meeting the deadline – to which motivation, as another significant self-regulator mediated by self-efficacy, is a strong impacting factor, and to which significant associations between academic goal-setting and academic performance have been demonstrated (Travers et al, 2013, Morisano & Locke, 2012). However, expanding on this is for a later discussion although the graphic below serves to illustrate how the dimensions of magnitude and strength might be working in relation to the example-task of writing an academic essay. Each quadrant provides a suggestion about how a student might be thinking when approaching this essay-writing task and are related in terms of their levels of perceived capability (magnitude) and confidence (strength) as dimensions of their academic self-efficacy beliefs.

In his original paper (1977) Bandura set out the scope and self-efficacy dimensions of magnitude and strength, and also the third dimension, ‘generality’  which  relates to how self-efficacy beliefs are contextually specific or more widely attributable. The paragraph in this paper which provides a broad overview is presented verbatim (below) because it is considered useful to observe how confounding this earliest exposition is, and hence to reflect on how Bandura’s original thesis may have confused subsequent researchers due to the interchangeability of terms, words and phrases that later had to be unpicked and more precisely pinned down:

‘Efficacy expectations vary on several dimensions that have important performance implications. They differ in magnitude. Thus when tasks are ordered in level of difficulty, the efficacy expectations of different individuals may be limited to the simpler tasks, extend to moderately difficult ones, or include even the most taxing performances. Efficacy expectations also differ in generality. Some experiences create circumscribed mastery expectations. Others instill a more generalized sense of efficacy that extends well beyond the specific treatment situation. In addition, expectancies vary in strength. Weak expectations are easily extinguishable by disconfirming experiences, whereas individuals who possess strong expectations of mastery will persevere in their coping efforts despite disconfirming experiences.’

Bandura, 1977, p194



Task / domain specificity

essay writingTo follow through from our student facing a challenging essay-writing task it should be noted that self-efficacy is not necessarily a global construct and tends to be task-specific (Stakjovic, 1998). Our student may think herself perfectly capable in essay-writing, but consider that arguing the key points to peers through a group presentation quite beyond her. Taking another example outside the environment of learning and teaching: In the domain of entrepreneurship and risk-taking, the sub-construct of entrepreneurial self-efficacy (ESE) was proposed as part of the research hypothesis in a study to explore decision-making in relation to the opportunities or threats presented in test dilemmas. Results supported the idea of entrepreneurial self-efficacy as a relevant, task-specific construct by indicating that decision-making based on higher levels of ESE were more opportunistic and had a lower regard for outcome threat (Kreuger & Dickson, 1994). A later study, also using ESE, generated research results which, it was claimed, established entrepreneurial self-efficacy as a distinct characteristic of the entrepreneur in relation to individuals operating in other business or management sub-domains and that it could be conversely used to predict the likelihood of an individual being strong in the specific traits observed as part of the profile of successful entrepreneurs (Chen et al, 1998). In moving closer towards an educational domain, at least in terms of the research datapool, Rooney & Osipow (1992) further tested a ‘Task-Specific Occupational Self-Efficacy Scale’ (TSOSS), previously developed in an earlier study, using a sample of psychology and journalism undergraduates (n=201) to explore its applicability to career development and career decision-making. Underpinned by prior research which measured occupational or career self-efficacy, the outcomes of their study supported the task-specificity of self-efficacy although admitted the emergence of measurable differences between what they termed ‘general’ occupational self-efficacy and task-specific sub-components derived through their TSOSS. This was apparent through results from a datagroup which presented high self-efficacy for a particular general occupation but presented low self-efficacy in relation to some of the associated sub-tasks of that occupation – for example, some males in their sample believed that they could perform the occupation of social worker but not complete the sub-tasks associated with the domain of social work very effectively. Although these examples seem confounding, one aspect that emerges is that there appears to be a need to distinguish between a self-efficacy measure that is adopted to gauge self-efficacy beliefs in a general domain to those related to specific tasks within that domain. Hence our essay-writing student may present low self-efficacy beliefs related to the specific task of writing about the behaviour of mitochondria in cell energy factories, but be more effficacious when caused to reflect about studying more generally on her biological sciences course.

Thus even though the wealth of research evidence supports the domain specificity of self-efficacy and indeed within that, elements of task-specificity, an element of generality is apparent and it is worth mentioning as a closing remark to this section that some researchers have persisted in attempting to take a more generalist viewpoint on self-efficacy. Schwarzer & Jerusalem (1995) developed a General Self-Efficacy Scale which attracted further development and spawned validation studies by the originators and others throughout the following two decades (eg: Bosscher & Smit, 1998, Chen et al, 2001,  Schwarzer & Jerusalem, 2010). An example of how it has been used is demonstrated by an extensive, cross-domain and cross-cultural investigation which, through a meta-analytic validation study, claimed general self-efficacy to be a universal construct and that it could be used in conjunction with other psychological constructs meaningfully (Luszczynska et al, 2004), and an even more comprehensive meta-analysis using data from over 19,000 participants living in 25 countries which also suggested the globality of the underlying construct ( Scholz et al, 2002). Bandura has consistently doubted the veracity of research results which, he claims, misinterpret self-efficacy as a clear, narrow-in-scope construct and which hence try to justify the existence of a decontextualized global measure of self-efficacy, especially citing the lack of predictive (for behaviour) capability that is weak when using a global measure as opposed to a specifically-constructed, domain-related evaluation, and that this ‘trait’ view of self-efficacy is thin on explanations about how the range of diverse, specific self-efficacies are factor-loaded and integrated into a generalized whole (Bandura, 2012, 2015).



Mediating processes in learning (academic) self-efficacy

An appealing characteristic of self-efficacy theory is that it is strongly influenced by an individual’s cognitive processing of their learning experiences (Goldfried & Robins, 1982) and so in the field of human functioning, but in particular in learning processes, Bandura’s underlying arguments that efficacy beliefs are core regulators of the way we interact and engage with learning opportunities and challenges are weighty and robust. His theories provide evidence that the process by which efficacy beliefs shape our learning is most strongly influenced by four, intervening agencies which he describes as ‘mediating processes‘, and which although may be of individual interest, are processes which operate mutually rather than in isolation (Bandura, 1997). In this context ‘mediating’ means where the action of a variable or variables affect, or have an impact on the processes that connect ourselves with our actions – in this case, our learning behaviour.

Bandura distills these these mediating processes into four components:

  • cognitive processes – where efficacy, that is, the capacity or power to produce a desired effect or action, and personal beliefs in it, are significant in enhancing or undermining performance;
  • motivational processes – where in particular, that through integrating these with attribution theory, the focus of interest is with explaining causality. In this way, theoretical frameworks are constructed which can find reasons that set apart otherwise similarly placed individuals but who take different approaches to (learning) challenges: At one end of the spectrum is the individual who attributes success to their personal skills, expertise and capabilities, and failure principally to a lack of effort. This individual is more likely to accept the challenges of more difficult tasks and persist with them, even in the face of a lack of successful outcomes. Whereas at the other end is the individual who may be convinced that their success or failure is mainly due to circumstances outside their control and hence, generally believes there to be little point in pursuing difficult tasks where they perceive little chance of success - generating a destructive sense of learned helplessness, an attribute which is known to be associated with dyslexia (Glazzard, 2010);
  • affective processes – which are mainly concerned with the impacts of feelings and emotions in regulating (learning) behaviour. Significantly, emotional states such as anxiety, stress and depression have been shown to be strong affectors.
  • selective processes – where the interest is with how personal efficacy beliefs influence the types of ((social) learning) activities individuals choose to engage with and the reasons that underpin these choices.

However the most significant aspect of social cognitive theory when applied to a social construction of learning where academic self-efficacy is suggested to be one of the most important influential factors, are the four, principal sources of efficacy beliefs. Bandura (1997) identified these four source functions as: mastery experience; vicarious experience; verbal persuasion; and physiological and affective states:

Mastery experience is about successes won by building upon positive experiences gained through tackling events or undertakings, whether these be practical or physical, theoretical or cerebral. That is, experience gained through actual performance. But building a sense of efficacy through mastery experience is not about just applying off-the-peg, ‘coached’ behaviours, it appears to rely on acquiring cognitive processing, behavioural and self-regulatory skills that can enable an effective course of action to be executed and self-managed throughout the duration of an activity or life-action. For example, experience gained in essay-writing at university that steadily wins better grades for the student is likely to increase beliefs of academic self-efficacy – in essay-writing at least – whereas failures will lower them, especially if these failures occur during the early stages of study and do not result from a lack of effort or extenuating external circumstances; academic self-efficacy is widely regarded as domain specific in that it must be considered as relational to the criterial task (Pajares, 1996). However, although experience successes and failures are powerful inducers, Bandura reminds us that it is the cognitive processing of feedback and diagnostic information that is the strongest affector of self-efficacy rather than the performances per se (op cit, p81). This is because many other factors affect performance, especially in academic contexts, relying on a plethora of other judgements about capability, not least perceptions of task difficulty or from revisiting an historical catalogue of past successes and failures, and so personal judgements about self-efficacy are incremental and especially, inferential (Schunk, 1991).

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However our essay-writing student will have also formed a judgement of their own capabilities in relation to others in the class. In contrast to the absolutism of an exam mark gained through an assessment process where answers are either correct or not, many academic activities are perceived as a gauge of the attainment of one individual in relation to that of similar others. The influence that this has on the individual is vicarious experience and it is about gaining a sense of capability formed through comparison with others engaged in the same or a similar activity. As such, a vicarious experience is an indirect one, and even though generally regarded as less influential than mastery experiences, the processing of comparative information that is the essential part of vicarious experience may still have a strong influence on efficacy beliefs, especially when learners are uncertain about their own abilities, for whatever reason (Pajares, et al, 2007). A key aspect of vicarious experience is the process of ‘modelling’ by which an individual externalizes the outcome of the comparative processing into actions and behaviour that are aligned with the immediate comparative peer group. Thus for students engaging in learning activities of which they have limited experience, their efficacy beliefs can be influenced by the ways in which they perceive their peers to have achieved outcomes when working on similar tasks (Hutchison et al, 2006). In a sense, this is a kind of quasi-norming process by which an individual uses social comparison inference to view the attainments of ‘similar others’ as a diagnostic of one’s own capabilities. Hence, viewing similar others perform successfully is likely to be a factor in elevating self-efficacy, as equally the converse is likely to depress it. An element of self-persuasion acts to convince the individual that when others are able to successfully complete a task then a similar success will be their reward too. The influence of vicarious experience has been particularly observed in studies concerning the learning behaviours of children where although ‘influential adults’ are of course, powerful models for signalling behaviours, when ability is a constraint, the influences induced by comparison with similar peers can be more significant (Schunk et al, 1987).

An individual’s self-efficacy can also be developed as a consequence of the verbal persuasion of significant others who are relational to them. Verbal persuasion in the form of genuine and realistic encouragement from someone who is considered credible and convincing is likely to have a significant positive impact (Wood & Bandura, 1989). There is plenty of research to support the influence on self-efficacy of verbal persuasion as one of the factors of social cognitive theory with examples coming from a range of disparate fields:  In management and accountancy, a work-integrated learning programme to prepare accountancy under-graduates for employment specifically focused on verbal persuasion as a key, participatory component of the course as a mechanism for enhancing self-efficacy. ‘Significant others’ comprised accounting professionals and industry representatives and the outcomes of the metric used to assess self-efficacy ‘before’ and ‘after’ showed verbal persuasion to have had a significant impact on the increased levels of self-efficacy observed in the participants of the programme (n=35) (Subramaniam & Freudenberg, 2007). In teacher-training, the sense of teaching (self)-efficacy has been found to have a strong influence on teaching behaviour (not unsurprisingly) which is especially significant in student-teachers as they develop their classroom competencies and where encouragement gained from positive feedback and guidance from more experienced colleagues positively impacts on teaching practice confidence (Tschannen-Moran & Woolfolk Hoy, 2002, Oh, 2010). And not least in sport where there are a plethora of studies reporting the positive impact that verbal persuasion has on self-efficacy beliefs either through motivating ‘team talks’ presented by trainers or coaches (eg: Samson, 2014, Zagorska & Guszkowska, 2014) but also through actions of ‘self-talk’ although one interesting study reported that the greatest elevations of self-efficacy, collective efficacy and performance indicators were with individuals who practised self-talk verbal persuasion that took the group’s capabilities as the focus (Son et al, 2011).

Somatic study is an enquiry that focuses individuals’ awareness holistically and is inclusive of associated physical and emotional needs and where decisions are influenced and informed by an intrinsic wisdom (Eddy, 2011). We understand ‘soma’ to mean in relation to the complete living body and in the context of behavioural regulation, it means a process of doing and being. This is especially distinct from cognitive regulation of actions and decision-making – hence Eddy’s attribution of somatic enquiry to dance. The connection here to Bandura’s work is that in forming judgements about capabilities, individuals’ physiological and affective states are partially relied upon and Bandura proposes that whilst somatic indicators are more especially relevant in efficacy judgements about physical accomplishments – in physical exertion such as strenuous exercise for example - our corporeal state is the most significant gauge of achievement, (or not, depending on our level of fitness perhaps) and hence influences our predictive ability to forecast likely future capacity and potential for further improvement – the ways in which our physiology reacts to or anticipates situation-specific circumstances and how our emotions are interrelated with this are impacting factors on efficacy judgements. (Bandura, 1997).

shakespeare quotationMany early research studies exist which explore the impact of affective states on learning – that is, how we are feeling whilst we are learning – especially following the publication of Bandura’s original paper about factors that drive and control self-regulation (1977) which kindled interest in how emotion influences learning. However some studies appeared oblivious of the significance of Bandura’s work but are of interest because they present a slightly different perspective on how emotions and affective states impact on behavior regulation. One interesting paper proposed a linkage system of ’emotion nodes’ that are each comprised of components that are connected to it by associative pointers such as autonomic reactions, verbal labels and expressive behaviours (Bower et al, 1981); the theory proposes that individuals’ memory patterns are likely to be more deeply engrained when ‘mood-congruency’ exists. For example, a literature student preparing for an exam may be more likely to be able to recall a significant quotation from Shakespeare’s ‘As You Like It’ if their affective state at the time of learning matches the mood expressed in the quotation.

It is clear to see how powerful this process might be in learning contexts, especially for exam revision, and could almost be interpreted as akin to Skinner’s conditioned-response theories of learning which gained such popular acclaim amongst contemporary educational psychologists and practitioners some decades since. More modern theories proposing means’ to enhance study skills continue to advocate the use of memory triggers as a highly effective technique for exam preparation, for example constructing hierarchical pattern systems or memory pyramids (Cottrell, 2013), and many are developments of study-principles rooted in the pre-technology age when assessment was more closely aligned with the effective recall of facts (Rowntree, 1998). Indeed, one of the most recent developments in relating affective states to learning and memory has resulted in an emotional prosthetic which, through a variety of ‘mood sensors’, it is claimed, allows users to reflect on their emotional states over a period of time (McDuff et al, 2012). This idea originated in earlier work on multimodal affect recognition systems designed to predict student interest in learning environments (Kapoor & Picard, 2005), and hence connect emotions and mood to learning effectiveness. The ‘AffectAura’ product emerged out of this field of research and appears to have been available from the developers at Microsoft as a download for installation on a local PC or Mac, however no sign of its current availability has been found suggesting that it was a research project that was eventually deemed commercially unviable.

Bandura too was taken by the idea of ‘mood congruency’ to support the argument about how affective states are able to directly influence evaluative judgements, (1997, p112, referring to Schwartz & Clore, 1988). The most important idea is about how individuals use a perception of an emotional reaction to a task or activity rather than a recall of information about the activity itself as the mechanism through which an evaluation is formed. Hence, positive evaluations tend to be associated with ‘good moods’ and vice versa although it is the attribution of meaning to the associated affective state which can impart the greater impact on the evaluative judgement. For example, a student who is late for an exam may attribute increased heart rate and anxiety levels to their lateness rather than associate these feelings to prior concerns about performing well in the exam – which in this case could possibly be a positive contributor to the likelihood of the student gaining a better result! Of more significance is that where mood can be induced, as opposed to being temporally inherent, a respective positive or negative impact on efficacy beliefs can also be observed, indeed the greater the intensity of mood that is evoked, the more significant the impact on efficacy becomes: individuals induced to ‘feel good’ exhibit more positive perceptions towards task characteristics and claimed to feel more satisfied with their task outcomes (Kraiger et al, 1989) which implies enhanced efficacy beliefs. More interesting still, is that mood inducement is reported to have a more generalized effect on efficacy beliefs rather than be directly connected with the domain of functioning at the time of the mood inducement (Kavanagh & Bower, 1985) which is clearly highly relevant in teaching and learning environments.

Contradictory evidence does exist however, suggesting that in some situations, induced negative mood in fact increases standards for performance and judgements of performance capabilities because it lowers satisfaction with potential outcomes and hence, serves to raise standards (Cervone et al, 1994) – at least amongst the undergraduate students in that study. The argument proposed is that a consequence of negative mood was an evaluation that prospective outcomes would be lower and hence the level of performance that is judged as satisfactory, is raised, resulting in an outcome that is better than expected. In other words, make students miserable, they will try harder and hence get better results. A curious and surely dubious educational strategy to pursue. In any event, this is aligned with the idea of ‘affect-as-information’ the broad gist of which is that in general, individuals are more likely to more easily recall and focus on the positive aspects or outcomes of a task or activity when they are in a ‘good mood’ and equally more likely to experience the converse when their mood is more negative (Schwarz, 1989). In Bandura’s Social Cognitive Theory, the impact of affective state on perceived self-efficacy follows a similar contention: that success achieved under positive affectors engenders a higher level of perceived efficacy (1997).




In more recent writing, Bandura has taken an agentic perspective to develop Social Cognitive Theory (Bandura, 2001) in which 'agency' is the embodiment of the essential characteristics of individuals' sense of purpose. Sen (1993) argues that agency is rooted in the concept of capability, which is described as the power and freedoms that individuals possess to enjoy being who they are and to engage in actions that they value and have reason to value. Hence in adopting this perspective the notion of capability becomes more crystalized as a tangible concept rather than as an elusive threshold one, as outlined above. Cross-embedded with capability is autonomy with both being dimensions of individualism against which most indicators of agency have been shown to have strong correlations (Chirkov et al, 2003) in the field of self-determination theory (Ryan & Deci, 2000). Capability and, to a lesser extent, autonomy have been shown to be key characteristics for successful independent and self-managed learners (Liu & Hongxiu, 2009, Granic et al, 2009), especially in higher education contexts where the concepts have been enshrined as guiding principles in establishing universities' aims and purpose, strongly endorsed by the Higher Education Academy some two decades ago (Stephenson, 1998). In this domain, Weaver (1982) laid down the early foundations of the 'capability approach' with strong arguments advocating the 6 Cs of capability - culture, comprehension, competence, communion, creativity, coping - that set to transform the nature and purpose of higher education away from the historically-grounded didactic transmission of knowledge to largely passive recipients through a kind of osmotic process, into the kind of interactive, student-centred university learning to aspire to. Capable learners are creative as well as competent, they are adept at meta-learning, have high levels of self-efficacy and can adapt their capabilities to suit the familiar, varied or even unfamiliar activities, situations and circumstances in which they find themselves (Nagarajan & Prabhu, 2015).

In Social Cognitive Theory, agency is where individuals produce experience as well as gain it, and as such shapes, regulates, configures or influences events that they engage in (Bandura 2000). It is viewed in terms of temporal factors embodying intentionality and forethought. These are deemed essential bases for planning, time-management and personal organization which are all elements of self-regulation that temper behaviour or are drivers of motivation in response to self-reactive influences. In particular, these are influences that guide or correct personal standards and foster introspective reflection about one's capabilities and the quality of their application in the self-examination of one's own functioning. Bandura advocates efficacy beliefs as the foundation of human agency (ibid, p10) and the most important idea is that three forms of agency are differentiated in social cognitive theory where each has a different influence on the behaviours and actions of individuals. Most of the theory and research centres around personal agency, with the focus being on how cognitive, emotional and affective processes, motivation, and choice all contribute towards shaping our actions. It is here that the key concept of self-efficacy belief is located, and, as outlined earlier, this construct is theorized as one of the drivers that influence our goals and aspirations, our feelings and emotions in relation to activities and behaviour, our outcome expectations and how we perceive and engage with difficulties, obstacles and opportunities encountered in our social sphere. In proxy agency, the second derivative of agency in Social Cognitive Theory, the interest is with how individuals use influential 'others' to enable them to realise their outcome expectancies. This may be for one of three reasons: firstly, the individual does not consider that they have developed the means to reach the desired outcome; secondly, they believe that engaging someone to act on their behalf will see them more likely to achieve the outcome, or lastly, the individual does not want to, or does not feel able to take personal responsibility for direct control over the means to achieve the outcome. Proxy agency has been extensively observed in exercise research where numerous studies have evidenced the role of proxies in helping individuals manage the multiple self-regulatory behaviours that relate to continued adherence to exercise regimes (eg: Sheilds & Brawley, 2006,) and in industrial or institutional collective actions for example (Ludwig, 2014).

flipped classroomWhich leads neatly to the last form of agency, collective agency. Here, individuals act cohesively with a joint aim to achieve an outcome that is of benefit to all of them. This can be widely observed in the natural world where many animals work in swarms or in smaller groups together to strive towards a collective objective. In people, collective agency occurs extensively in group behaviour but most notably occurs in sport where it is a principle factor in effective team-working. More pertinent to our domain of interest, collective agency is witnessed in schools where teachers' beliefs in their own teaching efficacy have been noted to contribute to a collective agency in the institution which progresses the school as a whole (Goddard et al, 2000, Goddard et al, 2004a). Indeed, some studies have reported that high collective efficacy in schools can generate a strongly positive, institutionally-based, embedded learning culture, which in turn can impact positively on student achievement (Hoy, et al, 2006, Bevel & Mitchell, 2012). This has led to the emergence of fresh education research pioneered by Goddard (eg: 2001, Goddard et al, 2004b) and notable others (eg: Tschannen-Moran et al, 2004) leading to more recent interest in promoting learning and teaching regimes that adopt a more collaborative approach between teachers and students in the classroom to foster higher levels of academic achievement (Moolenaar et al, 2012) and more particularly in exploring how the 'flipped classroom' can completely turn around the learning process to place students in positions of much greater control over the mechanisms that they may individually adopt to gain knowledge and which then utilizes the expertise and guidance of their teachers or lecturers to create activities in the classroom that build on the academic material learnt independently. There are a mixture of advantages and pitfalls of flipped-classroom learning, not least because it is too early to judge the impact that this revolutionary change in learning ideology may have on student achievement but also because difficulties in operationalizing clear definitions of what is meant by 'flipped classroom' is obfuscating conclusions that might be drawn from research outcomes (Bishop & Verleger, 2013). However, what has also emerged from this field of exploring the impact of collective efficacy on learning and student achievement is the liklihood that a new construct has been identified, that of academic optimism, pioneered in early research by Hoy et al (2006) and which is gaining traction as a valuable measure that can identify linkages between collective efficacy and raised levels of student achievement in learning environments (McGuigan & Hoy, 2006, Smith & Hoy, 2007).

Bandura (2001) summarizes the application of the agentic perspective of Social Cognitive Theory to education and learning by drawing attention to 21st century developments in technology that have influenced all domains of learning. This has shifted the focus from educational development being determined by formal education structures and institutions (that is, schools, colleges and universities) to new learning structures where information and knowledge is literally 'on demand' and at a learner's fingertips. By virtue of SCT attributing personal self-regulation to be a key determiner of behaviour, it is clear that in this new learning landscape, those who are more effective self-regulators are likely to be better at expanding their knowledge and cognitive competencies than those who are not (Zimmerman, 1990). However, a modern debate about the impact of social media on learning effectiveness is becoming quite polarized with traditionalists arguing that the high incidence of engagement with social media negatively impacts on learning capabilities because it appears to present a constant classroom distraction (Gupta & Irwin, 2016) which supports Zimmerman's point above by converse example. Alternatively, advocates of embracing social media platforms to provide an alternative format for curriculum delivery argue that to do so increases learning accessibility, fosters inter-learner collaboration and encourages students to communicate to each other about their learning much more readily (Lytras et al, 2014) even if there is less evidence to suggest that they use it for learning per se, hence engendering some of the principles of Universal Design for Learning, advocated earlier.

The graphic below draws from Bandura's extensive writings to summarize the various components and factors which enable individuals' self-efficacy beliefs to move them towards a behavioural outcome. It can be seen that the picture is far from straightforward but it shows that self-efficacy beliefs and performance as an accomplishment can be considered as precursors to outcome expectancies and outcomes themselves. In the mix, we see control and agency beliefs, but of particular interest is the extent to which confidence might be considered as a strong agentic factor in the flow from self-efficacy and performance towards outcomes especially in the light of evidence that this process is not as unidirectional as Bandura would have us believe (above). Nevertheless, Nicholson et al (2013) suggested that confidence, in tandem with 'realistic expectations', were key drivers that can influence academic outcomes. Findings from their study supported their expectation at the outset that more confident students would achieve higher end-of-semester marks (ibid, p12), a point made in the opening introduction of this paper.

self-efficacy beliefs map


The next sub-section briefly reviews the contributions from other notable researchers to social cognitive theory in educational domains, ahead of moving the discussion into the domain of academic self-efficacy and particularly academic confidence as a sub-construct of academic self-efficacy, locating the discussion into the context of this research project.



Social Cognitive Theory in Education and Learning: Pajares, Schunk, Zimmerman

The application of SCT in the realms of education and learning has attracted a substantial body of research amongst educational psychologists, theorists and research-practitioners, with notable colleagues and collaborators of Bandura leading the field in the recent decades. Of these, the three that it might be argued have contributed the most towards exploring the application of SCT in educational settings are Zimmerman, Schunk and Pajares who have worked both individually and collaboratively to present theses that attempt to tease out a better understanding of how knowledge is constructed in learning processes through the lens of Social Cognitive Theory. In particular, their interest has been in exploring self-efficacy beliefs as one type of motivational process in academic settings not least because motivation in learning has been widely accepted as one of the major contributing factors to academic achievement (eg: Pintrich, 2003, Harackiewicz & Linnenbrook, 2005). Studies include for example, exploring motivation and academic achievement in maths in Nigerian secondary school students (Tella, 2007), achievement motivation and academic success of Dutch psychology students at university (Busato et al, 2000), motivation orientations, academic achievement and career goals of music undergraduates (Schmidt & Zdzinski, 2006), academic motivation and academic achievement in non-specific curriculum specializations amongst Iranian undergraduates (Amrai et al, 2011) and in a substantial cohort (n = 5805) of American undergraduates (Mega et al, 2014). All of these studies indicated positive correlations between academic achievement and motivation although it was also a general finding that motivation in academic contexts can be a multidimensional attribute, succinctly observed by Green et al (2006) in their extensive longitudinal study of secondary students (n = 4000) in Australia.

Many of Zimmerman's less recent papers emphasize the idea of self-regulated learning as a central force that can drive academic achievement. Of this, it can be said that the examination of how individuals set learning goals and develop the motivation to achieve them has been Zimmerman's keen research interest, the outcomes of which have broadly demonstrated that students who are efficient at setting themselves specific and proximal goals tend to gain higher academic rewards when compared with other, less self-regulated peers (Zimmerman, 2002). Hence this evidence claims that becoming more self-aware as a learner is agentic in developing learning effectiveness (Zimmerman, 2001).

In reviewing the literature more carefully, three features of Zimmerman's research interests emerge that are significant:

  • firstly, both his own, and his meta-analyses of others' studies, generally focus on finding out more about whether learners display the specific attributes of initiative, perseverance and adaptability in their learning strategies and explore how procative learning qualities are driven by strong motivational beliefs and feelings as well as metacognitive strategies (Zimmerman & Schunk, 2007);
  • secondly, a 'soft' conclusion is reached arguing that, certainly as demonstrated in earlier research, skills and strategies associated with self-regulated learning had to be taught to students in order for them to subsequently gain academic advantages and that such strategies were seldom observed as spontaneous or intrinsically derived (eg: Pressley & Mc Cormick, 1995). This is interesting because it appears to support the approach adopted in higher education institutions (in the UK at least) that academic 'coaching' is likely to enhance academic achievement and, anecdotally at least, this coaching appears ubiquitous throughout universities who enroll learners from a wide range of backgrounds with an equally diverse portfolio of academic credentials. academic coachingWhat is not clear without a deeper evaluation of the relevant literature is whether academic coaching is a remedial activity that is focused on bringing 'strugglers' or those deemed as 'learning disabled' up to the required standard, or whether in being repackaged as learning development or academic enhancement, coaching services are being more widely taken up by a much broader range of learners from the student community, or even whether the more general academic portfolio that learners are bringing to university is not a match for the challenges of the curriculum and hence demands learner upskilling. A more jaundiced interpretation may also be that as a result of recent government initiatives ostensibly to drive academic standards upwards through hierarchical university grading systems such as the Research Excellence Framework and more latterly, the Teaching Excellence Framework (Johnes, 2016), it is in the business interests of universities to maximize the visibility of their academic 'standing' so that this can be used as a student recruitment initiative. In such circumstances, it might be argued that fostering a learning climate based on curiousity and inquisitiveness has been superceded by a need to ensure financial viability, even survival, in an uncertain economic climate in higher education, and that the desire to attract students has led to a lowering of academic standards and an element of 'grade inflation' (Bachan, 2017). 
  • The final observation is that in Zimmerman's and others' interest in developing devices to evaluate elements of self-regulated learning, these evaluative processes all seem to regard self-regulated learning as a global (learning) attribute and do not appear to have considered any domain specificity that may need to be accounted for. In other words, the assumption is that the study strategies that students apply are likely to be consistent across all their subject disciplines and no account is taken of differences that may be measurable in students' approaches to say, maths or sciences in contrast to studying humanities. This is all the more interesting given the American roots of both Zimmerman's research and the evaluative processes that his studies have contributed to because the curriculum in US tertiary education tends to be broader than here in the UK at least, and so we might have expected that the opportunity to explore curriculum differences in self-regulated learning would have been exploited. Other researchers who have explored self-regulated learning and its impact on achievement have adopted a more discipline-focused approach. For example, Greene et al (2015) specifically identified that as computer-based VLEs (Virtual Learning Environments) become more ubiquitous in places of learning, understanding how students' self-regulated learning may be different in science compared to history but their study was diverted into first exploring how to capture and model self-regulated learning, which led to contradictory research outcomes, finding both similarities and differences in self-regulated learning processing across domains.

academic athletesBuilding on earlier research about links between levels of achievement in academics and in sport (Jonker et al, 2009), McCardle et al (2016) studied Dutch competitive pre-university athletes and found that those presenting high engagement metacognitive processes and variables in their sports were also highly engaged in their academic studies. As demonstrated above, this is highlighting the important point that within the umbrella of Social Cognitive Theory, under which self-regulated learning resides, the co-associated construct of self-efficacy beliefs has been shown to be less general but more domain specific in not only learning contexts but in other areas of human functioning too. This example of self-regulation in sport may be an indication that high-engagement, self-efficacy beliefs can be a transferable learning approach. This is in keeping with the construct of academic confidence, considered as closely related to self-efficacy, but which appears to present as a more generalized learning attribute, with variances across disciplines, academic or otherwise, being less observable (Sander & Sanders, 2009).

Evaluative processes for self-regulated learning appear to have been developed not least due to the consensual definition which emerged from a seminal paper presented by Zimmerman (1986) to the American Educational Research Association. The paper sought to integrate contemporary researchers' work on learning strategies, volitional strategies, metacognitive processing and self-concept perceptions into a single rubric (Zimmerman, 2008, p167). The core idea of this definition was/is that self-regulated learning focuses on student proactivity in their learning processes as a means to enhance their academic achievement. This leads us to consider therefore, whether a 'good' or 'strong' student is more so the one who builds on intrinsic proactive learning strategies and academic learning management skills as opposed to an equally-achieving peer who has been taught self-regulated learning skills. This is pertinent in an increasing climate of student-coaching where those who have been coached subsequently derive academic enhancement. Given that assessment processes, notably summative ones where a student's performance is graded according to a mark achieved in a test or an exam, are supposed to be measuring student ability, if the exam outcome can be shown to have been significantly influenced through coaching, it follows that the assessment cannot be an accurate indication of the student's aptitude.

Schunk's contribution to research about the application of Social Cognitive Theory to educational domains follows a similar vein to Zimmerman's - hence their regular, collaborative projects. As another eminent student of Bandura's work, Schunk focused his research interests on learning more about the effects of social and learning-and-teaching variables on self-regulated learning with a particular emphasis on academic motivation, framed through the lens of Bandura's theories of self-efficacy (Schunk, 1991). In this early paper (ibid), goal-setting is said to be a key process that affects motivation, and in learning contexts Schunk's suggests that close-to-the-moment or 'proximal' learning objectives tend to elicit stronger motivational behaviours in children in comparison to more distant goals, an argument that is supported by a brief meta-analysis of other studies. goal settingIn young learners at least, Schunk finds that elevated motivation towards proximal learning goals is observed because students are able to make more realistic judgments of their progress towards these, whereas distant objectives by their very nature are said to require a much more 'regulated' approach - hence the interest and connection with self-regulated learning. Schunk also indicates that a significant difference in levels of motivation can be observed between target goals that are specific as opposed to those of a more general nature. In other words for example, this might be where an assessment requires a student to achieve a minimum mark in comparison to where a more general instruction to 'do your best' is provided as the target (ibid, p213). These are conclusions that are also evidenced in earlier studies: for example, in their meta-analysis of research of the previous two decades, Locke et al (1981) found that in 90% of the studies they considered, higher motivational levels of behaviour and subsequent performance were demonstrated towards specific goals when compared with targets that were easy to achieve, or learners were instructed to 'do your best', or no goals were set at all.

In a later collaborative summary paper, Schunk furthers his thesis on academic self-regulation by explaining how this grows from mastery of self-reflective cycles in learning processes (Schunk & Zimmerman, 1998) drawing on earlier work which established how self-regulated students are differentiated from their peers by their goal-setting regimes, their self-monitoring accuracy and the resourcefulness of their strategic thinking (Schunk & Zimmerman, 1994). Both prior and subsequent papers (and book contributions) generally reframe the core ideas that underpin self-regulation in learning as a function of self-efficacy beliefs with the research agenda broadly pitched at school-aged learners (eg: Schunk, 1996, Schunk, 1989, Schunk, 1984). However some studies have had a more focused interest, not least in exploring how self-regulative learning approaches impact on children's uptake of reading and writing skills. A meta-analysis of prior research in this field integrated with some field work of his own led to the conclusion that fostering the development of self-regulative strategies is an imperative for teachers of reading and writing skills if learners are to use such self-regulative devices such as progress feedback, goal-setting and self-evaluations to enhance their academic achievement in these core areas of communication skills (Schunk, 2003).

However Schunk has also been interested in the social origins of self-regulative behaviours in learning contexts,, demonstrated through an interesting study which considered self-regulation from a social cognitive perspective, noting that through this lens, it can be shown that students' academic competencies tend to develop firstly from social sources of academic skill. This is an idea that draws on earlier and much vaunted sociocultural learning theory, typically attributed to Vygotsky's thesis about the zone of proximal development, which is where learners are said to develop academic capabilities through supportive associations with their peers as much as through a teacher. Academic competency acquisition then can be shown to progress through the four stages of observational, imitative, self-controlled and finally self-regulated learning (Schunk & Zimmerman, 1997). The authors recommended that further research should be conducted, not least into how peer-assisted learning strategies (PALS) might be established in learning environments and we have witnessed the legacy of this idea in universities where many such initiatives have been established in recent years. Advocates of such programmes cite studies which support their benefits in terms of improved grades and skills development (eg: Capstick et al, 2004, Hammond et al, 2010, Longfellow et al, 2008). This has been especially true in medicine and clinical skills education where a development of peer-assisted learning, that of problem-based learning (PBL), actively generates learning through collaborative student learning enterprises. Here, only the required learning outcome has been specified with the route to the learning outcome mapped by the students participating in the programme, a process which includes the cooperative identification and thence distribution of component learning tasks, later to be brought back to the group for shared dissemination. Research outcomes show that such programmes can be effective learning mechanisms, are popular with students and can contribute to enhanced academic performance (Burke, et al, 2009, Secomb, 2008). However, PBL as a learning approach is not without its critics: For example, Kirschner et al (2006) claimed extensive evidence from empirical studies for the superiority for guided instruction not least because the instructive processes generally employed are aligned with human cognitive architecture and it is only when students are equipped with a significant level of relevant prior knowledge to provide 'internal' guidance, that PBL approaches can be shown to be of value.  Counter to this, Hmelo-Silver et al (2007) responded by arguing that problem-based- and inquiry-learning can be highly effective tools in the development of not only the lasting retention of knowledge, but also in enabling learners to know more about their own metacognitive processes, especially when such learning environments include carefully scaffolded support structures that permit multi-domain learning to take place. Not least, such learning approaches develop 'soft' skills such as collaboration and self-direction (ibid). In a university context a much later study explored the delivery of learning enhancement through virtual forms in contrast to peer mentoring schemes (Smailes & Gannon-Leary, 2011). The backdrop for the project was an increasing awareness of the rapid advance in the use of social media and networking amongst the student cohort and the research concluded that implementing a peer-mentoring programme that used this platform would be worth developing as a means to counter the sharp reduction in interest amongst university learners for the conventional, face-to-face PALS programmes. To date, the outcome of the second stage of the project appears to have been unreported so it is not possible to comment on whether the researchers' expectations where met.

Pajares’ early research interest was to explore 'teacher thinking' and in particular, how teachers' beliefs about their work, their students, their subject knowledge, their roles and responsibilities could each or all impact on educational processes, not least the learning quality of their students. The core point to be drawn was that teachers' beliefs should become an important focus for educational enquiry so as to contribute more fully towards understanding learning processes and engagement with education (Pajares, 1992). This line of research was supplanted in the mid-nineties with a deeper interest in self-efficacy beliefs and especially how these related to mathematical problem-solving in adolescents. A useful paper tried to establish key differences between math self-efficacy and math self-concept, finding that self-efficacy was a better predictor for problem-solving capabilities than other constructs, notably prior experience of maths and gender, in addition to math self-concept (Pajares & Miller, 1994). Other papers of this era exploring the relationships between maths self-efficacy beliefs and performance predictors showed support for Bandura's contention that due to the task-specific nature of self-efficacy, measures of self-efficacy should be closely focused on the criterial task being explored and the domain of function being analysed (Pajares & Miller, 1995). It is in these and other, related papers not only with a mathematics focus but also exploring the influences of self-efficacy beliefs on student writing, (eg: Pajares, 1996b, Pajares & Kranzler, 1995, Pajares & Johnson, 1995) that we see Bandura's self-efficacy theories enshrined and used to underpin much of Pajares' writing, not least drawn together in an important summary paper that sought to more generally apply Bandura's ideas to educational, academic settings (Pajares, 1996a) which also acted as a prequel for Pajares' deeper interest in the developing idea of academic self-efficacy.

maths self-efficacyWork of a slightly later period maintained output focused on maths self-efficacy in undergraduates in US universities. For example one study conducted a contemporary review of a previously developed Maths Self-Efficacy Scale(MSES) (Betz & Hackett, 1982) which is of interest to this project because it applied factor analysis to the scale's results when used with a sizable cohort of undergraduates (n = 522) (Kranzler & Pajares, 1997). Although the MSES had become a widely used and trusted psychometric assessment for establishing the interrelationships between maths self-efficacy and, for example, maths problem-solving, Kranzler & Pajares argued that looking at the factor structure of the scale is an essential process for gaining an understanding of the sources of variance which account for individual differences, claiming that this is required in order to substantiate results. The point is that through this statistical procedure, Pajeres and collaborators have shown a clear understanding of the multidimensional aspects of, in this case, maths self-efficacy but also the pertinence and value of local factor analysis being applied to local study-captured data. It was also interesting to note that for this study at least, Kranzler & Pajares' analysis led to their claim for the identification of a general meausre of self-efficacy which is at variance with Bandura's contention that self-efficacy beliefs are quite clearly context-specific (Bandura, 1997), and indeed also at variance with one of Pajares' own earlier studies (Pajares & Miller, 1995) which strongly argued for context specificity if research outcomes are to be considered reliable and valid. It is of note that in that study (ibid, 1995), the cohort of 391 undergraduate students' self-efficacy judgement were assessed according to three criteria: confidence to solve mathematical problems, confidence to succeed in math-related courses, and confidence to perform math-related tasks. Sanders' later (2006) contention is that (academic) confidence is a sub-construct of (academic) self-efficacy and although similar, the differentiation is necessary, and so we are left to consider that Pajares & Miller's study was in fact assessing maths self-confidence rather than maths self-efficacy albeit on the basis that this small but important distinction was yet to emerge. Key to this summary of Pajares' research output and contribution to self-efficacy theory in educational settings is more recent research and summary papers which sharpen his area of interest into the emerging field of academic self-efficacy (eg: Pajares & Schunk, 2001) and it is this sub-construct of self-efficacy that is the umbrella construct for academic confidence.

There is not the scope to also explore this wide ranging literature resource in detail in this thesis, nor is there a need as the groundwork review of the core theory, enshrined in Bandura's thesis on the position of self-efficacy in Social Cognitive Theory and the development of this into academic contexts is sufficient to underpin the research design and practical components of this project. These move directly to embracing the construct of academic confidence, operationalized as Academic Behavioural Confidence, as the dependent variable to which connections will be established with dyslexia so that we may not lose focus on the key objectives of the project, which is to establish that the process of identifying dyslexia in whatever form we may choose to define it in higher education contexts will impact on the academic confidence of students at university thus labelled.

Academic Behavioural Confidence


Academic Behavioural Confidence is the key metric that is being used as the dependent variable in the data analysis for this research project and represents the operationalization of academic confidence, a sub-construct of academic self-efficacy as outlined above where it has also been shown that academic confidence may also be linked to the academic outcomes and achievement of students at university. Hence measures obtained through the application of the Academic Behavioural Confidence Scale to the three, research subgroups in this study are highly interesting because the outcomes that have been derived from the analysis may be suggesting that the identification of dyslexia has a negative impact on academic confidence and hence possibly on academic achievement, even though no research evidence has been found to date to show that absolute scores of ABC are directly linked to absolute academic outcomes such as degree classification or grade point averages. It is suggested that a study to directly explore this possible use of academic behavioural confidence as a predictor of academic outcome is overdue, especially amongst groups of students conventionally considered as being under-represented at university for a variety of reasons, the discussion of which is beyond the scope of this current study. However it is considered that this project offers a valuable contribution to this field of research, especially since it will be reported later in the Results, Analysis and Discussion section of this thesis that the comparison of ABC values between the three research subgroups of interest in this project clearly demonstrates that for this research datapool at least, the academic behavioural confidence of students with dyslexia is not only statistically lower than for non-dyslexic students, but also lower than for students with unreported dyslexia-like profiles.


Historical development of the Academic Behavioural Confidence Scale

In her doctoral dissertation, Decandia (2014) looked at relationships between academic identity and academic achievement in low-income urban adolescents in the USA. Although briefly reporting on the original Academic Confidence Scale developed by Sander & Sanders in 2003, her study chose to use neither that metric, nor the more recently developed version – the Academic Behavioural Confidence Scale – but instead reverted to an Academic Confidence Scale originating in a near-twenty-year-old doctoral thesis (McCue-Herlihy, 1997), which Decandia developed as ‘an organic measure of confidence in academic abilities’ (op cit, p44) for her study. academic confidenceThis earlier thesis by McCue-Herlihy does not appear to have been published and thus remains lodged in its home-university repository at the University of Maine. However this thesis would be of interest, as McCue-Herlihy’s Academic Confidence Scale appears to be the very first time such a metric was constructed. It is assumed that it was created to contribute towards gauging how the elements self-efficacy, academic achievement, resource utilization and persistence might be interrelated in a group of non-traditional college students and so McCue-Herlihy's work, presumably suggesting a measurable connection between confidence and routes towards achievement in academic study, appears to have pre-dated Sander's development of the Academic Confidence Scale. It is assumed that Decandia chose to use this earlier metric because the focus of her study appears to have been so similar to that of McCue-Herlihy's earlier research.

Sander's Academic Behavioural Confidence Scale is a development of an earlier metric, the Academic Confidence Scale, which had been designed and used to explain the differences in students’ expectations of the teaching-and-learning environment of university (Sander et al, 2000).  In that study, the research group comprised convenience samples of students from three disparate disciplines enrolled on courses at three different UK universities and the study emerged out of interest in finding out more about the expectations of students in relation to their learning at university. This line of enquiry arose out of anecdotal evidence from university teaching colleagues who had observed changes in aspects of student learning behaviours that it was felt may have been at least partly attributable to a shifting student demographic largely due to widening participation initiatives, and for an emerging trend to regard students as customers for university products (Hill, 1995). In this new approach to university learning provision which is now maturing, students are considered as consumers of the knowledge and learning that is the curriculum in a university course in line with the direction of the UK Government White Paper: Higher Education and Research Bill (2016), of which one of the main provisions has been the introduction of the Teaching Excellence Framework, albeit on a trial basis, designed to increase accountability and raise teaching quality. Hence, students have been increasingly demonstrating customer-like behaviours that are focused on gaining value from their institutions (Woodall, 2014) not least due to the emerging awareness amongst student communities that they are directly paying for their university education and hence, are more readily questioning what they get for their money although at present, this remains an under-researched area (Money, et al, 2017).

The research group in the Sander et al (2000) pioneering first study consisted of medical students (n=167), business studies students (n=109) and psychology students (n=59) with the cohorts each studying at a different university. The questionnaire that was deployed interrogated students’ expectations of teaching and learning methods and respondents were requested to indicate their preferences. Aside from results and discussion that were specifically pertinent to this study, the construct of academic confidence was proposed as a possible explanation for significant differences in groups’ preferences in relation to role-play exercises and of peer-group presentations as approaches for delivering the respective curricula. In particular, the group of medical students and the group of psychology students both expressed strong negativity about both of these teaching approaches but it was the difference in reasons given that prompted interest: the medical students cited their views that neither of these teaching approaches were likely to be effective, whereas the reasons given by the psychology students attributed their views about the ineffectiveness of both approaches more to their own lack of competence in participating in them. Sander et al suggested that these differences may have arisen from students' levels of academic confidence possibly arising out of the different academic entry profiles of the two groups.

The idea of academic confidence was developed into a metric, the Academic Confidence Scale (ACS) (Sander & Sanders, 2003), where academic confidence was conceptualized as the extent to which students at university express strong belief or sure expectation about what the university learning experience will be offering them. Hence academic confidence is a less domain-specific construct than academic self-efficacy and Sander's rationale for designing and developing a distinct metric for exploring academic confidence has been a consequence of practitioner observations about how university teaching regimes and artefacts appear to influence student learning behaviours. This is significant for the researcher as it means that the metric can be used to explore attitudes and feelings towards study at university without these being in relation to a particular academic discipline or a specific academic competency – dealing with statistics for example, or writing a good essay. Underpinning academic confidence as a sub-construct of academic self-efficacy, this later study set out to explore the extent to which academic confidence might interact with learning styles and have an impact on academic achievement. Sander & Sanders argue that academic confidence is a ‘mediating variable between an individual’s inherent abilities, their learning styles and the opportunities afforded by the academic environment of higher education’ (ibid, p4). For that investigation, two further groups of medical and psychology students were recruited (again at two different universities, n=182, n=102 respectively) although rather than attempt to relate their evaluation of the students' academic confidence to particular teaching artefacts or learning interventions, the aim of this research was to merely explore changes in academic confidence between two time-points in the students’ studies, presumably to gain an insight into the impact that the university teaching and learning environment had on their levels of academic confidence although this was not a clearly stated aim. The gist of the research outcome was first of all that academic confidence was moderated by academic performance rather than acted as a predictor, and secondly that for these students at least, their studies appeared to have commenced with unrealistic expectations about their academic performance and that this was tempered by actual academic assessment outcomes, unsurprisingly perhaps. However, as a result of this study, construct validity was established for the ACS and a preliminary factor analysis was also conducted although differences between the factor loadings for the two student groups led the researchers to conclude that analysis on a factor-by-factor basis would be inappropriate in that study at least, although as we will see, the process of dimensional reduction was returned to later.

The scale was renamed as the Academic Behavioural Confidence Scale to recognize that is is more properly a gauge of confidence in actions and plans in relation to academic study behaviour (Sander & Sanders, 2006b) but in all other respects the metric was unchanged. Research interest in the Academic Confidence Scale in this early period was modest. Of the 18 studies found, one was an exploration of music preferences amongst adolescents, relating these to personality dimensions and developmental issues (Schwartz & Fouts, 2003) which although included academic confidence as a metric in the data evaluation, that scale appears to have been derived from one of the 20 scales included in the Millon Adolescent Personality Inventory (Millon et al, 1982), perhaps suggesting that at the time of the study, the researchers were unaware of the recently developed Academic Confidence Scale. Another study explored university students’ differences in attitudes towards online learning using the Academic Confidence Scale as one of a battery of 5 metrics in a longitudinal survey which aimed to gauge the impact of student engagement with an online health psychology module before and after the module was completed (Upton & Adams, 2005). The design focused on determining whether or not measures of academic confidence, self-efficacy and learning styles were predictors of performance on the module and hence which students would benefit most from this form of curriculum delivery. The study’s data analysis revealed no significant relationship between the variables measured and student engagement with the module from the 86 students included in the survey with the disappointed researchers claiming with hindsight that the lack of observable differences may have been attributed to an ill-advised research design and inappropriate choices of measures.

attritionLockhart (2004) conducted an interesting study about attrition amongst university students which was the first to explore the phenomenon using a sample of student drop-outs, acknowledging the range of difficulties that exist in contacting individuals who have already left their courses and to encourage their participation. As a result, the survey was small (n=30, in matched pairs of students remaining at, and students who had left university) but nevertheless a comprehensive battery of questionnaire items was used which were drawn from several sources, together with a programme of semi-structured interviews. The Academic Confidence Scale was incorporated into the research questionnaire with a view to exploring how different levels of confidence were related to student expectations of higher education. Care was taken to eliminate academic ability as a contributor to differences in academic confidence by matching pairs of participants for course subject and prior academic attainment. One of the research outcomes determined academic confidence to be a significant contributor to attrition, reporting that higher levels were recorded on the Academic Confidence Scale for participants remaining at university compared with those who had left their courses. It was acknowledged however, that many other factors also had a strong influence on students’ likelihood of leaving university study early. Of these, social and academic integration into the learning community and homesickness in the early stages of study were cited as the most significant. However Lockhart’s results also appeared to indicate academic confidence to be a transitory characteristic which is affected by the most recent academic attainments. This is consistent with the idea of academic confidence as a malleable characteristic, which had been suggested earlier through Sander’s original research and more strongly proposed in a later, summary paper (Sander et al, 2006a).  In a study similar to Lockhart’s, also into student retention and likelihood of course change, Duncan, (2006) integrated 5 items from the Academic Confidence Scale into the research questionnaire on the grounds that data obtained may offer insights into the mediating effect of academic confidence on the relationship between academic ability and academic integration, although no reasons for identifying these specific items from the full ACS as being particularly appropriate were offered. It is possible that the reason was simple expediency for reducing the questionnaire to a manageable size even though it still comprised a total of 151 Likert-style scale items. Results indicated academic confidence to be strongly positively correlated with course-change or drop-out intention. up_33

A highly focused study used academic confidence in relation to the influences of assessment procedures on the confidence of teachers-in-training, in particular the use of video recordings of teaching sessions (White, 2006). A mixed-methods design appears to have been used which combined questionnaire items with semi-structured interviews with participants (n=68) who were all level 7 students (= Masters level (QAA, 2014)). The research objective was to explore whether video assessment processes would mitigate uncertainties about lesson planning and delivery and increase self-efficacy and confidence. The Academic Confidence Scale per se was not used but elements of it were imported into the data collection process. Results were not discretely related to the construct of academic confidence but were used to support a much more general use of the term ‘confidence’ in the context of teaching planning and delivery. Hence the research outcomes specifically in relation to academic confidence as described by Sander were undetermined.

Of the remaining 13 studies out of the 18 retrieved that included or implied use of the Academic Confidence Scale, all were either conducted by Sander, usually in collaboration with others, or Sander appears to have been a contributing author. This collection of studies includes Sander’s own doctoral thesis (Sander, 2004) which explored the connections between academic confidence and student expectations of their university learning experience and built on the original project for which the Academic Confidence Scale was developed. The thesis comprised the author’s prior, published works which were all concerned with exploring students’ expectations and preferences towards teaching, learning and assessment at university. As previously indicated, it was for this purpose that the Academic Confidence Scale was originally developed and subsequently used as the principal metric. These early studies increased research assurances about the use of academic confidence to explain differences in students’ learning preferences with the findings providing evidence to argue that teaching institutions should be attempting to gain a greater understanding of their students as learners in order for their teaching regimes, artefacts and processes of curriculum delivery to be more effective (Sander, 2005a, Sander, 2005b). This was pertinent in the university climate of a decade or so ago that was witnessing student numbers increasing to record levels through a variety of initiatives, not least due to the emergence of widening participation as a social learning construct in education and the greater diversity of students that this and other new routes into higher education through foundation and access courses was bringing to the university community. An apparent consequence of this however, appeared to be greater attrition rates (eg: Fitzgibbon & Prior, 2003, Simpson, 2005) leading to a rise research attention being directed towards finding explanations for increasingly poor student retention with academic confidence being linked to students terminating their courses becoming one of the factors to have been explored, although reporting on this further is beyond the scope of this project.

The first of Sander’s studies to utilize the newly-named Academic Behavioural Confidence (ABC) Scale extended early research interest in the impact of peer-presentations on students’ confidence at university (Sander, 2006). This study also augmented the theory claiming to underpin academic confidence as a sub-construct of academic self-efficacy, arguing that the ABC Scale bridges the gap between self-efficacy and self-concept measures. Sander's point is that where self-efficacy measures stress the significance of mastery experience as a major part of the establishment and maintenance of efficacy beliefs, hence drawing on the underlying themes of Bandura's Social Cognitive Theory, these may not take a sufficient account of the wider socio-educational components in university study that affect students' concepts of themselves as learners. As with earlier studies, the research was driven by a desire to find ways to improve university teaching by understanding more about students’ attitudes towards teaching processes commonly used to deliver the curriculum. Two broadly parallel participant groups were recruited (n=100, n=64 respectively) and all were psychology students, mostly female. The research aimed to determine whether significant differences in academic confidence could be measured in relation to whether students were delivering non-assessed, compared with assessed presentations. Results indicated that despite the initial (and previously observed and reported (Sander et al, 2002, Sander et al, 2000)) reluctance of students to prepare and present their knowledge to their peers, beneficial effects on academic confidence of doing so were observed. Students typically reported these benefits to include experience gained in interacting with peers and hearing alternative perspectives about their learning objectives (op cit, p37). An interesting outcome from this study showed significant differences in post-presentation academic confidence attributed to whether the presentations were assessed or not assessed, with measurable gains in ABC being recorded following presentations that were assessed. Of particular interest in the discussion was an item-by-item analysis of ABC Scale statements suggesting this process as worthwhile for a better understanding of participant responses to be gained. This indicates that although ABC is designed to be a global measure of academic confidence, by exploring specificity, as revealed by comparisons taken from items within the scale, this can reveal greater detail about academic confidence profiles. Following their presentations, all participants in this study showed an increase in ABC items that related to public speaking. The idea of presenting the analysis of a construct in the form of a profile is a process that has been adopted in this current research project (see Section 3), both for academic confidence and for characteristics of dyslexia, on the basis that comparisons between profiles are easier to comprehend than tables of data because significant differences or contrasts can be more readily spotted which can lead to a more meaningful and deeper analysis of these differences or contrasts subsequently being conducted.

A slightly later study explored gender differences in student attitudes towards the academic and the non-academic aspects of university life looking at relationships between gender and academic confidence, a research topic that has been taken up by others (below). Results from analysis of data collected using the ABC Scale showing that males gave a lower importance rating to their academic studies in relation to the non-academic side of being at university in comparison to females (Sander & Sanders, 2006b). Drawing on literature evidence arguing that females generally lack academic confidence and that males are more likely to rate their academic abilities more highly than female students, findings obtained through the ABC Scale questionnaire were, however, inconclusive with no overall differences in ABC between males and females being identified. This was explained as most likely due to the relatively small research group (n=72) and the strong female participant bias both in students enrolled on the course (psychology, females = 82.4%) and in the survey (⚤ = 80.6%) which it was suggested would have added a significant skew to the research outcome. This study also reported in more detail how the Academic Behavioural Confidence Scale had emerged from the earlier Academic Confidence Scale as an iterative development process in collaboration with university teaching staff who were recruited to contribute towards designing the stem-items that together formed the Scale.

Pursuing a similar agenda, a susequent study added to the earlier evidence (op cit) about noticeable gender-differences in attitudes to study revealed through use of the ABC Scale, which confirmed some previous findings about measurable differences in academic confidence between male and female undergraduates, with differences being observed, in this study, particularly during their first year of university study (Sander & Sanders, 2007). It was argued that male students may be disadvantaging themselves due to a different orientation to their academic work which, it was suggested, compounded other issues faced by male psychology students through being in a significant minority in that discipline. Again, interesting ABC Scale individual-item differences were revealed showing for example, that male students were significantly less likely to prepare for tutorials and also less likely to make the most of studying at university in comparison to their female peers. These findings were consolidated by returning to the same student group at a later date, hence creating a longitudinal study. Although students from both genders were included in the study the research focused specifically on the academic confidence of male students (Sanders, et al, 2009). Once again, whilst there was little significant difference between ABC scores of males and females overall, detail differences on an item-by-item basis did emerge which were explained as possibly revealing a measure of over-confidence in males’ expectation of academic achievement – especially in the first year of study. However it was noted that this perception was not displaced later, as actual academic achievement was comparable overall to that achieved by females and suggested that in this study at least, males saw themselves as able to achieve as good a result as females but with less work, with poorer organization and less engagement with teaching sessions. It was observed that a higher attrition rates amongst males enrolled on the course may have impacted on this conclusion however, with the suggestion being made that had those students who left, instead stayed, their most likely lower academic attainment may then have enabled a more accurate picture of the true relationships between academic confidence and gender in this research cohort to have been determined.

A renewed interest in the structure of the Academic Behavioural Confidence Scale used factor analysis (Principal Component Analysis (PCA)) once more to search for subscales in the main scale (Sander & Sanders, 2009) with the claim that were these revealed, this may lead to a more satisfying explanation of unexpected lack of differences in academic confidence when examining the between-groups scores in earlier studies. This process had previously been applied to the first of their studies resulting in six subscales being suggested: Grades, Studying, Verbalizing, Attendance, Understanding, Requesting. Emerging out of the later application of PCA to the combined datasets from their previous studies (ntotal=865) were firstly the same six subscales as had been previously suggested, but through the additional analysis of structural equation modelling concluded that a revised, four-factor structure more accurately reflected the most likely nature of the complete ABC Scale. These were designated: Grades, Verbalizing, Studying, and Attendance, and following further analysis exploring scale-item redundancy, the original 24-item scale was reduced to 17 items. A slightly later study (Sander et al, 2011) using the Spanish language version of the revised, 17-item ABC Scale with a substantial sample of Spanish university students (n=2056) provided validation and confirmatory evidence to support the recent four-factor subscale model. The analysis outcome from that research was also used to suggest that the ABC Scale can be helpful in gaining an understanding of students' orientation to their studies, notably as a diagnostic tool to aid tutors in creating more effective learning opportunities.

Meanwhile, other studies using the Academic Behavioural Confidence Scale were beginning to emerge, possibly as a result of more widespread interest in a seminal paper presented by the original researchers (Sander & Sanders, 2006a) that summarized and consolidated their findings to date, binding their theories about academic confidence and how it affected student learning and study behaviours more closely to the substantial body of existing research on academic self-efficacy. In that paper useful comparisons between attributes of the related constructs of academic self-concept, academic self-efficacy and academic behavioural confidence were made, which drew on a lengthy and substantial comparative review (of the two former constructs) grounded in theories of academic motivation (Bong & Skaalvik 2003). The comparison table is reproduced here as a pertinent summary of dimensions of all three constructs:

Comparison dimension Academic self-concept Academic self-efficacy Academic Behavioural Confidence
Working definition Knowledge and perceptions about oneself in achievement situations Convictions for successfully performing given academic tasks at designated levels Confidence in ability to engage in behaviour that might be required during a (student) academic career.
Central element Preceived competence Perceived confidence Confidence in abilities
Composition Cognitive and affective appraisal of self Cognitive appraisal of self Assessment of potential behavioural repertoire
Nature of competence evaluation Normative and ipsative Goal-referenced and normative Response to situational demands
Judgement specificity Domain specific Domain specific and context specific Domain and narrowly context specific
Dimensionality Multidimensional Multidimensional Multidimensional
Structure Hierarchical Loosly heirarchical Flat and summative
Time orientation Past-oriented Future-oriented Future-oriented
Temporal stability Stable Malleable Malleable
Predictive outcomes Motivation, emotion and performance Motivation, emotion, cognition and self-regulatory processes and performance Motivation, coping, help-seeking and performance
      (Sander & Sanders, 2006a, Table 1, p36; adapted from Bong & Skaalvik, 2003)

Photo from Eleni Mantesou https://i.pinimg.com/originals/c6/e5/00/c6e500cad3731a9b25d33d3b99375766.jpgThis tabulated comparison of dimensions demonstrates a kind of cascade relationship between academic self-concept, academic self-efficacy and academic behavioural confidence. For example where academic self-concept can be thought of as how an individual holds self-knowledge and self-perceptions about themselves in broad, academic outcome-driven situations - for instance, studying at university - within this will be held beliefs about performance in a particular academic task at a specified level - say, constructing a final-year dissertation - and in order to accomplish this academic outcome, levels of confidence in engaging in the academic activities necessary to accomplish the task are functions of those academic activities. In a dissertation task this may be a student's level of confidence about how likely they are to be able to work out how to construct their primary argument without recourse to tutorial assistance.


Recent research using the ABC Scale

Since Sander's re-launch of his Academic Confidence Scale as the Academic Behavioural Confidence (ABC) Scale (ibid, 2006a) to date, 25 research studies have been found which use the ABC Scale specifically as a metric in their data collection processes. This includes further research studies conducted by Sander and collaborators either in fresh analysis of data they had collected in previous studies or with new data. Setting the Sander et al studies aside, the ABC Scale began to attract wider attention from researchers post-2009, indicating that it has been in use as a researcher metric for less than a decade.

Matoti & Junquiera (2009) were interested in perceptions of maths self-efficacy amongst South-African undergraduates enrolled on two distinct teacher-training courses but which both had a high mathematics content. It was an exploratory study which went no further than reporting the differences in ABC found between students on each of the courses, but also reported gender differences in ABC consistent with prior observations reported above. Although their overall results were inconclusive on complete ABC scores, they reported significant differences between their comparison groups when ABC scores were considered on an item-by-item basis.

Hlalele used the Academic Behavioural Confidence Scale in several studies, also in South Africa, where his broad interest was with student engagement in access and foundation courses. In one study (Hlalele & Alexander, 2011), the ABC Scale was used to gain an insight into self-perceptions of academic competence amongst students enrolled on a humanities access programme (n=141). The rationale for the study was to determine the extent to which these students demonstrated a need for learning development to increase their levels of academic confidence implying that it was lower than for students who had enrolled on a similar course through a regular academic route, although no direct comparison was made with such other students. Hlalele & Alexander were able to establish some within-group differences in ABC when sifting their sample cohort by gender, by age and by ethnicity. In a slightly earlier study relating to evaluating learning skills, also in university access courses, Hlalele (2010) argues in support of promoting the mastery of academic learning management skills as a means to improve students' academic confidence, working on evidence that academic confidence has been shown to be positively correlated with academic competence. The core focus in this study was that there should be a much wider inclusion of socio-emotional factors in university student programmes citing a theory of achievement which argued that five, foundational factors developed 'habits of the mind' which become conducive to effective academic performance and competence. Confidence is one of these factors, the others being persistence, organization, social amenability and emotional resilience. Although no primary research was conducted in this study, Hlalele strongly advocated the use of the Academic Behavioural Confidence Scale as an operational mechanism for assessing academic self-efficacy but particularly for highlighting the unpreparedness of access students for wider academic programmes.

A later study by Hlalele (2012) persisted with this theme of gaining insights into the academic confidence of access-course students, this time amongst those enrolled on foundation maths and science programmes (n=169). The suggestion was that due to a recent incorporation of an 'historically damaged (HD)' campus into an 'historically White (W)' university, differences in academic confidence were to be expected. No direct between-groups ABC comparisons were made however, with the focus just on exploring ABC levels within a student group from the HD campus. Again, the purpose seemed no deeper than to gain more knowledge about ABC levels in the research sample rather than to then use this as a precursor for proposing any learning development interventions or other new teaching artefact that might enhance the academic output of this group to an implied equivalence to students on the White campus. Although this perhaps suggests an underdeveloped research design, differences in ABC levels were revealed using the dependent variables of gender, age, and home language.

Taylor & House (2010) were interested in issues facing students in the emerging Widening Participation universities in the UK. Amongst students who considered themselves at university as a direct result of widening participation encouragements and incentives, their small-scale enquiry (n=42) highlighted concern about such students being perceived as 'non-traditional' and hence less academically able in comparison to their student peers who had enrolled in their courses through more conventional academic processes. Although the researchers did not use the ABC Scale as a metric in their study, it was suggested in their discussion that a development of their research might include it as a mechanism for identifying where learning development initiatives might be usefully targetted at WP students for enhancing their levels of academic confidence and dispelling their self-perceptions of academic inferiority.

The Academic Behavioural Confidence Scale has also been used as a longitudinal comparator to observe changes in academic confidence over time, or as a pre-, post-initiative evaluator. Chester et al (2010) used the ABC Scale as a within-groups before-and-after measure of students' changes in academic self-efficacy to help in identifying the effects of peer-to-peer mentoring schemes in three academic disciplines; and in a subsequent study (Chester et al, 2011) used the ABC Scale as a between-groups comparator to investigate the impact of newly-introduced podcasts as a teaching-and-learning artefact on the academic confidence of students who widely used them in comparison to those who did not and in both studies, measurable differences were recorded. Keinhuis et. al. (2011) were interested in pre- post-initiative effects on academic confidence, and in their study were exploring the academic outcome effects following the implementation of an 'Interteaching Model' designed to increase student engagement in large-class teaching situations. The Interteaching Model promoted mixed-mode delivery, but a key aspect was the introduction of small-group tutorials being held in advance of the main, large-class topic lecture with the aim of seeding keypoint ideas and concepts. The ABC Scale was used to evaluate changes in students' confidence for managing academic tasks. Results indicated that following curriculum delivery through the Interteaching Model, academic self-efficacy showed a significant increase on the Verbalizing subscale of Academic Behavioural Confidence, leading to a concludion that this was an indication that students had gained confidence in academic tasks that were dependent on verbal communication skills. Keinhuis et. al. returned to exploring the impact of the Interteaching Model on academic confidence in a later study conducted across a student cohort at the same institution, although it is not clear whether this was the same cohort as used in the earlier (2011) research in order to be a longitudinal study (Keinhuis et al, 2013). It was, nevertheless, a more extensive evaluation of their innovative teaching approach using several metrics to gauge the outcomes of the Model, of which the ABC Scale was one. Using the same methodology of pre- / post- analysis, the research team additionally attempted to measure levels of student engagement, student learning style preferences, student satisfaction, and particularly looked at actual learning outcomes in relation to students' perceptions of their academic progress. The original, 24-item, 6-subscale factor model of the ABC Scale was used and mixed outcomes were reported variously showing both positive and negative differences in ABC, pre- / post- , which was concluded to be useful in evaluating which aspects of their Interteaching Model were the most effective.

Another study using the ABC Scale in a comparative context explored differences in academic confidence between Mexican and European university students with the intent of finding out more about how learning culture influences confidence (Ochoa et al, 2012). Interesting findings added to the argument that over-confidence is prevalent in European students and that in so-called 'collectivist' cultures such as Mexico and Japan where the needs and goals of the group tend to prevail over a more individualistic, egocentric culture, more realistic levels of confidence tend to be the norm. A relatively small sample of students in Mexico (n=92) was compared with a much larger sample of European students (n=2685) this being a datapool of several combined studies' data which the researchers had access to. The outcome concluded that although the sample of Mexican students was small, the validity of the ABC Scale in a different cultural university learning context was demonstrated, and also that the results of the study were consistent with wider research that shows that in collectivist cultures, self-effacement is more prevalent generally as opposed to cultures promoting individualism such as in Europe and North America.

By using the ABC Scale to operationalize the acadaemic self-efficacy of under-graduate students (n=315) at an institution in South Africa, Matoti (2012) used an observational methodology to add to the research evidence supporting the usefulness of the metric to provide information about students' approaches to academic learning management through their study-skills behaviour. At a similar time, Putwain et al (2013a) were more directly exploring confidence in study-related skills and behaviours by conducting a robust scientific study with British undergraduates (n=206), which aimed to clearly relate levels of Academic Behavioral Confidence to academic achievement. This was an important project, not least because it was conducted collaboratively with Sander, the originator of the ABC Scale. The outcomes concluded that academic self-efficacy can be usefully assessed by gauging self-efficacy in self-regulated learning - which is the principle concern of the ABC metric - and that this is then a good predictor of future academic performance. This study also highlighted the value of information derived from the ABC subscales as a means to hone analysis conclusions more specifically. Through use of the 17-item, four-factor version of the ABC Scale with students at the start of their courses, the outcomes showed that levels of students' readiness to engage in the various kinds of study-related skills and behaviours which are required on their courses and which are assessed by the subscales, were strong predictors of their subsequent academic success at the end of their first year. Attention was also directed to how information gained in this way about new undergraduates could be useful for designing learning development initiatives that focus on developing students' perceptions of their own abilities with a view to grounding them at more realistic levels at the beginning of their courses. This work was consolidated in a similar study which confirmed that students who commenced their courses with a realistic expectation about their likely academic performance in conjunction with a strong, but not an over-confident approach to their academic learning management competencies were more likely to gain higher grades than peers who were less aware of their true academic competencies (Putwain et al, 2013b).

Meanwhile, a slightly different perspective on first-year undergraduates' self-perceptions of their academic competencies on commencing university study had been taken by Wesson & Derrer-Rendall (2011). In their two-study project with students at a UK university (n=121, n=77) the focus was on exploring the relationships between self-belief and goal achievement, building on earlier evidence that in addition to measures of absolute academic ability, future academic performance can also be linked to non-cognitive factors such as confidence about academic ability, effort requirements, goal-setting and task difficulty. Through using the Academic Behavioural Confidence Scale, one outcome of their research was that students with high-levels of ABC over-predicted their final grades and were thus described by Wesson & Derrer-Rendall as 'not as well calibrated' as their more moderately-scoring ABC peers, whose grade predictions tended to be more accurate. Conclusions indicated the importance of students being aware of a necessity to re-calibrate their academic confidence into more realistic levels as they progress from first-year study and that this can be based, not unsurprisingly, on increased study and academic learning management experience gained throughout this early stage of their university study. This 'calibration' approach in relation to confidence in academic competencies connects with Klassen's earlier work (2002) with students with learning disabilities (predominantly dyslexia) in North America which was also interested in how levels of confidence - more specifically referred to as self-efficacy beliefs - are accurately calibrated and why they are often not, in dyslexic learners.

de la Fuente, in a large-scale (n=2429) collaborative project with Putwain and Sander (de la Fuente, et al, 2013), reiterated Bandura's (2008) argument that there is a bidirectional relationship between [academic] self-efficacy and [academic] performance. This postulates that academic performance influences academic self-efficacy through mastery experience and that students with high levels self-efficacy tend to perform better. The study's outcome concluded that academic confidence, as one aspect of academic self-efficacy, can be a realistic predictor of academic performance although added that it is not the only predictor with other factors, notably prior achievement, having a significant effect. Although this study was looking at academic confidence specifically through the lens of gender differences, it did more generally confirm expected relations between confidence, approach to learning and achievement. Gender was taken as the categorical independent variable and the 17-item, 4-factor-subscale Academic Behavioural Confidence Scale was used and although overall results were mixed, when these were deconstructed by using the subscale components of ABC independently in relation to the other variables in the study, some significant gender differences were recorded.

structural equation modellingThe same research team (Sander, Putwain & de la Fuente, 2014) collaborated again in a study which was grounded in trying to understand which student learning factors might influence teaching and learning parameters so that ways to enhance student academic performance might be suggested. A particular focus of this substantial study looked at the role of academic confidence as a meaasurable sub-construct of academic self-efficacy amongst other variables such as effort regulation, gender, prior academic performance and age. The relatively advanced statistical technique of structural equation modelling (SEM) was used to explore and test models that might aid understanding of the simultaneous impact of these variables on academic performance. The broad rationale for applying such a technique to pre-existing data is that it permits distinctions to be applied to variables that are explicitly measured as opposed to those that are latent but known, in an attempt to judge how closely a constructed statistical model fits the data it is derived from (Hooper et al, 2008). In this study, Sander et. al. used data from their earlier studies to explore again the likely factor structure of the revised, 17-item Academic Behavioural Confidence Scale. The process treated as latent, the subscale factors of Grades, Studying, Verbalizing and Attendance, which had been derived from an earlier principal component analysis procedure, in conjunction with each of the individual scale items for all of the participants in their previous studies where these had been explicitly measured through completion of the complete ABC Scale. A clear point was made that their interpretation of the meaning of academic confidence was that it is an academic self-efficacy measure that assesses more general academic capabilities through exploration of behaviours in academic learning management and study-skill competencies, rather than a more specific self-efficacy measure which might attempt to gauge confidence to achieve a particular grading target or other clearly defined academic achievement criteria. One key outcome from the study was a conclusion that academic confidence, when operationalized as Academic Behavioural Confidence, should be considered more as a multi-dimensional construct rather than as a uni-dimensional one which had been the case when used in earlier studies. One notable feature of the discussion presented as a result of their structural equation modelling process was a clear alignment with Bandura's model of triadic reciprocal causation where the three facets of Human Behaviour, Internal Personal Factors and the Environment in Bandura's model were mapped respectively onto the student's behaviour in relation to their academic performance, the student's own internal cognitions and emotions, and the teaching-and-learning environment created directly by the teacher and more widely through the prevailing educational ethos of the institution, As a result of the SEM process, some pertinent general observations about academic confidence and about the Academic Behavioural Confidence Scale were made. Firstly, a strong argument was presented in favour of structural equation modelling as a technique for assessing the factor structure of metrics used in assessing aspects of student learning and study behaviour in universities, in particular, those which are aiming to contribute to a better understanding about how teaching and learning processes and environments can be made better - that is more effective - for student learning and engagement. Secondly a recognition of the multi-facetedness of the processes that are mutually interacting in teaching and learning spaces were strongly advocated. Notably, these were the relationships between student self-regulated learning processes and those which are external and regulatory as part of the construction of teaching. Lastly, and as has been identified in other studies reported in this section, the usefulness of academic behavioural confidence was commended as a metric for assessing and profiling individual learners so that individualized and highly targeted learning development interventions can be designed in response to specific scale-item responses in the ABC Scale. A very recent study that attempted to find out more about teaching procedures and learning processes at university focused especially on the roles that meta-cognitive, meta-motivational, and affective processes play in university learning and how these impact on academic output (de la Fuente et. al., 2017). Although not using the ABC Scale as the evaluator metric as the study was particularly intrested in the interactive relationships between tutor and student, evidence was cited supporting the increasingly understood positive associations between the academic confidence of university students and learning approaches, coping strategies and academic performance.

A similar process of structural equation modelling was conducted by Matovu (2014) with data on the academic confidence and academic effort of university students in Malaysia. In this case the process was used to assess the degree of model fit of an academic self-concept scale developed in an earlier study of secondary school-aged students (Lui & Wang, 2005) of which academic confidence and academic effort were suggested as the two subscales. The findings indicated a good fit of the model to the data. Other related studies in the social sciences but which are beyond the scope of this review have utilized a similar statistical technique to test evaluation scales which adds to research confidence in the process as a means to gain a deeper insight into the structure of such scales.

de la Fuente et al (2007) had previously developed a teaching-and-learning model, 'DEDEPRO', an acronym for DEsign, DEvelopment, PROduct, arguing that self-regulated learning, as a fundamental part of a successful university education, needed to include more than a focus on the student and the learning process because the learning context, which especially encompasses teaching processes, will be a significant factor. A later study (de la Fuente et al. 2013) which explored more specifically teacher-student interactions, proposed that a three-stage process is helpful: presage which is student self-regulation --> process, being self-regulated learning --> product which is the outcome measured as academic performance or achievement. With a sample of 765 university students, academic behavioural confidence was pinned to the construct of personal self-regulation by exploring the interface between teacher instructional styles and the effectiveness of the corresponding student learning, viewed through the lens of the development of self-regulated learning styles as desirable for promoting deep learning and hence, higher academic achievement. The ABC Scale was used as the primary metric and the study reaffirmed the belief that academic confidence can be considered as the anticipated belief in one's performance adequacy on academic tasks, rather than as having a more direct influence on absolute academic achievement. This means that academic confidence can be considered as a mediator between academic learning management processes, that is, study-skills, and academic output which is much in line with Sander's underlying precept of the construct.

In small but quite focused studies, other researchers have identified the ABC Scale as a useful metric: Miller (2014) used the Scale as a straightforward time-frame comparator on a particular teaching programme for English language learning. With a small group of first-year, female students (n=60) at a European university, the aim was to measure differences in academic confidence at the end of the course as a result of a learning intervention in the form of a peer-mentoring scheme to support learners who were finding the course challenging, in comparison to the baseline ABC measured on enrolment. The longer, 24-item scale was used and although the outcome was mixed, differences that were observed did indicate that the learning intervention had increased levels of academic confidence amongst the target group of students. Another small-scale study (n=54), also in the domain of language learning, looked at the relationship between academic confidence and learning anxiety in ancient language learning (Takahashi & Takahashi, 2014). Baseline levels of academic behavioural confidence of Japanese language students were measured which first indicated that in relation to general academic study, although this student group presented high levels of ABC these were lower overall than those measured in Sander & Sanders original (2000) study that was used to develop the scale. Even though these researchers did not make the connection in their report, it is possible that this observeration is consistent with academic confidence tending to be lower amongst learners in collectivist cultures in comparison to those in Europe and the USA as reported above (Ochoa et al, 2012). Takahashi & Takahashi's study was particularly interested in language-learning anxiety and was attempting to establish a correlation between this and academic behavioural confidence although none of a significant value was found.

Also using the ABC Scale as a temporal differences measure, Putwain & Sander (2016) were interested in how academic confidence is impacted on by the first year of academic study at university. ABC was measured at three time-points during the year and the results built on their earlier research that had suggested that students' academic confidence tends to decline during the first year of undergraduate study, possibly due to unrealistic expectations of the academic challenges of university study. This project used profiles of achievement goals as the focus - that is, study-related cognitions such as effor, persistence, help-seeking, planning, withdrawal, and based the research premise on the idea that students whose goal is to develop competence - that is, express a mastery goal - tend to hold more positive beliefs and adopt more proactive aademic learning management strategies than their peers whose goal is based on competence relative to others in their peer group - that is, performance goals. The 17-point, 4-factor model of the ABC Scale was used as the independent variable measured at three time-points with goal profile set as the dependent variable with students clustered according to their goal profiles. In other words, goal profile was assessed first, after which ABC was measured. Putwain & Sander reported that ABC either dipped and then recovered, or remained relatively stable throughout the year.

Sanders, Mair & Racheal (2016) were interested in learning more about the academic learning management and non-cognitive differences between traditionally-aged and mature students all at risk of non-completion. Two simultaneous studies were completed (n=160, n=503) with the aim of evaluating the use of the 17-point, 4-factors model of the ABC Scale and another metric, the Performance Expectation Ladder (PEL), to predict successful completion of the first year of study at university. The indpendent variable in the study was a straightforward binary outcome of the examining board which determined whether a student would progress (P) or not progress (NP) to the next stage of study. The research outcome showed that although the ABC Scale was more useful at predicting attrition than the PEL, it was only partially useful with just the 'attendance' subscale of the ABC Scale showing any significant differences between students in the P and the NP groups. Overall, the research consolidated earlier (unsurprising?) evidence that starting a learning course with a realistic expectation of a successful outcome is more likely to lead to a successful outcome. In a similar study, Sanders, Daly & Fitzgerald (2016) used the ABC Scale to explore foundation year students' expectations of the academic performance and achievement specifically to determine whether the levels of academic behavioural confidence might forecast attrition and hence be an early indicator of the need for learning development interventions. For this group of students (n=232), it was reported that the two subscales 'attendance' and 'grades' were good predictors of subsequent likely learning difficulties.

The summary of literature so far demonstrates the increasing interest in academic behavioural confidence as a construct worthy of research in tertiary learning contexts. The design rationale of the Academic Behavioural Confidence Scale as an evaluator of student study behaviours is rooted in a strong theoretical background stemming from Bandura's widely accepted Social Cognitive Theory and the metric is adding to a body of research evidence that argues in support of measuring academic confidence through this evaluator to find out more about how non-cognitive learning parameters might impact on student learning effectiveness and ultimately, their academic achievements at university. Such was the premise that underpinned a substantial meta-analysis (Braithwaite & Corr, 2016) which drew its research rationale from the earlier work the theorist, Hans Eysenck. Although principally a personality theorist, Eysenck also wrote on the relationships between personality and learning, indicating an emphasis for empirical, experimental studies of the effectiveness of education design and pedagogy, that is, how learners' personalities might influence their reactions to specific methods of teaching and the learning environment in which it takes place, and hence how this might impact on their academic attainments (eg: Eysenck, 1996). Brathwaite & Corr's meta-analysis looked at 47 studies (ntotal = 5771) that were all interested in proposing and testing methods of enhancing university student self-efficacy and self-confidence attributes as a means to influence a range of academic outcomes. Whilst it must be recognized that the process of combining data from multiple studies has the advantage of creating a much larger datapool, a cautious approach must be adopted to ensure that the parameters being explored in the combined data are as close as possible to those originally measured in each individual study. Ignoring this, not least because studies are rarely exact replications of each other, runs the risk of introducing bias and reducing the credibility of the meta-analysis outcome (Egger et al, 1997, Card, 2015). Notwithstanding this, the meta-analysis reported small to moderate but statistically significant positive effect sizes across all of the domain outcomes examined, significantly in repect of supporting the usefulness of the ABC Scale, a significant positive correlation was identified between ABC score and final degree outcome. This was consistent with a much earlier meta-analysis of 39 studies (Multon et al, 1991) which found a statistically significant relationship between self-efficacy and academic performance although in citing this earlier study, Briathwaite & Corr indicated that because Multon et al had embraced results from some non-experimental (that is, observational) studies on learning development interventions designed to enhance student self-evaluation processes in order to impact on a range of university-outcome-capabilities, an element of caution should be adopted in drawing too much out of the Multon et al study. However the significance of both of these meta-analyses, caution accepted, is the emergence of evidence that indicates that student learning behaviours, which includes academic learning management activities, are a factor that is additional to absolute ability in influencing academic outcomes at university.

One final study included in this chronological review of the recent use of the Academic Behavioural Confidence Scale is an interesting project which took an unusual approach by exploring levels of academic confidence, operationalized through measuring Academic Behavioural Confidence, in relation to past academic experience (Hill, 2017). This enquiry conceptualized prior academic experience as 'academic sustenance' and the research aim was to establish that (current) academic confidence is a function of academic sustenance which Hill determined in her study of Australian undergraduates (n=255) is comprised of 4 factors: encouragement, drive, grounding, and efficacy. Central to Hill's enquiry was advocacy of the increasing importance of understanding more about how students approach their studies at university, citing such research areas as motivation and self-efficacy as key elements of successful learning approaches, also arguing for a greater focus to be placed in institutions on more pro-actively developing academic competencies such as critical thinking abilities and multiple timeline academic learning management skills. Aside from this ethos resonating significantly with the research project reported in this thesis, Hill's use of the ABC Scale in her study is the only one found to date where a study-specific principal component analysis was conducted on the research results generated from the application of Sander & Sanders' complete, ABC Scale to the participant cohort, rather than adopting the existing and by now, widely used 4-factor subscales generated from Sander & Sanders' PCA analysis of their own data. As described later in the Results, Analysis and Discussion section, this process of study-specific PCA on data collected through the ABC Scale has been used in my research project because I remained equally unconvinced that the adoption of the 'standard' 4-factor model for determining subscales of the ABC Scale could offer the best analysis outcomes.

In summary: The Academic Behavioural Confidence Scale has been selected for use in numerous research studies since its development into its current form in the early 2000s. Researchers have used the metric in studies of university students for exploring the contribution that non-cognitive factors may make on the self-regulated learning approaches that are widely expected in higher education settings. Some studies have used the metric to evaluate temporal changes, either as a natural course of progression through the learning events of university semesters, usually in the first year of study or with students enrolled on access or foundation courses. Other research has shown that the ABC Scale is useful for gauging the impact of learning development initiatives or interventions on student engagement and achievement. Whilst some significant projects have used the ABC Scale to contribute to developing theories about student-teaching interactions and the learning-teaching interface with the intention of suggesting how these might be modified to enhance learning effectiveness at university with a view to raising academic attainment, or at the other end of the student-learning spectrum, to reduce attrition. Significantly, many studies have reported that academic confidence, as operationalized through academic behavioural confidence, may be related to academic achievement. It is of note that no published studies have been found which explore how specific learning difficulties such as dyslexia impact on academic confidence at university and hence there appears to be a gap in the research which this project fills.

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