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celebrate dyslexiaDraft (2017-18)


Academic confidence and dyslexia at university


thesis graphic


Theoretical perspectives

This is the Literature Review section of the thesis and is divided into two parts.


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 is about academic self-efficacy which is considered as the parent construct for academic confidence (Sander & Sanders 2006) and which is the outcome variable of the project. Academic self-efficacy and academic confidence are suggested to 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). These components are suggested to be mastery experience, which relates to how successes in tackling challenges are built upon positively; vicarious experience, where individuals form a sense of their capabilities by judging them in comparison to peers engaged in similar activities; verbal persuasion, in which individuals receive genuine and realistic encouragement from others deemed significant and important, and physiological states, which is about the impact of how individuals are feeling whilst they are engaged in challenges or tasks. In university contexts, Sander & Sanders (2003) suggest that academic confidence is 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 to gauge students' levels of academic confidence.

But first, a selection of literature is reviewed to provide a backdrop on 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 would be beyond the scope of this thesis. However, 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 take a positional stance to argue that now may be the time for these definitional paradoxes to be displaced in the light of the most significant more recent constructions of dyslexia which are challenging whether it makes any sense to be regarded as 'dyslexic' at all. These firstly advocate that dyslexia should be best considered as a multifactorial set of characteristics or dimensions which impact on a student's academic progress in a variety of both positive and less helpful ways; secondly, that this should 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; thirdly, that in order to accommodate these, the focus needs to shift in learning and teaching environment to work towards adjusting them in ways that are properly inclusive, accessible and flexible rather then continue to put the dyslexic individual at the centre of 'reasonable adjustments', arguing that this reversal would ameliorate much of the stigma associated with feelings of being different or disabled; and lastly to unequivocally support the suggestion that a better framework for understanding dyslexia might now exist by best considering it as alternative form of information processing (Tamboer et al, 2014). 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


A complex phenomenon or a psuedo-science?

Dyslexia - whatever it is - is complicated.

The contemporary view of dyslexia as it occurs in university students is as a learning difference rather than a learning disability, but 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 and for example, to be able to distinguish the syllables of a word and particularly to tune in to the individual sounds, or phonemes, of a word. Key to Snowling's summarizing was that 'dyslexia is [likely to be] characterized by a particular cognitive profile that places a child at risk of reading failure' (op cit, p20) which alludes to 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 (Ellis, 1984) which 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 in the Theoretical Perspective section of this thesis. 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 section of this thesis. But the most important points to be drawn out of Snowling's (2002) discussion which commenced this sub-section are firstly, emphasis on the importance of acknowledging phonological processing difficulties as significant in understanding what dyslexia is, but also to propose that dyslexia should be thought of as more than an issue with literacy (op cit).

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 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, some of which is outlined briefly above, but which also takes a much more inclusive approach by making no specific mention of deficits and affirms that some of the traits of dyslexia are to be recognized as abilities rather than all disabilities:

  • "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)

although the most up-to-date version of their definition (BDA, 2018) enshrined a slightly later report about identifying and teaching people with dyslexia (Rose, 2009) and expanded the range of characteristics still further:

  • "Dyslexia is a learning difficulty that primarily affects the skills involved in accurate and fluent word reading and spelling;
  • Characteristic features of dyslexia are difficulties in phonological awareness, verbal memory and verbal processing speed;
  • Dyslexia occurs across the range of intellectual abilities;
  • [Dyslexia] is best thought of as a continuum, not a distinct category, and there are no clear cut-off points;
  • Co-occuring 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;
  • 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)

Significant in both the original and the current BDA definitions is an absence of any reference to dyslexia as a disability, learning, or otherwise. But 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 the earlier research of Pennington (2006) and subsequently Le Jan et al (2009) by exploring 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 liklihood 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 which is one that 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 and this is presented in Section 4: Analysis and Discussion.


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Dyslexia: the definition issues

In summary, it might be considered that the major theories are divided into five, broad categories: firstly that dyslexia is a phonological processing disturbance which offers the explanation for reading difficulties as resulting from impairments in forming grapheme-phoneme correspondence, that is, understandling 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).

Frith (1999) tried to get to the nub of the definition problem by exploring three levels of description - behavioural, cognitive and biological - but still defined dyslexia as a neuro-biological disorder, speaking of controversial hypothetical deficits, and how these impact on the clinical presentation of dyslexia. The point here is that despite a courageous attempt to clearly provide a targeted explanation through an insightful analysis of the multifactoral impact of her three levels, this seminal paper still broadly concluded that 'undiagnosable' cultural and social factors together with (at the time) uncomprehended genetically-derived 'brain differences' persist in obfuscating a definitive conclusion. In his paper, Ramus (2004) took Frith's framework further, firstly by drawing our attention to the diversity of 'symptoms' (the preference in my paper here is to refer to a diversity of dimensions rather than 'symptoms' - more of this below) and subsequently confirming through his study that neurobiological differences are indeed at the root of phonological processing issues which is a characteristic that is often an early indication of a dyslexic learning difference, regularly observed. But more so, his study shed early light on these variances as an explanation for the apparent comorbidity of dyslexia with other neurodevelopmental disorders, often presented as sensory difficulties in many domains, for example, visual, motor control and balance, and others, which adds to the challenges in pinning dyslexia down. Although Ramus does not propose a single, new neurobiological model for dyslexia, more so suggests a blending of the existing phonological and magno-cellular theories (see below) into something altogether more cohesive, the claim is that evidence presented is consistent with results from research studies in both camps to date, and so is quite weighty. Fletcher (2009), in trying to bring together a summary of more recent scientific understanding of dyslexia, attempts to visualize the competing/contributory factors that can constitute a dyslexic profile in a summary diagram which is helpful.

fletcher's visualization of dyslexia

[adapted from Fletcher, 2009, p511]


Fletcher adds a dimension to those previously identified by Frith, Ramus, et al by factoring in environmental influences, not least of which includes social aspects of learning environments which may be some of the most impacting factors on learning identity. Mortimore & Crozier (2006) demonstrated that acceptance of dyslexia as part of their learning identity was often something that students new to university were unwilling to embrace, often because they felt that the 'fresh start' of a tertiary educational opportunity would enable them to adopt other, more acceptable social-learning identities that were deemed more inviting. This conclusion is supported by one respondent in the current research project who reflected on their dyslexia hence:

  • "I don't really like feeling different because people start treating you differently. If they know you have dyslexia, they normally don't want to work with you because of this ... I am surprised I got into university and I am where I am ... and I find it very hard [so] I don't speak in class in case I get [questions] wrong and people laugh" (respondent #85897154, available at: http://www.ad1281.uk/phdQNRstudentsay.html )

This illuminates aspects of dyslexia which impact on the identity of the individual in ways that mark them as different -in learning contexts at least - and is an important element that will be discussed below.

Other explanations rooted in physiology, notably genetics, have encouraged some further interest, notably a paper by Galaburda et al (2006) who claimed to have identified four genes linked to developmental dyslexia following research with rodents, and a more recent study which was concerned with identifying 'risk genes' in genetic architecture (Carion-Castillo et al, 2013). However, scientific as these studies may have been, their conclusions serve as much to prolong the controversy about how to define dyslexia rather than clarify what dyslexia is because these studies add yet another dimension to the dyslexia debate.

sensory differencesSensory differences is an explanation that has attracted support from time to time and attributes the manifestations of dyslexia most especially to visual differences - the magnocellular approach to defining dyslexia (Evans, 2003 amongst many others). Whilst there is no doubt that for many, visual stress can impair access to print, this scotopic sensitivity, more specifically referred to as Meares-Irlen Syndrome (MIS), may be a good example of a distinct but co-morbid condition that sometimes occurs alongside dyslexia rather than is an indicator of dyslexia. Later research by Evans & Kriss (2005) accepted this comorbidity idea and found that there was only a slightly higher prevalence of MIS in the individuals with dyslexia in their study in comparison to their control. Common in educational contexts to ameliorate vision differences, especially in universities, there is a long-standing recommendation for tinted colour overlays to be placed on hard-copy text documents, or assistive technologies that create a similar effect for electronic presentation of text. But evidence that this solution for remediating visual stress is more useful for those with dyslexia than for everyone else is sparse or contrary (eg: Henderson et al, 2013) or as one study found, 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 is an interaction between the two conditions which may have an impact on the remediation of either (Singleton &Trotter, 2005).

An alternative viewpoint about the nature of dyslexia is represented by a significant body of researchers who take a strong position based on the notion of 'neuro-diversity'. The BRIAN.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 BRIAN.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).


So defining dyslexia as a starting point for an investigation is challenging. This causes problems for the researcher because the focus of the study ought to be supported by a common understanding about what dyslexia means because without this, it might be argued that the research outcomes are relational and definition-dependent rather than absolute. However, given the continued controversy about the nature of dyslexia, it is necessary to work within this relatively irresolute framework and nevertheless locate the research and the results and conclusions of the research accordingly.

What seems clear and does seem to meet with general agreement, is that at school-age level, difficulties experienced in phonological processing and the 'normal' development of word recognition automaticity appear to be the root causes of the slow uptake of reading skills and associated challenges with spelling. Whether this is caused by a dyslexia of some description or is simply unexplained poor reading may be difficult to determine. Setting all other variables aside, a skilful teacher or parent will notice that some children find learning to read particularly challenging and this will flag up the possibility that these learners are experiencing a dyslexia.

What also seems clear, is that for learners of above average academic ability but who indicate dyslexia-associated learning challenges - in whatever way both of these attributes are measured - it is reasonable to expect these learners to strive to extend their education to post-secondary levels along with everyone else in their academic peer groups, despite the learning challenges that they face as a result of their learning differences. Amongst many other reasons which include desire for improved economic opportunities resulting from success at university, one significant attraction of higher education is a desire to prove self-worth (Madriaga, 2009). An analysis of HESA data bears out the recent surge in participation rates amongst traditionally under-represented groups at university of which students with disabilities form one significant group (Beauchamp-Prior, 2013). There is plenty of recent research evidence to support this which relates to students entering university with a previously identified learning difference and this will be discussed more fully in the final thesis. But a compounding factor which suggests an even greater prevalence of dyslexia at university beyond data about dyslexic students on entry is indicated through the rising awareness of late-identified dyslexia at university. This is evidenced not the least through interest in creating screening tools such as the DAST (Dyslexia Adult Screening Test, Fawcett & Nicholson, 1998) and the LADS software package (Singleton & Thomas, 2002) to name just two technology-based items which will be discussed further, below. But this is also a measure of the recurring need to develop and refine a screening tool that works at university level which takes more interest in the other learning challenges as additional identifying criteria rather than persist with assessing largely literacy-based skills and the relationship of these to perhaps, speciously-defined, measures of 'intelligence'. This is also discussed a little more and in the context of this paper, below.

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Disability, deficit, difficulty, difference or none of these?

With the exception of Cooper's description of dyslexia being an example of neuro-diversity rather than a disability, difficulty or even difference, definitions used by researchers and even professional associations such as the British Dyslexia Association, Dyslexia Action, The Dyslexia Foundation, The International Dyslexia Association, The American Dyslexia Association, all tend to remain focused on the issues, challenges and difficulties that dyslexia presents for individuals engaging with learning that is delivered through conventional curriculum processes. This approach tacitly compounds the 'adjustment' agenda which is focused on the learner rather than the learning environment.

head full of letters'Difficulty' or 'disorder' are both loaded with negative connotations that imply deficit, particularly within the framework of traditional human learning experiences in curriculum delivery environments that do remain 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. 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 which, although now broadly delivered electronically, still demand a 'conventional' ability to properly and effectively engage with the 'printed word' both to consume knowledge and also to create it. This persistently puts learners with dyslexia - in the broadest context - and with dyslexia-like learning profiles at a continual disadvantage and hence is inherently unjust. An interesting, forward-looking paper by 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 – a standpoint that resonates with the underlying rationale that drives this PhD Project.

Cavanagh is one of the more recent proponents of a forward-facing, inclusive vision of a barrier-free learning environment - the Universal Design for Learning (UDL) – which as a 20-year-old 'movement' originating from a seminal paper by Rose & Meyer (2000) is attempting to tackle this issue in ways that would declare dyslexia to be much more widely recognized as, at worst, a learning difference amongst a plethora of others, rather than a learning difficulty or worse, disability. 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 text-related issues, difficulties and challenges that are undoubted deficits in conventional learning systems 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 researchers persistently draw attention 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).

Other measures are still required to ensure an element of equitability in learning systems that fail to properly recognize and accommodate learning diversity. Extensive earlier, and recently revisited research on learning styles has demonstrated (not unsurprisingly) 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, is 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, preferences towards knowledge being presented visually is demonstrable in many dyslexic learners (Mortimore, 2008). There are arguments to support a neuro-biological explanation for this apparent preference which is based on investigations of the periphery-to-centre vision ratio metric. This describes the degree of peripheral vision bias in individuals' vision preferences and research evidence suggests that this is high in many people with dyslexia (Schneps et al, 2007) which means that many dyslexics evidence a preference towards favouring the peripheral vision field over the centre (Geiger & Lettvin, 1987). Ironically, this may also account for deficits in information searching capabilities often observable in many with dyslexia because accuracy in this activity relies on good centre-vision focus (Berget, 2016), it also may explain greater incidence of more acute visual comparative abilities and talents often associated with dyslexia. In learning environments this may be particularly significant where interrelationships between concepts are complex and would otherwise require lengthy textual explanations to clearly present meaning. Not least this is sometimes due to a comorbidity of dyslexia with attention deficit disorder where the dyslexic reader may experience difficulty in isolating key ideas or be easily distracted from, or find increasing difficulty in engaging with the reading task (Goldfus, 2012, Garagouni-Areou & Solomonidou, 2004) or simply find reading exhausting (Cirocki, 2014). Dyslexic learners often get lost in the words. However another detailed study of learning style preferences in adolescents which adopted the Visual-Auditory-Kinestetic learning styles model as the means for acquiring data revealed no significant differences between the learning styles of dyslexic participants and those with no indication of dyslexia although the research did demonstrate that dyslexic learners present a greater variety of learning style preferences than their non-dyslexic peers (Andreou & Vlachos, 2013). This is an interesting result which might be explained by suggesting that learning frustration experienced by more academically able dyslexic learners in attempting to engage with learning resources which, to them at least, present access challenges, is compensated by developing alternative learning strategies and styles which they match to meet the demands of learning situations as they are encountered. Many other studies in recent years have explored relationships between dyslexia and learning styles although conclusions reached appear mixed. For example, in the cohort of 117 university students with dyslexia used in Mortimore's (2003) study, no link was established between any preference for visuo-spatial learning styles and dyslexia which may seem unexpected in the light of other research suggesting that one of the characteristizing aspects of dyslexia can be elevated visuo-spatial abilities in certain circumstances (Attree et al, 2009). Indeed, common knowledge in professional practice in university level support for dyslexic students regularly advocates and provides assistive learning technologies that are designed to make learning more accessible for those with visual learning strengths. This continues to be a central provision of technology support for dyslexic students in receipt of the (UK) Disabled Students' Allowance . Searching for alternative means to provide easier access to learning for dyslexic students appears to have spawned other, interesting studies. For example, Taylor et al (2009) developed innovative animated learning materials, hoping to show that these provided improved learning access for students with dyslexia. However the outcome of the study showed that their animations were of equal learning value to both dyslexic and non-dyslexic students and attempted to explain this by suggesting that as with other forms of learning resources, non-dyslexic students typically find these easier to access than their dyslexic peers.


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Labels, categories, dilemmas of difference, inclusivity

[introduce this section drawing on the seminal work by Minow M, 1991, 'Making all the difference' that set out the broad framework in the inclusion/exclusion debate for accommodating difference as the most valuable and meaningful 'label']

categorizationThere are many well-rehearsed arguments that have sought to justify the categorization of learners as a convenient exercise in expediency that is generally justified as essential for establishing rights to differentiated 'support' as the most efficacious forms of intervention (Elliott & Gibbs, 2008). This is support which aims to shoe-horn a learner labelled with 'special needs' into a conventional learning box, by means of the application of 'reasonable adjustments' as remediative processes to compensate for learning challenges apparently attributed to their disability.

Outwardly, this is neat, usually well-meaning, ticks boxes, appears to match learner-need to institutional provision, and apparently 'fixes' the learner in such a way as to level the academic playing field 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 some researchers claim that such accommodations can sometimes positively discriminate, leading to unfair academic advantage because the 'reasonable adjustments' that are made are somewhat arbitrarily determined and lack scientific justification (Williams & Ceci, 1999). Additionally, there is an interesting concern that many students who present similar difficulties and challenges when tackling their studies to their learning-disabled peers but who are not officially documented through a process of assessment or identification (that is, diagnosis) are unfairly denied similar access to corresponding levels of enhanced study support. It is exactly this unidentified learning difference that the metric in this research study is attempting to reveal and the development of which is described in detail below. Anecdotal evidence from this researcher's own experience as an academic guide in higher education suggests that at university, many students with learning differences such as dyslexia have no inkling of the fact, which is supported by evidence (for example) from a survey conducted in the late 90s which reported that 43% of dyslexic students at university were only identified after they have started their courses (National Working Party on Dyslexia in HE, 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 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 technology which has been reported to have a significantly positive impact on study (Draffan et al, 2007). Some researchers who investigated the psychosocial impacts of being designated as dyslexic have demonstrated that embracing their dyslexia enabled such individuals to identify and use many personal strengths in striving for success, in whatever field (Nalavany et al, 2011). In taking 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 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 officialdom to adopt the medical model of learning disabilities and pay less attention or even ignore completely other challenges in educational systems. 'Learning disabilities' (LD) is the term generally adopted in the US, broadly equivalent to 'learning difficulties' everywhere else, of which it is generally agreed that 'dyslexia' forms the largest subgroup; and the legislation that is relevant here is enshrined in the UK in the Disability Discrimination Act, later followed by the Disability Equality Duty applied across public sector organizations which included places of learning, all replaced by the Equality Act 2010 and the Public Sector Equality Duty 2011. So one conclusion that may be drawn here is that as long as 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, pathologizing the poor performance of some groups of learners enables institutions to avoid examining their own failures (Channock, 2007).

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. 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. Social stigma itself can be disabling and the social stigma attached to disability, not least given a history of oppression and unequal access to many, if not most of society's regimens, is particularly so. 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). 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). Even in educational contexts and when the term is arguably softened to 'difficulties' or even more so to 'differences', the picture remains far from clear with one study (Riddick, 2000) suggesting that stigmatization may already exist in advance of labelling, or even in the absence of labelling at all. A significant essay by Ho (2004) strongly argued that whilst mainstream education systems remain broadly designed and delivered on the assumption that all learners engage with, and absorb knowledge in similar ways, such curricula will continue to fail to meet the academic needs of those who think and learn in different ways. Ho further argues that by pathologizing and labelling learning disabilities (i.e. dyslexia) the stigma traditionally attached to disabilities which remains endemic in educational environments and social structures may persist, this being despite advances in the social justice agenda, not least through disability legislation which has perhaps seen some progress being made in the last decade or so across society more generally. Ho's fundamental point is that it is more appropriate to adjust the curriculum rather than provide compensations for the learner, a perspective which again resonates with the stance of this current project.

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 more deeply analysed later, however an example presented here is representative of many similar others that were received:

  • "When I was at school I was told that I had dyslexia. When I told them I wanted to be a nurse [and go to university], they laughed at me and said I would not achieve this and would be better off getting a job in a supermarket" (respondent #48997796, available here)

Similar evidence relating to social bias was recorded by Morris & Turnbill (2007) through their study exploring the disclosure of dyslexia in cohorts of students who successfully made it to university to train as nurses, although it is possible that their similar conclusions to these other studies were confounded by nurses' awareness of workplace regulations relating to fitness to practice. This aspect of disclosure-reluctance has been mentioned earlier. It has also been recorded that the dyslexia (LD) 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).

pseudoscienceAs a key researcher and commentator of many years standing, Stanovich has written extensively on dyslexia, on inclusivity and the impact of the labelling of differences. His approach appears to be 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 he described the learning disabilities field 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) as also reported above. 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'. 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. and connects well with the principles of universal design for learning outlined above.


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Impact of the process of identification

Having said all this, 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/learning histories of individuals with dyslexia (eg: Dale & Taylor, 2001, Burden & Burdett, 2007, Evans, 2013, Cameron & Billington, 2015, Cameron, 2016). 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. The researchers observed from these postings that while some contributors took on a mantle of 'difference' rather than 'disability', 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.

The pilot study that underpins this current research project (Dykes, 2008) also explored feelings about dyslexia which was designed as a secondary aspect of its data collection process but it emerged that individuals responding to the enquiry were keen to express their feelings about their dyslexia and how they felt that it impacted on their studies. In the light of the findings of this earlier research, perhaps it should have been unsurprising to note in this current project, the significant number of questionnaire replies that presented quite heartfelt narratives about the respondents' dyslexia. Some 94% of the 98 QNR replies returned by students with dyslexia included data at this level. The complete portfolio of narratives can be accessed on the project webpages here and it is intended to explore this rich pool of qualitative data as the constraints of the project permit although it is anticipated that it likely that further, post-project research will be required in due course to fully understand it.

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.

However, and as already explored variously above, it seems clear that in the last two decades at least, many educators and researchers in the broad domain of revisiting the scope and presentation of tertiary-level learning and thinking are promoting a more enlightened view. It is one that rails against the deficit-discrepancy model of learning difference. It seeks to displaces entrenched ideology rooted in medical and disability discourses with one which advocates a paradigm shift in the responsibility of the custodians of knowledge and enquiry in our places of scholarship to one which more inclusively embraces learning and study diversity. There is a growing advocacy that takes a social-constructionist view to change the system rather than change the people (eg: Pollak, 2009), much in line with the Universal Design for Learning agenda briefly discussed above. Bolt-on 'adjustments', well-meaning as they may be, will be discarded because they remain focused on the 'disabling' features of the individual and add to the already burdensome experiences of being part of a new learning community - a factor which of course, affects everyone coming to university.

bits of textTo explore this point a little further, an example that comes to mind is technology 'solutions' that are designed to embed alternative practices and processes for accessing and manipulating information into not only so-called 'disabled' learners' but into everyone's study strategies. These are to be welcomed and great encouragement must be given to institutions to experiment with and hopefully adopt new, diverse practices of curriculum delivery although the rapid uptake of this seems unlikely in the current climate of financial desperation and austerity being experienced by many of our universities at this time. Having said this, encouraging or perhaps even requiring students to engage with technology in order to more easily facilitate inclusivity in study environments can raise other additional learning issues such as the investment in time necessary to master the technology (Dykes, 2008). These technologies may also remain too non-individualized nor easy-to-match to the learning strengths and weaknesses of many increasingly stressed students (Seale, 2008). So for differently-abled learners, these 'enabling' solutions may still require the adoption of additional, compensatory study practices, and may often be accompanied by an expectation to have to work and study harder than others in their peer-group in an academy which requires continuous demonstration of a high standard of literacy as a marker of intellectual capability (Cameron & Billington, 2015) and which moves to exclude and stigmatize those who cannot produce the expected academic outcome in the 'right' way (Collinson & Penketh, 2013). Eventually we may see this regime displaced by processes that will provide a much wider access to learning resources and materials that are available in a variety of formats and delivery mediums, the study of which can be assessed and examined through an equally diverse range of processes and procedures that carry equal merit. No apology is made for persistently returning to this point.


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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 learning differences. On the one hand, there is a clear and strong argument that favours changing the system of education and learning so that difference is 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 one to be adopted 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 the least 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).

Devising a process for gauging the level of dyslexia that an individual may present is only of any value in an educational context. Indeed, it is hard to speak of this without referring to severity of dyslexia which is to be avoided - in the context of this paper at least - because it instantly contextualizes dyslexia into the deficit/discrepancy model. However and as already mentioned, in the current climate labelling a learner with a measurable learning challenge does open access to learning support intended to compensate for the challenge. At university level, this access is based on the professional judgment of a Needs Assessor and on an identification of mild, moderate or severe dyslexia, with the extent of learning support that is awarded being balanced against these differentiated categories of disability, even though the differentiation boundaries appear arbitrary and highly subjective. This support in the first instance is financial and economic, notably through the award of the Disabled Students' Allowance (DSA) which provides a substantial level of funding for the purchase of technology, other learning-related equipment and personally-tailored study support tutorials. This is usually in addition to wider 'reasonable adjustments' provided as various learning concessions by the institution, such as increased time to complete exams. To date, and with the exception of a study by Draffan et al (2007) into student experiences with DSA-awarded assistive technology to which one conclusion indicated the significant numbers of recipients electing not to receive training in the use of the technology that they had been supplied with, no other research enquiries have been found so far that explore the extent to which assistive technology provided through the DSA, for example, is effective in properly ameliorating the challenges that face the dyslexic student learning in current university environments, nor indeed to gauge the extent to which this expensive provision is even utilized at all by recipients. Research into the uptake of differentiated study support for students with dyslexia also identified a substantial time lag between a formal needs assessment and the arrival of any technology equipment for many students (Dykes, 2008) which is likely to be a contributing factor to the low uptake of this type of learning support because students simply become tired of waiting for the promised equipment and instead just get on with tacking their studies as best they can. So it comes as no surprise that the award of DSA funding for students with dyslexia is under review at this time as perhaps this is an indication of how financial custodians have also observed the apparent ambivalency towards technology assistance from students in receipt of the funding, which ironically may be more due to systemic failures than to a perceived vacillation amongst students - more of this below.

However, to return to the point, one of the main aspects of this research project is a reliance on finding students at university with an unidentified dyslexia-like profile as a core process for establishing measurable differences in academic agency between identified and unidentified 'dyslexia', with this being assessed through the Academic Behavioural Confidence metric developed by Sander & Sanders (2006). So to achieve this, incorporating some kind of evaluator that might be robust enough to find these students is key to the research methodology. A discussion about how this has been achieved is presented in the next section.

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

It might be thought that 'measuring dyslexia' is a natural consequence of 'identifying dyslexia' but the commonly used dyslexia screening tools offer, at best, an output that requires interpretation and in UK universities, this is usually the task of a Disability Needs Assessor. Given an indication of dyslexia that results from a screening, what usually follows is 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 measurement to describe the extent of the dyslexic difference identified, other than a generally summative descriptor of 'mild', 'moderate' or 'severe', some assessment tools do provide scores obtained on some of the tests that are commonly administered. Nevertheless, these are generally are of use only to specialist practitioners and not usually presented in a format that is very accessible to the individual under scrutiny.

One student encountered in this researcher's role as a dyslexia support specialist at university recounted that on receiving the result of his assessment which indicated that he had a dyslexic learning difference, he asked the assessor: 'well, how dyslexic am I then?' He learned that his dyslexia was 'mild to moderate' which left him none the wiser, he said. One of his (dyslexic) peers later recounted that his view was that he did not think dyslexia was real because he believed that 'everyone if given the chance to prove it, could be a bit dyslexic' (respondent #9, Dykes, 2008, p95). His modest conclusion to account for his learning challenges was that his problem was that he was just not as intelligent as others, or thought that perhaps his lack of confidence from an early age decreased his mental capacity.

On the one hand, 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. This has been mentioned briefly earlier. Some identifiers include an element of assessment 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 also carry quantifiable measures of assessment although they are discretely determined and not coalesced into an overall score or value. Besides, there is widespread agreement amongst psychologists, assessors and researchers 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) as also reported above. 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, certainly once our learner progresses into the university environment.

checklist Hence is it widely reported (and mentioned elsewhere in this paper) that 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 abilities. The research is far from conclusive about the reasons for dyslexia compensation, most 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). Their study revealed that 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, p63). 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 and hence, identification and assessment processes that still have literacy and decoding skills at their core are less relevant than such tests may have been for earlier-years learners.

However even when strong indicators of dyslexic difficulties persist into higher education, comprehensive and daunting 'examinations' by an educational psychologist are unlikely to produce much by way of useful measurements to describe the extent of the dyslexic difference identified, that is, an individuals level of dyslexia-ness. Most usually, a generally summative descriptor such as 'mild', 'moderate' or 'severe' dyslexia may be the best outcome. This project is fundamentally interested in the concept of 'dyslexia-ness' becuase without gaining a sense about where an individual's learning and study profile lies along the spectrum of characteristics conventionally associated with dyslexia but may, to some degree, equally occur in apparently non-dyslexic learners, it would not have been possible to identify the key research subgroup of non-dyslexic students who are nevertheless presenting many of the learning characteristics often associated with dyslexia. Some assessment tools do provide scores obtained on some of the tests that are commonly administered but these are generally only meaningful to specialist practitioners and not usually presented in a format that is very accessible to the assessed individual. Aside from facilitating a route towards focused study skills support interventions, when a screening for dyslexia indicates that a full assessment from an educational psychologist is prudent, this becomes 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, suggesting that as a consequence, university community include a significant proportion of dyslexic students who choose not formalize their learning difference, and hence, open opportunities for the targeted learning support benefits that are provided for dyslexic students in most universities. It is of note, however, that with a recent refocusing of the target group of disabled students who are able to benefit from the DSA (Willetts, 2014) access to this element of support is likely to be withdrawn for the majority of students with dyslexia at university in the foreseeable future although for this current academic year (2016/17) it is still available. This may be an indication that dyslexia is no longer 'officially' considered as a disability, which is at least consistent with the standpoint of this research project, although it is more likely that the changes are as a direct result of reduced government funding to support students with additional needs at university rather than any greater understanding of dyslexia based on informed, research-based recommendations.

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. 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 posture stability test – which seems curiously unrelated although it is claimed that its inclusion is substantiated by pilot-study research - a backward digit span test and a non-verbal reasoning test. Literature review appears to indicate that some researchers identify limitations of the DAST to accurately identify students with specific learning disabilities, 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 such a coarse discriminator, 'at risk' again appears to be viewing dyslexia through the lens of negative and disabling attributes. 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.

studentsWarmington et al (2013) responded to the perception that dyslexic students present additional learning needs in university settings, implying 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. Incidentally,, Warmington et al quotes HESA 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), and also representing some 48% of students disclosing a disability, which certainly will make students with dyslexia the biggest single group of students categorized with disabilities at university, such that they are currently labelled. It is of note that the HESA data is likely to be an under-reporting of students with a learning disability - that is, specific learning difficulty (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. 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. 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 abilities and competencies of students at university (ibid, p49). 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.

Aside from the use of norm-referenced evaluations for identifying dyslexia as a discrepancy between intellectual functioning and reading ability being controversial, one interesting study highlighted the frequently neglected factors of test reliability and error associated with a single test score, with a conclusion that a poor grasp of test theory and a weak understanding of the implications of error can easily lead to misdiagnosis (Cotton et al, 2005) in both directions – that is, generating both false positives and false negatives.

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. 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, p2).

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 - that is, the severity of the dyslexia (using 'severity' advisedly as in itself, the term reverts to the model that to be dyslexic is to be disadvantaged, as mentioned earlier). Elliott & Grigorenko (2014) 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. To this end, 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 addition, and given the positive stance that this project takes towards including learners with dyslexia-like profiles into an integrated and universal learning environment, the design of the evaluator 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. For this research at least, it was felt that such a metric should be developed and 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 is being described as the Dyslexia Index of a student's learning profile and will attempt to collectively quantify learning, study and learning-biography attributes and characteristics into a comparative measure which can be used as a discriminator between students presenting a quasi-dyslexic and a non-dyslexic profile out of a sample of university students 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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Recent thinking

Describing dyslexia using a multifactoral approach

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 a 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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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). 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 which 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) and other, subsequent theorists and researchers in psychology and educational psychology who have taken a similar perspective on the processes and rationales which drive the interactivity of humans with the environment and with each other. The underlying principle in social cognitive theory is that it is an 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 multifactoral 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 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 social cognitive theory 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 being Skinner (eg: 1950), and 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, and so the focus will firstly 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; and secondly, close attention will be paid to unpicking the somewhat elusive construct of academic confidence as viewed through the lens of the self-efficacy component of Social Cognitive Theory. Lastly, a research development of academic confidence, namely Academic Behavioural Confidence (Sander & Sanders, 2006), will be considered in terms of its roots in SCT, its linkages with academic self-efficacy, its development through numerous studies that have used it as the principal metric in their research and concluding with the specifics of how it has been used in this research project to explore the relationships between dyslexia and academic confidence in university students.


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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 considered as 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 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 ever-evolving field of 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. Nonetheless, connectivism is winning advocates to its cause and 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.


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 ]

Much of these are tied up with forethought based on past experiences and other influences - many of these 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 behaviour is 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-observationjudgemental 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, as we will see below.

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) and although may possibly lead to elements of academic dishonesty (Hei Wan et al, 2003), it is more likely that proactive learning innovations will bring higher academic rewards.

However, being self-judgemental can be challenging, 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 judgements about one’s own performance relative to standards, which 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 show 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-judgemental 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, we learn, is the process by which standards regulate courses of action. This is about the way in which we integrate our personal standards 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 support the well-used cliché, ‘success breeds success’ with plenty of this in learning contexts: for example 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).

Thus the three-component structure of social cognitive theory has been briefly prequelled with particular attention being paid to its relationship to education and learning by providing examples about how the application of SCT might fit into learning and teaching contexts. But the functional operation of SCT now needs discussing and specifically, the construct of self-efficacy (and human self-efficacy beliefs) which is a key determiner that influences individuals’ choices about courses of action, how much effort they invest in them, their level of persistence and determination – especially in the face of adversity or setbacks – and the ways in which their thought patterns contribute positively or only serve to impede their progress.



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). This idea now moves the discussion forward a little and might be illustrated in the context of a typical, university, academic example:

  • 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 and we should notice that self-efficacy is domain (context) specific (eg: Wilson et al, 2007, Jungert et al, 2014, Uitto, 2014). Task and domain specificity is considered in more detail below. 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 underpinning theory to 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. To further illustrate the point that ‘capability’ should be not be left undefined, one other 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 indeed, if not exemplified at all (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. Perhaps we have to live with this kind of definition uncertainty but it remains unsettling for the researcher because surely science prefers ground rules and definitions when scoping out and conducting research as opposed to building a study on a foundation of intangibles. An analogy might be the reliance on ‘similar case evidence’ such that the legal profession are known to employ to attempt to prosecute a case in the absence of facts and witness statements, which may as equally leave a jury uncomfortable in reaching a verdict as it might the scientist about the outcome of a study. Nevertheless, working with difficult-to-define concepts and constructs appears to be the status quo for research in the social sciences and in this study, working with ‘undefinables’ is one of the limitations that is important to identify.

Thus the literature shows that many researchers 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 because the evaluation scales 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. In their scales, various characteristics of workers’ approaches to the demands of their occupations were supposedly determined – characteristics such as work satisfaction, organizational commitment and work performance – and although their scales were claimed to exhibit good reliability, any discussion about the likely, or at least possible, mutually influential interrelationships between self-efficacy and outcome expectancy was not evident, rather, offered an acknowledgement that the conclusion to the study remained disappointing and put this down to their results nevertheless being at least consistent with ‘the reality that performance is determined by many factors’ (ibid, p801). In the light of several earlier and contemporary studies which indicated that the causal unidirectionality was beginning to be challenged (see below) that had emerged between Bandura’s original thesis (1977) and Riggs’ research, it is a weakness in Riggs’ study for this not be considered as a factor which may have led to their ‘disappointing results’. Nevertheless, the four scales that their study developed, respectively measuring Personal Efficacy (PE), Personal Outcome Expectancy (POE), Collective Efficacy (CE) and Collective Outcome Expectancy (COE), do at least provide an insight into their interpretations of the interrelationships between self-efficacy and outcome expectancy in the context of an occupational setting (view the scales here) and their study’s factor analysis of the scales is claimed to support their understanding about Bandura’s early contention that self-efficacy beliefs and outcome expectancies are discrete constructs.

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 and despite Bandura’s numerous papers persistently refuting challenges (eg: Bandura, 1983, 1984, 1995, 2007) 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, notely 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

As an aside to trying to gain a clearer understanding of the message about level, strength and generality, it is of note that in this earliest of his writings on his theme, Bandura somewhat offhandedly speaks of ‘expectations’ which, in the light of the points made earlier, would be discomfiting were it not for later, clearer theses which relate the term to outcomes, with ‘efficacy expectations‘ being subsequently referred to as ‘perceived self-efficacy’ and ‘self-efficacy beliefs‘ – altogether more comprehensible terms. Indeed, in a later paper (1982) the phrase ‘efficacy expectations’ occurred just once and was used in referring to changes in efficacy through vicarious experiences (more of this below). By the time of this paper, Bandura’s discursive focus had sharpened with the result that the ideas were less confusing for the researcher, easier to understand and more appropriately applicable.



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.

And so it is apparent that the self-efficacy component of Bandura’s Social Cognitive Theory has been tested in a variety of domains. Aside from those described above, it has been applied in university athletics to explore aspects of training commitment and motivation (Cunningham & Mahoney, 2004), in sport more generally in relation to competitive orientation and sport-confidence (eg: Martin & Gill, 1991), in music performance anxiety, (Sinden, 1999), in health studies to explore outcome expectations of diabetes management (eg: Iannotti et al, 2006), and investigating alcohol misuse in college students (Oei & Morawska, 2004) amongst a plethora of other study foci. However, the particular interest of this project is with self-efficacy in an educational context – academic self-efficacy – and this is discussed in more detail below.

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

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 are supported by a plenty of research providing 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.
  • 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 impacting (Schunk et al, 1987). It is also interesting to note that in line with points raised above about the impact of technology on the domain of learning and the functioning of learners, the influence of social media on learning behaviour is now becoming more recognized and researched, particularly where the vicarious experiences gained through widespread use of social media networks amongst communities of learners in relation to their learning may be having an impact on academic outcomes, both positive and negative (Unachukwu & Emenike, 2016, Collis & Moonen, 2008).

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 statesare 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 thesis 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.

Having said this, contradictory evidence does exist which suggests 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, and other papers cited in this section are 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 broadly observed throughout tertiary education today. 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. Sometimes however, it can be observed that a collection of highly talented and skilled individuals - an example that comes to mind are some national football team in recent years - fail to bind together cohesively and cooperatively and hence, under-perform relative to both the individual expectations of the team members and indeed, their nations as a whole. Collective agency also occurs widely in the industrial or commercial workplace where unionized workforces collectively act towards, for example, improving working conditions and it can be seen that in this example in particular, a blend of proxy and collective agency operates to meet outcome expectancies. 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. This is in sharp contrast to the conventional, passive approach typically characterized by the process of listening to a lecture followed by an out-of-class 'homework' assessment activity. Research evidence is emerging which appears to be indicating 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 gathering credence 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).

In keeping with the points raised above, 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 social cognitive theory 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.  One concern worthy of mention is the emergence of an increasing body of research evidence that explores internet, and in particular Facebook addiction with its impact on student learning being a particular focus (eg: Hanprathet et al, 2015). One study even developed a Facebook Addiction Scale to assess the impact of this social media platform on college students' behavioural, demographic and psychological health predictors (Koc & Gulyagci, 2013) and the likely impact on academic progress with the outcome broadly supporting a negative correlation between Facebook addiction and academic achievement, with another paper claiming strong evidence for the adverse effect of smartphone addiction on academic performance (Hawi & Samaha, 2016).

In terms of the brain-bending impact that technology may have on learning, Bandura argues that examining the brain physiology which is activated in order to enable learning is unlikely to guide educators significantly towards creating novel or challenging conditions of learning nor develop faculties of abstract thinking, nor how to encourage participation or incentivize attendance, nor how to become more skillful in accessing, processing and organizing information nor whether more effective learning is achieved cooperatively or independently (op cit, p19). Indeed, it has been left to other researchers, some who have collaborated with Bandura, to explore more sharply the impact of applying social cognitive theory to academic achievement. For example, a longitudinal study which commenced at about the same time as the publication of Bandura's (2001) originative paper, used structural equation modelling in a scientifically robust methodology to examine the predictive nature of prosocial behaviour in children - that is, where prosocial actions included cooperating, sharing, helping and consoling - on the later academic achievement and peer relations of them as adolescents. The outcome, perhaps not unsurprisingly, was that prosocialness appeared to account for 35% of the variance in later academic achievement in contrast to antisocialness (broadly in the form of early aggresion) which was found to have no significant effect on either academic achievement nor social preferences (Caprara et al, 2000).

To conclude this section, the graphic below attempts to draw from Bandura's extensive writings to summarize the various components and factors which enable the processes which emanate from individuals' self-efficacy beliefs to move them towards a behavioural outcome. It can be seen that the picture is far from straightforward but we might observe how 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 the discussion earlier which presented evidence that this process is not as unidirectional as Bandura would have us believe. 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.



Other notable and influential researchers: Pajares, Schunk, Zimmerman

Bandura's Social Cognitive Theory explains human behaviour according to the principles of triadic reciprocal causation as briefly summarized above, and as we have seen, researchers from many fields have sought to apply the ideas to their domain of interest.

Significantly, 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.

Zimmerman and Schunk in particular have regularly collaborated on research papers and book chapters, and continue to jointly publish, especially in relation to the role of self-efficacy beliefs in the self-regulation of learning. Since their earlier, individual studies of the eighties and nineties, much of their later work is a repackaging of previous ideas and theories which have been regularly updated to incorporate and reflect on the later research of others. For example, their most recent work (Zimmerman et al, 2017), provides 'text-book' chapter-and-verse on self-efficacy theory aimed at students of psychology who are exploring competence and motivation in learning contexts. This paper specifically relates the cyclical processes model of self-regulation, which emerged broadly out of Bandura's triadic reciprocal causation foundation for Social Cognitive Theory, and discusses how it features within knowledge acquisition mechanisms widely employed by successful learners at all levels. This latest paper concludes with summary, admittedly small-sample, evidence to support the effectiveness of a Self-Regulation Empowerment Program (SREP), especially developed as an academic intervention that aimed to amend students' motivation, strategic learning and metacognitive skills in order to enhance academic achievement (ibid, p328). Encouraging results from this and other, broadly parallel studies (Cleary et al, 2016, Cleary & Platten, 2013), designed to test and validate the SREP showed that academic achievement of students taking part in the trails did indeed improve upon completion of the programmes and claims to show that by teaching students more about how to integrate self-regulation learning strategies into their study processes can bring academic rewards.

Many of Zimmerman's less recent papers which also reformalise much earlier work 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' 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). So far, detailed enquiries that explore these points more specifically have not been found aside from a doctoral thesis (Robinson, 2015) which was exploratory rather than evaluatative, and a few others which cast an element of disdain on the more general marketization of higher education with the student-as-consumer as the contemporary focus (eg: Nixon et al., 2016,  if no others exist then such a study is surely overdue. Having said this, Barkley (2011) noted that in the US at least, commercial for-profit organizations are emerging which offer academic coaching to students and in the UK, flurries of discussion on internet forums established by the growing legion of academic skills tutors and learning developers (eg: Learning Development in Higher Education Network) regularly return to the thorny issue of commercial proof-reading services and how these might obfuscate the true academic abilities of students who pay for these services with some arguing that paid proof-reading borders on cheating.
  • 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 SRL 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 prevalent 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 SRL which led to contradictory research outcomes which found both similarities and differences in SRL processing across domains.

academic athletesAdditionally, building 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. This is highlighting an important point as our earlier discussion above shows 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 more domain specific than general in not only learning contexts but in other areas of human functioning too. However this example of self-regulation in sport may be an indication that high engagement, self-efficacy beliefs can be a transferable learning approach. As we shall note from the discussion below, 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).

To return to our discussion about evaluative processes, these appear to have been developed not least due to the consensual definition of self-regulated learning 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 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 judgements 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 tells us 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. Indeed, pursuant to modernizing goal-setting theory into contemporary contexts, Locke and his co-researchers (Latham & Locke, 2007) continue to expound the theory, particularly reminding us that goal-setting strategies continue to be driven by the two factors of the significance of the goal to the individual, and self-efficacy, but bring this into a modern (business, rather than educational) setting by deriving what they term as a 'High Performance Cycle' (Latham, 2007) which relates to how employee motivation is affected by specific challenges and high-goal demands.

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 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), and his has been especially true in medicine and clinical skills education. In these disciplines 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 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. It is of significant interest to my research project that little research evidence has been found which particularly explores the impacts of peer-assisted learning strategies (PALS) on learners with specific learning difficulties (that is, dyslexia). Nevertheless, Fuchs & Fuchs (1999, 2000) have reported positive results of such initiatives on high school students with 'serious reading problems' where evidence analysed from admittedly a very small sample (n=18) showed that poor readers who were participants in a specifically designed PALS initiative, developed better reading comprehension and fluency and reported enhanced self-belief for tackling their reading difficulties when compared to their contrast counterparts. It would be interesting to explore this avenue further, perhaps as a development of this current PhD project for a later time.


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 from this extensive essay 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 on 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 (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. Their study was the first to do this. As will be reported in a later part of this PhD thesis, factor analysis of the results collected from this project's data collection instrument has been an equally essential process in gaining an understanding about what the data means, and as will be presented later in more detail, has shown that factor structures established from previous deployment of a psychometric evaluator can not necessarily be applied to a fresh study - in this case, the factor structure for the Academic Behavioural Confidence metric (Sander & Sanders, 2009) is shown to be worthy of 'local' factor analysis because the factor structure for my results presented differences in comparison to that obtained by Sander & Sanders in their studies. 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. However, 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. From this time onwards, a good deal of output was collaborative, for example with Schunk and Zimmerman as reported above, or summary or more generalized papers that reformatted earlier ideas and research into more contemporary contexts or were collections of earlier papers edited into lengthier handbooks (eg: Usher & Pajares, 2008, Schunk & Pajares, 2009, Pajares, 2008). 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 by disciple researchers and others 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. However, the short section which follows briefly summarizes the emergence of confidence as a non-cognitive learning attribute which leads into a review of the development of the Academic Behavioural Confidence Scale by Sander & Sanders, followed by a review of the most pertinent recent studies which have used the ABC Scale so that a background can be provided to support and justify the use of the metric in this project.


Confidence as a learning attribute


In the period pre-1967 a search retrieval returned only 8 studies with the phrase ‘academic confidence’ anywhere in the text with none including the phrase in the title. Three of these were studies that were more concerned with proposals in 1960s for integrating learning communities in an otherwise racially segregated USA and referred to academic confidence only deprecatively. Of the other five, one was trying to understand more about the learning challenges faced by child ‘retardates’ [ref here]; a much earlier study focused on academic challenges faced by young asthmatics [ref], and the others used the term in narratives that were otherwise unrelated to learning or education [refs]. The summary table below shows the increase in published research studies since this time:


Date range - 1967 1968 - 1977 1978 - 1987 1988 - 1997 1998 - 2007 2008 - 2017
Number of papers retrieved, n, with "academic confidence" found in the title or anywhere in the text * : 8 26 42 200 695 2240
Number of expected paper, N, based on exponential growth model: 7 22 67 208 644 1996
* using GoogleScholar, search conducted 28 February 2017

graph of exponential modelThe number of actual items retrieved, n, from the search for each time-frame was plotted, and an exponential trendline generated in MS Excel was applied to the datapoints. This generated the model equation shown in the graphic which was then used to generate the theoretical number of items that would be expected to be retrieved, N, using this exponential model.

As an illustration of real data demonstrating an exponential growth pattern, this is a clear example and may be indicative of the increasing recognition of academic confidence as a learning characteristic that can impact on the learning processes of individuals generally and academic achievement in particular. Or it may just be showing that the number of researchers has increased.

Either way, this micro-analysis may be demonstrating a renewed interest in exploring learning processes in ways that relate non-cognitive functioning more closely to academic processes. It could be argued that this surge in interest in the relevance of psychosocial factors indicates an awareness amongst educators that such constructs as confidence, persistence, resilience, can have a measurable impact on student performance, that is, on academic achievement and this is demonstrated by recent research that attempts to understand more about these and other affective factors in student self-identity can be influenced in ways that can enhance the quality of their academic output. For example, Robbins et al (2004), conducted a meta-analysis of 109 studies to examine the relationship between psychosocial and study-skills factors on academic outcomes - in these cases, Grade Point Average - concluding that academic self-efficacy - the parent construct for academic confidence - and academic motivation were strong predictors of GPA.

However, other interesting results emerged in the first instance through use of the phrase ‘learning confidence’ in place of academic confidence, and secondly by combining each of these phrases in a Boolean search with ‘academic achievement‘. The table below collects all the search output results together for comparison:


Date range - 1967 1968 - 1977 1978 - 1987 1988 - 1997 1998 - 2007 2008 - 2017
Number of papers, n, with " ~ " found in the title or anywhere in the text *
~ = academic confidence 8 26 42 200 695 2240
~ = learning confidence 15 9 60 105 537 1610
~ = academic confidence AND academic achievement 4 12 15 89 290 1160
~ = learning confidence AND academic achievement 0 1 6 12 69 266
* using GoogleScholar, search conducted 28 February 2017

It should be noted that this is the literature broadly available as returned according to search constraints applied and there is not the scope in this study to explore in detail the greater relevance of most of the output, setting aside of course, research that directly informs this project. However, a cursory inspection of the first few items returned in each search indicated that with the exception of studies where academic confidence, for example, was the primary focus of the research, the term tended to be used in a much more generally descriptive rather than evaluative way, or otherwise was measured using a relatively surface-based approach. For example, Hallinan (2008) was interested in the attitudes of school students to their school and how their perceived view about their teachers influenced this. Although the focus of the study was to explore ways to increase academic outcomes by improving students’ attraction to school, the attribute of academic confidence was only one of four variables used to do this and data was collected through acquiescence responses to just one statement: “I am certain I can master the skills taught in this class” (ibid, p276). Hallinan’s greater interest was in measuring clearly non-cognitive factors such as the extent to which students felt their teachers ‘cared’ about them, or how ‘fair’ they thought their teachers were. It was also apparent that the search output for the phrase ‘learning confidence‘ also returned results that included incidences where both were used as separate nouns rather than ‘learning‘ used in an adjectival form to describe the attribute of ‘confidence‘. Taking this into account suggests that the number of items returned using the phrase ‘learning confidence‘ may be an over-representation of the true number of papers which used the attribute in the way that ‘academic‘ is used to describe ‘confidence‘.

As presented in the overview of this narrative, Stankov's (2012) definition of confidence as 'a robust characteristic of individual differences' works well. Since the late 1980s, Stankov has been publishing research exploring aspects of individual differences and how these impact on learning and education. Ranging from early papers exploring , for example, how training in problem-solving might expose differences in its effects on fluid- and general intelligence (Stankov & Chen, 1988) to a substantial body of more recent research that focuses on unpicking the wealth of data additional to academic achievement that is collected through triennial PISA (Programme for International Student Assessment) assessments. PISA is a battery of tests and questionnaires completed across OECD nations that assesses the skills and knowledge of a snapshot of 15-year-olds. PISA has been running since 2000 and in addition to assessing academic competencies, also collects data about other student characteristics such as their attitudes to learning and how the participants approach their studies from a non-cognitive perspective. One of Stankov's most recent papers (2016) exploited the data reservoir of the latest, 2013 PISA survey, with the focus being on connecting the non-cognitive construct of self-belief to achievement in maths. The study draws on the premise that in addition to other non-cognitive variables (in particular, socio-economic status), self-beliefs are significant effectors of cognitive performance - that is, academic achievement - as either impediments in the form of anxiety, or facilitators where self-efficacy and confidence are the two major determiners.

Thus there is a demonstrable increase in research interest in confidence as an attribute that can be attached to learning and academic progress and as will be outlined in the following section, more specifically regarding academic confidence as an academic learning management attribute can enable the construct to be operationalized in the form of academic behavioural confidence and hence measured using the Academic Behavioural Confidence Scale.

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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.

It is being applied as a comparator to the three research subgroups of interest: students with existing, identified dyslexia; students with no identified dyslexia but who present a dyslexia-like profile of study and learning attributes as indicated through the Dyslexia Index metric that has been developed for this project; and students with no previously identified dyslexia and who also present a very low incidence of dyslexia-like study and learning attributes. The mechanisms through which these metrics have been used and the justifications for doing so it reported in the Research Design section of this thesis.

As outlined above, academic confidence, through being a sub-construct of academic self-efficacy, 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 will be reported later in the Data & Analysis section of this thesis that the comparison of ABC values between the three research groups 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-dyselxic 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. In her study 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 appears to have been driven by 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) pionering 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 as a result of academic confidence stemming from the different academic entry profiles of the two groups.

The idea of academic confidence was developed into the metric: Academic Confidence Scale (ACS) (Sander & Sanders, 2003), where academic confidence was conceptualized to enshrine differences in 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 vary student learning behaviours. This is significant for the researcher as it means that the metric to be used more generally 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. Acknowledging 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. Viewing it through this lens, 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 this 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 this study at least, although as we will see, the process of dimensional reduction was returned to later. The 24 Likert-scale items remained unaltered for this study however it is notable that the ACS had been renamed as the Academic Behavioural Confidence Scale in a later study three years after its original development to recognize that the scale is more properly a gauge of confidence in actions and plans in relation to academic study behaviour (Sander & Sanders, 2006b).

Subsequent research interest in the Academic Confidence Scale in the intervening period between its original development and its 2005 revision into the Academic Behavioural Confidence Scale was modest. Of the 18 studies found, these ranged from 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, it 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; to a study exploring university students’ differences in attitudes towards online learning (Upton & Adams, 2005) which used 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. 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, although it was acknowledged 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 – not unsurprisingly. 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 since, with a total of 151 Likert-style scale items, it is surprising that the researcher received data from a such a numerically robust sample (n=195) of final year university undergraduates although it is not known what percentage return rate this represents. In any event, results indicated academic confidence to be strongly positively correlated with the research hypothesis which was theorizing course-change or drop-out intention were linked to it. The correlation outcome is presumably strongly positively correlated with the null hypothesis although this was not clearly indicated. It would be a highly unexpected result if it emerged that high levels of academic confidence were related to high levels of attrition! 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 in relation to academic confidence as described by Sander were undetermined and again, it is possible that the availability of the Academic Confidence Scale was not known to the researcher at the time of the study.

Of the remaining 13 studies out of the 18 retrieved that included 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 (Sander, 2005a, Sander, 2005b) in order for their teaching regimes, artefacts and processes of curriculum delivery to be more effective. This was pertinent in the university climate a decade or so ago which 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 that many studies were exploring (see below).

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 claimed 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 depending on 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, exploring specificity, as revealed by comparisons taken from items within the scale, 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, 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 and as will be reported below, looking at relationships between gender and academic confidence is a research topic that has been taken up by others. 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. This idea of iterative development has also been adopted in this current research project in the development of the Dyslexia Index Profiler, the principle metric for establishing research participants levels of dyslexia-ness, introduced above and detailed more fully in the following section relating the research methodology.

Pursuing a similar agenda, a susequent study (Sander & Sanders, 2007) 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, but in this study, being observed particularly during their first year of university study. Key findings proposed 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 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 the researchers 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. A codecil added that a higher attrition rates amongst males enrolled on the course may impact on this however, with the suggesting being made that had those students who left, 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 saw the originators using factor analysis once more to search for subscales in the main scale (Sander & Sanders, 2009) claiming 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 to the combined datasets from their previous studies (n=865) were firstly the same six subscales as had been previously suggested, but through additional analysis, which included structural equation modelling, concluded that a revised, four-factor structure more accurately reflected the most likely nature of the complete ABC Scale. These were suggested to relate to Grades, Verbalizing, Studying, and Attendance, and following further analysis which explored scale-item redundancy, the original 24-item scale was reduced to 17 items. As will be reported below, there has been variation in which scale is preferred by recent researchers and also whether the factor approach to data analysis is used or not. 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. This analysis outcome from this 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, which presented evidence of 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, summarized briefly earlier. In this 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 useful 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 supports what I will term a 'nested umbrella paradigm' for understanding the cascade relationships between academic self-concept, academic self-efficacy and academic behavioural confidence. This is saying that 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 they will hold beliefs about how well they will perform a particular academic task at a specified level - such as 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 - for example, a student's level of confidence about how likely they are work out how to construct their primary argument without recourse to tutorial assistance, in other words, being able to get on with it without asking for help.


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, which 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. 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 this 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. However, 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 enrolled on foundation maths and science programmes (n=169). In this case, the implication 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 although again, no direct between-groups ABC comparisons were made with the focus remaining 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. Nevertheless, 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. Their small-scale (n=42) enquiry highlighted concern amongst students, who considered themselves at university through widening participation encouragements and incentives, about 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 been used by many researchers 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 change measureof student 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. 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. with their results indicating 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 the researchers concluding 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, 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 utility 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 whose input to the research process it must surely be assumed, was substantial. 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 and through use of the 17-item, four-factor version of the ABC Scale with students at the start of their courses, 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 about 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 (dyslexia) in North America, discussed above, 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, with academic performance influencing academic self-efficacy through mastery experience and that students with high levels self-efficacy tend to perform better. This 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 conofidence, 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 which had been explicitly measured through completion of the complete ABC Scale as part of their enquiries. The researchers made a clear point of arguing 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, discussed above, 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 related in this study, some pertinent general observations about academic confidence and about the Academic Behavioural Confidence Scale were made: Firstly, Sander, Putwain and de la Fuente presented a strong argument 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, the researchers also strongly argue for a recognition of the multi-facetedness of the processes that are mutually interacting in teaching and learning spaces, notably the relationships between student self-regulated learning processes and those which are external and regulatory as part of the construction of teaching; and lastly, and as has been identified in other studies reported in this section, the usefulness of academic behavioural confidence 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, the researchers did cite evidence 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 influential 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.

Other researchers have identified the ABC Scale as a useful metric in small but quite focused studies: 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 in that were observed indicated 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, this student group at least, presented high levels of ABC, although it was pointed out that even so, levels of ABC were lower than those measured in Sander & Sanders original (2000) study that was used to develop the scale. Although 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 time-frame 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 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 differences between traditionally-aged and mature students 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 (unsprising?) 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. By virtue of the design rationale of the Academic Behavioural Confidence Scale as an evaluator of student study behaviours and which is rooted in a strong theoretical background stemming from Bandura's widely accepted Social Cognitive Theory, this metric is adding to a body of research evidence that argues in support of measuring academic confidence through this evaluator as a means 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 of another highly regarded 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 in order 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. To ignore this and because studies are rarely exact replications of each other, the risk of introducing bias and reducing the credibility of the meta-analysis outcome is raised (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 links student learning behaviours, which includes academic learning management activities, is 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 below, 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 selectively used 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.

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