Academic confidence and dyslexia at university

Discussion

 

5.1  Datapool demographics

I      Prevalence of dyslexia

 

The most recent data acquired from the Higher Education Statistics Agency (HESAGreep, 2017) indicated that students in UK HE institutions who disclosed a learning disability accounted for 4.8% of the student population overall, this being a proportional rise of 50% from the figure quoted by Warmington (2013) for 2006. This is at least one further statistic to support the suggestion that the prevalence of dyslexic students in UK universities is rising. A variety of reason may account for this, not least recent initiatives for widening participation in HE amongst traditionally under-represented groups, particularly those with dyslexia who may have been previously disenfranchised from more formal education (Collinson & Penketh, 2010).

 

Greep pointed out that this figure (4.8%) was an indicator of the incidence of all 'defined' learning disabilities and in addition to dyslexia, included dyspraxia, ADHD and Asperger's Syndrome for example. Greep added that there is currently no mechanism in place in the current data collection process at HESA for discriminating students with dyslexia as a discrete subgroup of those indicating learning disabilities. Hence it is reasonable to suppose that the proportion of declared dyslexic students in the UK university population in 2013/14 is likely to be less than the 4.8% quoted although Greep did indicate HESA's view that dyslexia is likely to be the most represented subgroup.

 

Casale (2015) quoted HESA data which indicated that 5.5% of university students are disabled where presumably this figure included all disabilities of which students with learning disabilities is a subset, further claiming that dyslexia accounted for 40% of these students – i.e. 2.2% of the student population generally. Casale drew a comparison with data provided by the British Dyslexia Association (2006) claiming that dyslexia is evident in approximately 10% of the general population of the UK. However, estimates of the prevalence of the traditionally considered dyslexia as a reading difficulty in children vary considerably with studies suggesting rates ranging from 5% to 17.5% (Shaywitz & Shaywitz, 2005).

 

Table 4 (sub-section 4.3(I)) showed the distribution of students in the sample who disclosed their dyslexia on the research questionnaire (RG:DI, n=68) in comparison with those who indicated no specific learning challenges (RG:ND, n=98). A sample size of n=30 is widely considered to be the minimum for any reasonable statistical analysis to be conducted (Cohen & Manion, 1980) although the minimum value needs to be considered in the light of the proposed analysis (Robson, 2000). On this basis, it was considered that a complete datapool sample size of n=166, the number of students who returned replies to the research questionnaire is sufficient for a meaningful statistical analysis to be conducted. The two principal research groups divided this datapool in the ratio 41%:59%  (RG:DI, n=68 : RG:ND, n=98). Table 5 (sub-section 4.3(I)) showed the number of students in each of the research subgroups, Test, Control, and Base, indicating that 18% of students (n=18/98) who reported no specific learning challenges, presented levels of dyslexia-ness that were comparable to those of students who had disclosed their dyslexia. This was indicated by a Dyslexia Index of Dx ≥ 592.5.

 

Thus in this datapool, and on the basis of the boundary Dx value established for the ‘critical’ level of dyslexia-ness to be crossed for a non-dyslexic student to be considered as presentating comparable characteristics of dyslexia to the identified dyslexics, this proportion of quasi-dyslexic students may be a more realistic indication of the likely prevalence of dyslexia at university in this instance.

 

Hence it might be concluded that determining true levels of incidence of dyslexia either at university, in compulsory education, or in the general population is a challenging statistic to establish. This is certainly consistent with many of the arguments presented in earlier sections of this thesis discussing issues about how dyslexia is defined and hence relating to challenges in measuring it. As a result, it seems reasonable to conclude that it is likely that the true proportion of dyslexic students at university is inevitably higher than data indicate. Secondly, the data collected in this current study which, on the basis of the definitions of the metrics used, indicates that a substantial proportion of apparently non-dyslexic students with quasi-dyslexia which may indicate dyslexic learning differences, is consistent with evidence that dyslexia amongst university students is widely under-reported (Richardson & Wydell, 2003; Stampoltzis & Polychronopoulou, 2008), and/or continues to be unidentified on entry (Singleton et al., 2002), with The National Working Party on Dyslexia in Higher Education (Singleton, 1999; Singleton & Aisbitt, 2001) reporting that 43% of dyslexic students in the UK HE were identified after their enrolment at university. Given the age of the data from which this conclusion was drawn and the lack of a more recent study in the same vein, it is likely that estimates of the prevalence of dyslexia amongst university students remains inaccurate (Longobardi et al., 2019).

II     Gender

 

Data on participant gender was collected in this current project so that it could be demonstrated that the distribution of participants in the datapool was not dissimilar to the gender demographic of university students more widely in UK HE. HESA figures for the academic year 2016/17 for students enrolled on courses at HE institutions showed that although female students outnumbered males, the ratio is moderately close to an even balance ( female : male,  57% : 43% ). For the UK generally, the ratio of females to males in the population as a whole in 2016 was female : male, 51% : 49% (Office for National Statistics, 2016).

 

There is considerable research on gender gaps in HE across a range of interests. Historically, much of this has related to inequities faced by women and girls during their progression through educational processes. Although in recent years, fairer and more equal opportunities have become the norm in western educational settings, gender differences remain. Notably, these are less so in attainment, but still persist across a range of student characteristics and approaches to study or engagement with co-curricula activities at university (Sax & Harper, 2007; Sax & Arms, 2008; Zacherman & Foubert, 2014). In this current study, 113 of the 166 students who participated were female, representing a female : male ratio of 68%:32% showing that females outnumbered males by a factor of more than two-to-one. Noting the differences in response rates between the two recruitment processes goes some way to explaining this: (assumed) non-dyslexic participants were recruited through open publicity incentives published on the university intranet, whereas dyslexic participants were recruited through the closed, e-mail distribution list of the University’s Dyslexia and Disability Service. This led to a gender distribution of students in the non-dyslexic group of 61% female : 39% male, whereas amongst the dyslexic students recruited, the ratio was more substantially skewed towards female students (78% female : 22% male).

 

These data for gender distributions could be accounted for by student registration with the University's Dyslexia and Disability Service being heavily biased towards females. Although data to check this were not available for the home university, anecdotal evidence taken from personal employment in learning development domains at two other UK universities were accordant with this conjecture. This would be consistent with evidence that male students are less likely than females to engage with learning development or support services, either as a consequence of a known, hidden or unknown disability or learning difference, or indeed for any other reason (Fhloinn et al., 2016; Kessels & Steinmayr, 2013, Kessels et al., 2014; Ryan et al., 2009). This is also consistent with some gender differences reported in levels of engagement with education and learning for a variety of reasons but especially in the self-regulated learning contexts which is dominant in HE settings (Virtanen & Nevgi, 2010). A deeper analysis of the reasons behind these differences may be an interesting focus for a future study, however, the strong female bias in the data for this current study may be a limitation to take into account when interpreting the data analysis outcomes.

 
 
 

III    Student domicile

 

Tables 4, 5 (sub-section 4.3(I)) showed that the domicile distribution of the datapool in this study can be considered as representative of the wider student community studying at university in the UK. However, it is of note that only 3 out of the 68 dyslexic participants in Research Group: DI were non-UK students. This figure represents 4.4% of that group and might be an indication of a very low incidence of non-UK, dyslexic students studying at UK universities although equivalent data from other institutions or HESA are not available for comparison. It is also possible that this low figure may instead be an indication of the lack awareness of opportunities for access to the university's Dyslexia and Disability Service for non-UK students with dyslexia. Hence, very few non-UK students would have been on the Service's e-mail distribution list to receive the invitation to participate in this current study.

 

An explanation for this may be that since non-UK students are not eligible for formal dyslexia identification through the provision of the Disabled Students' Allowance in the UK, this group may also not be eligible to access the learning development and support provided by the Service to dyslexic students, or may not even be aware that such a service exists. But an alternative explanation may be that access to dyslexia identification processes in their home countries for these non-UK students, is less prevalent than in the UK for a variety of reasons. This might be supported in this current study by comparing the ratio of non-UK to home students for both identified dyslexic students (RG:DI) and apparently-unidentified dyslexic students (RG:DNI). For dyslexic students in research group DI this ratio is 3 in 68 (4.4% as mentioned above). For students sifted into research subgroup DNI due to their Dyslexia Index values of Dx ≥ 592.5, the ratio is 3 in 18 (16.7%) which at face value, suggests that there may exist a large proportion of un-identified, apparently dyslexic, non-UK students in this datapool at least. However, as this subgroup is small (n=18), only cautious conclusions from this disparity may be drawn as it may equally be accounted for through margins of error. It would be necessary to establish a much larger subgroup of apparently non-dyslexic students who were presenting high levels of dyslexia-ness and hence examine the distribution ratio of 'home' to non-UK students to be able to say more.

 

IV    How students with dyslexia learned of their dyslexia

 

Academic confidence may impact on academic achievement due to reduced academic self-efficacy, and this may be associated with the effects of stigma on the social identity of dyslexic students at university (Jodrell, 2010). Hence one of the strands of this current study is acknowledgement of the stigma that is reportedly associated with the dyslexia label (Morris & Turnbull, 2007; Lisle & Wade, 2013). Although there has not been the scope in this thesis to present a detailed discussion about how dyslexia may induce stigmatization, nor to explore the greater construct of stigma, the issue of labelling and stigmatization relating to dyslexia has been briefly discussed above (sub-section 2.1(IV). In relating this to the ‘dilemma of difference’, where there is a persistent and unresolved debate about the value of attributing labels to individuals with atypical educational needs (e.g.: Norwich, 1999; Warnock, 2005; Terzi, 2005), it was considered pertinent to explore the extent to which the participants in this current study were affected by their dyslexia label, gauged through the lens of academic confidence. They were asked about the way in which they learned of, or were told about their dyslexia at the time of first identification. The hypothesis being tested was whether evidence could be found in the data to indicate that students whose dyslexia was specifically diagnosed to them as a disability presented reduced academic confidence when compared with their dyslexic peers who had their dyslexic situation alternatively related to them.

 

It seems reasonable to argue that using the most neutral and unbiased terminology when informing an individual about their newly-revealed dyslexia is likely to be the least discomfiting. Of the realistic options available, identifying dyslexia as a difference is suggested as perhaps the most appropriate because the neutral tone does not allude to disability, hence the likelihood of the dyslexia being internalized into the student's self-identity as a medical condition is reduced, not least to avoid an associated (but false) implication that there may be a ‘cure’, tacitly implied by ‘diagnosing’ it. There is evidence to suggest that the largely negative construction of disability is widespread in society more generally and also that disability is frequently associated with clinical conditions (eg: Connor & Lynne, 2006, Phelan, 2010). However, at the time of designing this current study, no literature had been found which specifically explored the varying impact of the different ways in which dyslexia is communicated to an individual as a result of a screening or assessment at university. Some studies have examined the psychosocial experiences of receiving an identification of dyslexia. For instance, one study claimed to be the first to explore how confirmed, and self-identified dyslexia impacted on adult perspectives of their experiences associated with their dyslexia (Nalavany et al., 2011). The study did not report on the ways in which individuals had learned of their dyslexia, always referring to dyslexia being diagnosed, and was concerned less with how the adults in the research group (n=75) experienced the impact of their dyslexia on their learning, but more so with how it affected their day-to-day lives. However, many of the participants recollected school and learning experiences that were “hurtful, embarrassing, and scary” (ibid, p74) and that their teachers misunderstood their learning challenges, with the study documenting how the lasting effects of experiencing 'being different' in younger years can persist into adulthood.

 

Another study was also concerned with the psychosocial components of living with the label of dyslexia (Armstrong and Humphrey (2008), referred to above in sub-section 2.1(IV)) although the datapool comprised adolescents at school or college rather than adults studying at university. Nevertheless, the outcome led to a specific proposal of a fresh model for understanding how individuals assimilate their dyslexia into their self-identity (the Resistance-Accommodation Model). Although the study provided an insight into the 'dyslexic self', again, the authors only referred to how individuals accommodated their diagnosis of dyslexia. No suggestion was made that use of the term ‘diagnosis’ might itself impact on an individual's internalization of their new knowledge about their dyslexia. This was despite suggesting that “the amount of resistance or accommodation displayed by individuals clearly stems at least in part from their perception of dyslexia” (ibid, p99).  Hence it is argued in this current study that it is possible that this perception of dyslexia might also, in part, be influenced by the ways in which an identification of it is communicated, especially when this is labelling dyslexia as a disability, but even when it is described as a 'difference'.

 

Notwithstanding caution about drawing conclusions based on small-sample analysis, it was shown that students in this current study whose dyslexia was diagnosed to them appear to present a substantially lower ABC when compared with students whose dyslexia was identified, described or disclosed to them. The outcomes were similar whether dyslexia was diagnosed as a disability or as a difficulty. This suggests that the phrasing used to communicate new knowledge to a student of a learning difference that may be attributed to dyslexia, may have a measurable impact on the academic confidence that they subsequently bring to their studies. For students in this study at least, evidence is presented that those whose dyslexia had been diagnosed to them as a difficulty or a disability, would be expected to have a reduced impact on their academic confidence, had their dyslexia been disclosed, described or identified to them as a learning difference. It is unfortunate that so few students in the datapool learned of their dyslexia as a difference because were the sample larger, a useful layer of analysis may have been able to determine whether there is a measurable distinction in academic confidence between those with dyslexia identified as a difficulty and those with dyslexia identified as a difference. This may have added further weight to the argument that the ways in which a new identification of dyslexia is assimilated into an individual's learning identity can have a significant impact on their confidence towards approaching their studies at university.

 

A deeper interpretation of the results (Table 7, sub-section 4.3(II)) revealed that students whose dyslexia is diagnosed appear to be less confident about attending their classes, lectures, seminars and other university teaching situations, than students whose dyslexia had been identified, disclosed or described to them. One explanation for this may be that the diagnosed students perceive and internalize their dyslexia as a clinical, medical condition, through a not unsurprising association between ‘diagnosis’ and ‘illness’. Subsequently, discomfort or anxiety in the company of student peers is induced through concern that their dyslexia will be discovered, and they will be perceived as mentally unwell, due to the self-interpretation of diagnosis implying that there is something wrong with them. Hence they are not willing to risk inadvertent disclosure through their classroom responses being unusual or unexpected, all of which compounds to reduced class attendance as an avoidance strategy. Such behaviour may be consistent with observations of the day-to-day learning lives of dyslexic students at university (Cameron, 2016) which, although might be considered limited due to the case study approach of deeply analysing learning diaries from just 3 research participants, did reveal some relevant points: Notably that in learning situations in which they were attending as members of a class, seminar or lecture in the company of other students, all three participants appeared to find these learning experiences uncomfortable or threatening, reporting 'fear of speaking out in seminars or discussions' so as not to appear 'stupid or incompetent in some way'; that they all felt 'different from others', 'less able or intelligent' and that they 'didn't belong' in academic spaces (ibid, p228). All three participants also reported considerable difficulty in verbalising their ideas and thoughts when speaking out in university spaces and how this made them often feel awkward and demoralised.

 

Hence it seems reasonable to suppose that students with dyslexia who experience such difficulties might need little encouragement to avoid such learning situations where possible. Although sharply identifying how some dyslexic students feel when they are learning in the company of their peers, Cameron's study does not mention how these students learned about their dyslexia. Only on the basis of the data collected in this current study being assumed as a typical, representative cross-section of students with dyslexia at university, and where the majority (>60%) had dyslexia diagnosed to them, is it possible to surmise that the participants in Cameron’s study are more than likely to have had their dyslexia diagnosed rather than identified to them in some other way. The concluding remarks: “having the dyslexic label means being constructed by discourses of learning, disability and literacy as an outsider within the education system” and “there is a justification for some adjustments ... to pedagogy within higher education”, (ibid, p235), resonate with the findings in this current study, and indeed with the stance of this project. 

 

But evidence is also emerging that many of the competing demands experienced by dyslexic students are equally faced by some other contemporary learners. For example, Fraser (2012) suggested that it might be argued in the context of widening participation that many non-dyslexic students from non-traditional educational or socio-economic backgrounds also face complex social-learning needs that can impact on their engagement with their studies at university. Although not analysed in detail in this current study, it has been noted that a small proportion of students in the non-dyslexic group presented levels of dyslexia-ness that approached the boundary Dyslexia Index value of Dx > 592.5 that was used to discriminate students in this group into the quasi-dyslexic, Test subgroup. A deeper analysis of which dyslexia dimensions accounted for placing these non-dyslexic students in this grey, ‘in between’ area of ‘partial’ dyslexia could be the topic for a further study, especially if these dimensions could be tested against students from the typically non-traditional groups that Fraser speaks of. Further mention of this idea of partial dyslexia is discussed below (sub-section 5.2(1)).

 

Also worthy of comment is the outcome of a study that was interested in how adults more generally constructed personal identities, and the extent to which these are positioned within discourses of disability, or of individual difference (Thompson et al., 2015). In analysing the themes that emerged from contributions to an online dyslexia support forum, Thompson and colleagues established that significantly, the majority of contributors indicated a greater alliance with the perception of dyslexia as differences in ability than with disability, despite the finding that many felt encumbered by an identity of dyslexia as a disability in educational contexts (ibid, p1328). The authors were able to establish that three distinct identity personae were identifiable: Firstly, that of being learning-disabled, where the dyslexia was focused on impairments and deficits. Secondly, of being differently-enabled, in which dyslexic individuals were able to focus on their strengths and celebrate their alternative ways of thinking and learning as an asset rather than as a liability.This is a construction that draws much from the idea that dyslexia is an example of natural neurodiversity, a point strongly supported by Cooper and Pollack amongst others (discussed above in sub-section 2.1(I)). Finally, as a dyslexia-identity construction that was rooted in social-disablement, where individuals felt disabled by the ways in which they conceptualized perceived, disabling factors into barriers which prevented them from conforming to the aspirations of a society which focuses on literacy as a marker of ability, achievement and normality.

 

In summary, this analysis indicated that there is a likelihood that the means by which dyslexic students are informed about their dyslexia may be a contributing factor to a measurable impact on their ABC, and hence their academic confidence about approaching their studies at university. It is conceded that with only 64 students in total, the sample sizes of the subgroups that were established are small, but given the consistent differences in favour of students who have NOT had their dyslexia diagnosed, the outcome does suggest that a further study might be warranted, not least to tease out students' perceptions of the meaning of diagnosis in relation to their dyslexia. What seems clear is that the manner in which individuals make sense of their dyslexia and internalize its meaning to them into their academic self-identity is an interesting, relevant and relatively under-researched area, especially in HE settings.

 

Thus the outcomes makes an important contribution to the overall argument in this thesis that a greater effort needs to be made firstly to recognize dyslexia - in whatever ways it can be defined - as a difference rather than as a disability; secondly that for definitions of the syndrome and the labelling of individuals with dyslexia to remain apposite in educational contexts, care must be taken about how this dyslexic label is communicated to individuals concerned; and lastly, that were learning environments designed and structured in more genuinely inclusive ways, the impact of such learning differences on academic confidence would be further reduced, with the counterpoint that learning quality and hence achievement is likely to be enhanced for students whose learning styles, needs and preferences are atypical.

 

5.2  Dyslexia Index

I      Distributions of Dyslexia Index

 

Close inspection of the normal distribution curves (represented in Figure 14, sub-section 4.3(III)) showed that the upper tail of the curve for research group ND overlapped with the lower tail of research group DI, indicating that there were a considerable number of participants in both research groups whose Dyslexia Index values placed them in a mid-range position - between the upper confidence interval limit of RG:ND and the lower limit of RG:DI. At least three explanations may account for this: Firstly, this feature may be indicating that there are students in research group DI whose Dyslexia Index is suggesting that their dyslexia may have been mis-identified; secondly, that there are students in research group ND who are showing some indications of dyslexia-ness as determined by the criteria of the Dyslexia Index Profiler; or lastly, that this variation in both research groups is naturally occurring, or contains too small a number of participants for meaningful conclusions to be drawn.

 

Nevertheless, displaying the distributions in this way demonstrates the disparity in Dyslexia Index between the research groups ND and DI, and that the Dx Profiler was differentiating levels of dyslexia-ness in the way it was designed to. But the most important feature to note is that for the datapool in this enquiry (n=166), the long upper tails to both confidence interval estimates shown in the normal distribution charts (Figure 14) especially demonstrate that there are a number of participants in the non-dyslexic research group ND who presented substantially higher levels of dyslexia-ness than the majority of their non-dyslexic peers. Indeed, with the upper range limits of distributions for both research groups ND and DI at values within a point or two of each other in the low 900s, this is strong evidence to suggest that the Dyslexia Index Profiler is identifying students from amongst those who had not declared any dyslexic learning challenges but who appear to be presenting levels of dyslexia-ness in line with the substantial proportion of their dyslexic peers. This further demonstrates that the Dx Profiler is likely to have correctly identified the Test subgroup of students whose academic confidence could be tested against both the Control and the Base subgroups.

 

Setting this aside for the moment however, this may also be suggesting that the Dx Profiler could be developed for use in HE contexts as a dyslexia screening tool that is more neutrally-nuanced in comparison to others that are constructed on the basis of evaluating deficits. Such a development would resonate with the needs expressed by Chanock and colleagues (2010) together with others who argue for alternative forms of profile assessment to support students at university who present dyslexia or dyslexia-like characteristics (Casale, 2006; Harkin et al., 2015).

 

Furthermore, in this current study there has been merit in dissecting the Dyslexia Index metric into factors to enable comparisons to be made between students in each subgroup at this level. Of interest has been the outcome of the analysis of Dx Factor means where the summaries (Tables 16a, -b, -c, 17, sub-section 4.4(I)) generally confirmed the validity of the Dx metric as a discriminator by gauging the levels of dyslexia-ness that were presented on average by students in each of the subgroups. Specifically, it was shown that the quasi-dyslexic students in the Test subgroup did indeed present similar levels of dyslexia-ness to their dyslexic peers across the range of factors. This comparison also highlighted the particularly high levels of dyslexia-ness amongst the dyslexic students in Dx Factor 1, Reading, Writing, Spelling, which was to have been expected, although it was surprising that there existed a substantial difference between the Dx Factor 1 means between the Control and the Test subgroups. This may be accounted for by the small sample size of the Test subgroup, but it could also indicate that students with quasi-dyslexia, were less troubled by literacy challenges than their dyslexia-identified peers, not least because attention to them had not been formally identified through diagnostic testing against norm references.

 

However, of greater interest was the similarity between the factor mean values for Dx Factor 3, Organization and Time-management which suggests that students at university who present very low levels of dyslexia-ness overall (represented by the Base subgroup), may be experiencing similar issues with organization and time management in their studies to their dyslexia-identified, and quasi-dyslexic peers. In their study of students with dyslexia at university, Mortimore & Crozier (2006) drew on prior research (Gilroy & Miles, 1996; McLaughlin et al., 1994) to support their own data and to evidence the difficulties experienced by dyslexic students in organizing their study processes and time-keeping. Whilst the outcomes of their study were consistent with the earlier research cited, their enquiry was conducted amongst students with dyslexia only, and did not appear to consider how the organization and time-keeping aspect of academic learning management of their respondents might be referenced against students with no reported dyslexia.

 

The data summary presented in this current study fills this gap and shows that in addition to being consistent with the findings of Mortimore and Crozier's study amongst students with dyslexia - as demonstrated by the Dx Factor mean of Dx = 615.72 for the dyslexic students and of Dx = 635.53 for the quasi-dyslexic students - also shows that according to this metric's results and analysis, students who are strongly non-dyslexic in other areas may be just as 'dyslexic' in organizational and time-management skills at university as students with dyslexia, as demonstrated by the Dx Factor 3 means of Dx=568.78 for students in the Base subgroup. This suggests that university students who tend to be disorganized and find time-management challenging are widespread across the learning community, and that this aspect of academic learning management may not be unique to students with learning differences.

 

An institutional response to this could be that if universities are motivated to ensure that all students across their learning communities become properly equipped to meet the learning challenges that they will be facing, not least as a factor to help avoid attrition, then making early provision for upskilling students' organizational and time management competencies as part of the groundwork for enabling them to develop their academic learning management capabilities would be time well-spent. The Dyslexia Index Profiler was developed as a discriminator rather than an identifier; however it is showing merit as a screening tool for dyslexia when dyslexia in HE settings is framed in terms of parameters of academic learning management and study-skills. More so, it may show promise, with development, as an appraisal device in the toolkit for learning development and academic skills support services at university. This is because it presents a readily comprehensible snapshot of any individual student's approach to study by generating a profile identifying strengths that can be developed as well as weaknesses that might be remediated.

 
 

II     Explaining differences in Dyslexia Index at a dimensional level

 

Differences in means between the Base and Control subgroups, and between the Base and Test subgroups in Dx Factor 4: Verbalizing and Scoping, show that for Dx Dimension 14, I prefer looking at the big picture rather than focusing on details, a moderate-to-large effect size together with the t-test output indicate significant differences. This may be evidence to support the viewpoint that dyslexic students are likely to be academically more comfortable adopting planning strategies which permit a more holistic overview to be taken when approaching an assignment challenge rather than plan in lists or other linear-thinking ways (Draffen et al., 2007). Hence the widely adopted feature of UK Disabled Students' Allowance provision of concept-mapping assistive technologies such as the applications 'Inspiration' (Inspiration Software Inc, 2018) and 'Mind Genius' (MindGenius Ltd, 2018). Both of these software tools are designed to foster creative thinking, to facilitate ideas-brainstorming and pattern-spotting, and to enable the grass-hopper thinking of many dyslexic students to be developed into meaningful learning from which powerful knowledge structures can be built, ordered and converted into a linear writing process (Canas & Novak, 2010).

 

Evidence has also shown that concept-mapping applications as learning technologies as opposed to assistive technologies are gaining traction in curriculum design, both as an additional and accessible learning tool (Nesbit & Adescope, 2006), as a mechanism for summative assessment (Anghel et al., 2010) and not least in HE contexts as a means to promote flexible learning approaches (Goldrick et al., 2014), all of which are the embodiment of UDL. Additionally, and of high relevance to students presenting weak spelling competencies, whether attributed to a dyslexia or not, is evidence from studies concerning TEFL (Teaching English as a Foreign Language) learners. Here, concept-mapping applications have been very successfully used to develop English-language spelling skills by enabling spoken phonemes to be connected with their written forms in a highly innovative and relationship-building format (Al-Jarf, 2011) and for connecting vocabulary to concepts in different contexts (Betancur & King, 2014). Furthermore, in Dx Factor 4, Verbalizing and Scoping, even more striking differences between dyslexic and non-dyslexic students for verbalizing ideas in preference to writing about them (Dimension 04) are evidenced – a not unsurprising outcome - where the Dyslexia Index dimension mean value of Dx=84.34 for the strongly dyslexic students in the Control subgroup contrasts sharply with the mean value of Dx=42.34 for the strongly non-dyslexic students in the Base subgroup.

 

Notable differences which emerge from the data for Dimension 02, ‘My spelling is generally good (weak)’, Dimension 17, ‘I get my 'lefts' and 'rights' easily mixed up’, and Dimension 10, ‘In my writing at school, I often mixed up letters that looked similar’: Students in the Control subgroup present substantially higher Dx mean values than for their peers in the Test subgroup in Dimension 02: Control: Dx=75.45, Test: Dx=49.17; Dimension 17: Control Dx=75.28, Test Dx=57.78; Dimension 10: Control Dx=67.17, Test Dx=45.33). For Dimension 02, relating to quality of spelling, this appears to be indicating that weak spelling is not a characteristic of the quasi-dyslexic students in the Test subgroup where in most other respects, these students are presenting dimensional levels of dyslexia-ness that are on a par with their identified dyslexic peers. This may explain why students in the Test subgroup who may be unidentified dyslexics have not had their dyslexia previously spotted, and may also suggest that these students have developed their own strategies for dealing with spelling challenges which have been largely successful.

 

The apparent link between Dimensions 17 and 10, mixing up ‘lefts’ and ‘rights’ and mixing up similar letters may be explained because typically confused letters such as ‘b’ and ‘d’ or ‘p’ and ‘q’ present reflective symmetry, as does left-ness and right-ness, the commonality of which may suggest that neurological disfunction in processing both share a common root. In reading, these static reversals are reportedly often considered as indications of dyslexia in learning to read in the early years. Although they occur in non-dyslexic early readers too, they tend to be more persistent in those with dyslexia (Willows & Terepocki, 1993). Findings are inconclusive, however, as some studies have produced contradictory results (Grosser & Tzeciak, 1981; Corballis, et. al., 1985). In left-right confusion, it is possible to trace the suggestion that this is more prevalent amongst dyslexic individuals than others to the seminal work of Orton in the early part of the last century. This argued that the two sides of the brain would code spatial information with opposite space-reflection symmetry and that this was dysfunctional to some degree or another in dyslexic individuals (e.g.: Orton, 1937). That there is a neurological explanation that associates letter reversals and left-right confusion seems without doubt (Corballis, 2018), and although the exact linkage remains elusive, as advances in technology reveal more about the physiology of the brain and how it reacts to inputs and stimuli, it seems likely that a better understanding of this association will emerge. The data produced in this study for these two phenomena in Dyslexia Dimensions 17 and 10 do at least support this association but it is beyond the scope of this thesis to explore the neurological elements of this further.

 

Looking across the complete set of dyslexia dimensions, the outcomes that emerge when the Test and the Control subgroups are compared show that in all but 4 of the 20 dyslexia dimensions, the mean values for each of the dimensions respectively are very similar which is supported by generally small effect size differences and p-values which indicate no significant differences between the means. This outcome is suggesting that the students in the Test subgroup who are presenting dyslexia-like profiles are indeed dyslexic within the terms of reference of the Dyslexia Index Profiler. This adds to the construct validity of the Dyslexia Index metric as a mechanism for discriminating students who may be dyslexic amongst the research group of students who declared no dyslexia. Thus confidence is gained in using the measure as an index of a construct that is not directly observable (Weston & Rosenthall, 2003), which in this project, is termed 'dyslexia-ness'. Smith (2005) summarizes the seminal work of Cronbach and Meehl (1955) on construct validity which comprehensively argues “that the only way to determine whether a measure reflects a construct validly is to test whether scores on the measure conform to a theory, of which the target construct is a part” (op cit, p405).

 

Hence, it is argued that by exploring the contrasts in Dx Index values at a dimensional level, and commenting on the extent to which the differences that have been measured are in keeping with the more widely accepted theoretical underpinnings of at least some of the typically observed characteristics of dyslexia, the construct validity of the Dyslexia Index Profiler is strengthened and justified as the discriminator for which it was designed in this current study.

 

5.3  Academic Behavioural Confidence

 

I      Reflecting on the Principal Component Analysis of the ABC Scale

 

Much has been drawn from the statistical rigour that Sander and Sanders have demonstrated to justify the robustness of their ABC Scale. For example, Sander and Sanders claim that the criterion validity of the ABC Scale is enhanced through their factor analysis procedure and the subsequent reduction into a 17-item scale. Criterion validity is presumed to refer to predictive criterion validity, although with the current data at least, given the negligible variation between effect size differences when using either the 17-item or the 24-item scale, it is not possible to argue the same point.

 

The metric is gaining a reputation as a well-proven and valid scale for exploring various aspect of academic confidence amongst university students, (e.g.: Nicholson et al., 2013; Matoti & Junquiera, 2009; Hlalele, 2010; Taylor & House, 2010; Stevenson, 2010; Matoti, 2011; Chester et al., 2010; Willis, 2010; Chester et al., 2011; Wesson & Derrer-Rendall, 2011; Hlalele & Alexander, 2011; Keinhuis et al., 2011; Aguila Ochoa & Sander, 2012; Hlalele, 2012; Kienhuis, 2013; Putwain et al., 2013; de la Fuente et al., 2014; Takahashi & Takahashi, 2015; Marek et al., 2015; Sanders et al., 2016; Braithwaite & Corr, 2016), a review of which has been presented earlier in Section 2. Thus, it is being used in this current study without hesitation as the best metric available for exploring the issues being considered.

 

However, there are notable differences in student demographics between the Sander and Sanders studies and the cross-section of participants in this current study. For example, in this research, students from across the university community were invited to participate, with the participation response producing an overall ratio between undergraduates and other students of 75% : 25%; ('undergraduates' included students attending foundation or access courses and 'other students' comprised post-graduates, research students and a very small number of others (n=3) who did not disclose their study level). In the Sander and Sanders studies, students were all undergraduates, and from a broadly similar family of academic disciplines. This may be due to convenience sampling rather than through any specific research design intention, and does not necessarily reflect on the quality of the data collected only the limitations.

 

Furthermore, in the Sander and Sanders' datasets, students were drawn from a narrow range of subject specialisms, a consequence of which could be that results obtained may lack generalizability across the wider student community, because some components of study skillsets might reasonably be expected to differ according to academic discipline being studied. Whereas in this current study, subjects studied were not recorded, and so it is not unreasonable to assume that students from a range of curriculum specialisms are as likely to have participated as not. Thus, analysis outcomes of the constructs being explored can be considered as a good cross-sectional representation from across the student community.

 

One limitation however, was that data were acquired largely from just one institution which although does not imply any degradation of data quality, only that caution should be adopted if results are to be generalized to HE contexts more widely. Hence, to ensure that analysis of the ABC Scale's output is robust in this current study, rather than rely on the existing factor structure developed by Sander and Sanders, especially in the light of differences between their student demographics and the participants in this current study, it was considered that there were reasonable grounds for generating a unique factor structure from the current data to compare with the existing factor structures of both the ABC24- and ABC17-item scales. If a substantially different structure emerges, then it would preferable to use it for integrating with the factor structure of Dyslexia Index Profiler for looking for evidence to address the original research hypotheses, rather than rely on existing ABC Scale factor structures. In this current project, no data were collected about students' subject specialisms nor their levels of study, for example.

 

Thus, interpreting data analysis outputs from this current study’s more broad-based source using its own ABC factor structure was considered more legitimate than using either the existing ABC24 Scale's 6-factor, or the ABC17 Scale's 4-factor structures. This cautious approach is a response to the need for data analysis processes to be as relevant and applicable as possible. But it is also a consequence of earlier attention drawn (sub-section 2.1(VII)) to an example of the reportedly disappointing effectiveness of a construct-evaluating metric developed from a closed cohort sample at a single university, when used to explore the same construct as presented in a sample taken from a different university's student community (the YAA Adult Dyslexia Scale; (Hatcher & Snowling, 2002)). In that case, the YAA-R was adapted for use in an Australian university with disappointing results (Chanock et al., 2010).

 

Chanock highlighted the limitations of the YAA due to its development being based entirely on data collected from a single source, arguing that this reduced its adaptability for use in outwardly similar contexts but where, in this case, significant differences in test-subject demographics appeared sufficient to upset the results. It is reasonable to suppose that Corkery et al. (2011) followed a similar line of reasoning to justify applying PCA to the local data in their study. The factors which emerged showed differences in comparison to both the Sander and Sanders factor 24-scale-item and the 17-scale-item ABC Scales’ factor structures, and indeed to the factor loadings and subscales which emerged from the PCA on the data in this current study. Hence it was considered that precedent had been set for applying PCA to the data of a research project, not least for comparing the resulting factor loadings and subscales with outputs generated from the existing ABC subscales. It could be argued therefore, that this does raise an issue about the stability and hence the generalizability of ABC factors.

 

Furthermore, researchers choosing to use the metric in their studies may be wise to explore the factor structure of the ABC Scale in relation to their local data, unless it could be shown that the demographics of their participant cohorts closely resemble those of Sander and Sanders' original (combined) studies.

 
 

II     ABC Factor structure in this current study

 

As reported earlier (sub-section 4.4(II)), the output of the PCA of the data collected using the ABC 24-item Scale in this current study, produced five factors that were labelled in accordance with the themes of the scale items which comprised them (see sub-section 4.4(II) for the complete list of dimensions in each factor):

  1. Study Efficacy

  2. Engagement

  3. Academic Output

  4. Attendance

  5. Debating

The dimensional compositions of each of these factors are notably different to the PCA results of both the ABC24 and the ABC17 Scales conducted in the Sander and Sanders studies. Hence the decision to apply PCA to this current data is considered as justified.

 

III    Factor loadings – understanding these for this current data

 

The Rotated Component Matrix for the ABC 24 item scale together with the Total Variance Explained, shows that the strongest influences to ABC overall, appears to be attributable to Study Efficacy, and Engagement, and to a lesser extent, Academic Output. Given the foundations of the ABC Scale being firmly rooted in Bandura's Social Cognitive Theory and all it says about self-efficacy, where it has been demonstrated that mastery experience is one of the key contributors, it is pleasing to note that these three factors are strongly indicative of the relationship between academic confidence and academic learning management processes, success in which might be argued as strong evidence of a student's academic mastery development.

 

But the factor loadings shown in the rotated component matrix also indicate the importance that developing strong academic writing styles has on academic confidence with a factor loading of 0.819 being the highest of all 24 loadings (ABC Scale item 116). The impact of this is that students who present high levels of dyslexia-ness will continue to be disadvantaged when academic outputs based on writing are the principal format for gauging academic capabilities. While education systems remain steadfastly rooted in literacy competencies, this remains the status quo, and so students with a dyslexia that has not been strategically ameliorated, whether unknowingly or through learning support and development, will continue to be challenged when asked to demonstrate their knowledge, typically, by writing an essay. This is an important point and revisits the earlier argument in support of Universal Design for Learning where access to learning becomes more adaptable to learner needs, and less constrained by conventional and traditional processes for the transmission of knowledge and the expression of ideas.

 

IV    ABC means - Differences between the research groups and subgroups

 

Following from the summary of key outcomes (Table 21, sub-section 4.5) and how these relate to the research questions and hypotheses (sub-section 2.3), some notable points emerge: Firstly, the summary table shows clear evidence that not only students with dyslexia present statistically significantly lower mean levels of ABC than their non-dyslexic peers, a finding which is amplified when looking at a similar comparison between the strongly dyslexic students in the Control subgroup and the strongly non-dyslexic students in the Base subgroup, but also that students who are strongly dyslexic present lower mean levels of ABC than their strongly quasi-dyslexic peers. Given that it has been shown earlier that these two subgroups present (statistically) very similar levels of dyslexia-ness, this appears to be supporting the conjecture of this current study that identifying dyslexia in students may impact negatively on their academic confidence in their studies at university.

 

Secondly, by looking at the outcomes on a factor-by-factor, basis it is notable that the two ABC factors which appear to have the greatest influence on the differences in ABC mean values between the Test and the Control subgroups of quasi-dyslexic and dyslexic students respectively, are Factor 2, Engagement, and Factor 3, Academic Output, with moderate-to-large and moderate effect sizes respectively (g=0.61, g=0.41). Factor 1, Study Efficacy, also appears to be a lesser, contributory element (g=0.25). It is suggested that again, these data illustrate the challenges faced by students with dyslexia in literacy-based education systems, however it is notable that identified dyslexics appear to fare worse than their non-identified, quasi-dyslexic peers.

 

Hence it might be surmised that identifying dyslexia together with the subsequent benefits that this is thought to provide may be a misplaced strategy, and that students who are left to face their academic challenges without a formal explanation of their cause are likely to deal with them more effectively. It was also notable that there were very small differences in absolute mean ABC values in Factor 4, Attendance, between any of the research groups or subgroups. This appears to indicate that levels of attendance at lectures and seminars can be discounted as a factor which may influence the levels of academic confidence of students at university. This may also indicate a converse conclusion that academic confidence does not impact on class attendance although this outcome cannot be specifically tested from these data.

 
 
 

5.4  Blending ABC and Dx outputs

I      Implications for university learning development

 

In a real-world, university-learning context it can argued that gaining a perspective on a student's blend of academic learning management strengths and weakness is useful, and that knowing more about their academic confidence across the spectrum of learning and study behaviours and preferences related to academic study, could be of value. This is recognizing that developing students as learners is part of the university experience, and in conjunction with an obvious responsibility to facilitate the acquisition of knowledge in whatever discipline a student has chosen, is an essential part of a coherent, balanced and meaningful learning journey (Gibson & Myers, 2010).

 

Aligned with this rationale in many universities is an emergent transition from the more traditional role of ‘study skills’ as a remedial service offered largely to weaker or failing students, to learning development as a more comprehensive component of university learning regimes (Samuels, 2013). This is striving to become a service which is not negatively connotated with remediating poor academic performance (Gibbs, 2009) but instead is a positive and desirable component of every student’s learning journey. Hence gaining an appraisal of students’ baseline learning capabilities and approaches to their studies, as a first stage in the process of developing them as learners, seems a reasonable approach to take.

 

By using the two constructs assessed by each of the metrics in this study, where the outputs have been presented not only in terms of aggregated ‘scores’ but also in more detail at the dimensional level, it is suggested that this approach could be developed into a helpful mechanism for enabling university learning development tutors to identify targets for the most appropriate learning counselling and development, both at an individual level and more widely across subject disciplines. The aim would be to mitigate the impact of apparent learning challenges whilst simultaneously offering guidance about how to capitalize on areas of strong competency, not least by empowering learners to be more pro-active in relation to their own, individual learning styles and processes. That is, to enhance their capabilities to self-regulate their learning, the features of which have been discussed in detail in Section 2. This would be to encourage students to enhance their metacognitive and metalearning awareness, and also to enable them to reflect on their own study routines. But especially to explore how some of these may be modified to mitigate a variety of affective responses to the challenges of study that they may come to realize are inhibiting their academic performance.


To support this argument, and in addition to the Dx Factor-based profile that was reported for the case study student (respondent #63726872, sub-section 4.6(I)), a more detailed profile map which shows every Dyslexia Index dimension collectively in the form of a 'rose' chart was generated. This was produced in parallel with a similar rose chart for that student's blend of actions, plans and behaviours related to their academic study as revealed by their self-report output on the ABC Scale. (Figure 25, below).

Fig25.png

Figure 25:   Rose chart profiles of Dx dimensions and ABC dimensions for student respondent #63726872, representing the median Dx point of the subgroup of quasi-dyslexic respondents.

To contextualize the case study student’s rose chart profiles, two further students’ datasets were selected, one from the Base subgroup of strongly non-dyslexic students (#65118727) and another from the Control subgroup of students presenting high levels of dyslexia-ness (#17465316) and the corresponding profile rose charts were constructed (Figures 26,27). These datasets were also at the Dyslexia Index median point of their respective subgroups, thus maintaining procedural consistency of selection with the case study student. The rose charts display dimensional levels of dyslexia-ness and academic confidence respectively, and for both metrics, the dimensions are grouped and colour-coded to indicate their parent factors. The scale for each chart radiates from 0% at the centre, to 100% at the circumference indicating levels of dyslexia-ness and of academic confidence respectively. Table 29 shows the Dx overall and Dx Factor data for all three sample students, with Table 30 presenting the corresponding data for academic confidence.

Fig26.png

Figure 26:   Rose chart profiles of Dx dimensions and ABC dimensions for student respondent #17465316, representing the median Dx point of the subgroup of strongly dyslexic respondents.

Fig27.png

Figure 27:   Rose chart profiles of Dx dimensions and ABC dimensions for student respondent #65118727, representing the median Dx point of the subgroup of strongly non-dyslexic respondents.

Table29_edited.png

Table 28:    Comparing Dx overall and Dx Factor values for respondents representing the Test, Control and Base subgroups.

Table30_edited.png

Table 29:    Comparing ABC overall and ABC Factor values for the same respondents

It can be seen both from Tables 29, 30 and Figures 25, 26, that the student identified with quasi-dyslexia in the Test subgroup presented broadly similar dyslexia-ness characteristics to the student with identified dyslexia in the Control subgroup, but presented higher levels of academic confidence in most of the ABC dimensions. This provides exemplar evidence to support the premise of this thesis that students with dyslexia may be best left in ignorance of the fact to avoid a detrimental impact on their academic confidence. Indeed, it is notable that the rose chart of ABC dimensions of the quasi-dyslexic student displays greater similarity to that of the non-dyslexic student (Figure 27), whose levels of dyslexia-ness in all but three dimensions are at low levels.

 

It is apparent that these three students present different blends of strengths and weakness in academic learning management and dyslexia-ness characteristics with not only contrasts, but also similarities being clearly visible. The greater point is that these data and charts present firstly, a comprehensive baseline from which learning development interventions might be formulated at an individual level to build bespoke ‘learning plans’ that each of these students could use to guide their studies at university. But also, by exploring themes and trends revealed by interpreting data collected more widely from the student community, it would be possible to develop focused, institution-wide learning development programmes aimed at enhancing the learning potential and likely academic achievement for all students, not least by embedding learning development as a core component of university learning that operates in an integrated way with the academic curriculum (Hill & Tinker, 2013).

 

It is suggested that these kinds of analyses and data presentations designed to reveal where learning blockages may be present, and hence ameliorated, and where strengths can positively enhanced, merits further study and development. To date, no studies have been found which use a factorial analysis of a dyslexia evaluator in HE settings as an independent variable correlator for exploring another construct, in the case of this project, academic confidence; or to show that the combinations of these aspects of metacognitive profiling in university students may have been considered as a means to gain a better understanding of the ways in which students engage with their studies. However it is true to say, as reported in the literature review earlier (sub-section 2.1(II)), that examining dyslexia at a factorial level has been gaining traction in recent research. For example, a recent study (n=154) conducted with students at the University of Amsterdam demonstrated nine distinguishable factors of dyslexia. These were classified as: Spelling, Reading, Rapid Naming, Attention, Short-Term Memory, Confusion, Phonology, Complexity, and Learning English (Tamboer et al., 2017).

 

This paper is not only an example of a study that has explored dyslexia at a factorial level, and hence sets a precedent for this research approach, but also because the cohort of research participants closely resembles those in this current study. This is because it comprised not only known dyslexic students, as well as those who were clearly presenting no indications of dyslexia as determined by any of the conventional criteria, but also, a significant subgroup of ‘maybe-dyslexics’ emerged out of the analysis. The outcomes of the study have a bearing on the factorial analysis outcomes in this current study due to the similarities of the factors and the process by which they were established. This is despite Tamboer’s and colleagues’ main interest, which was in determining the predictive validity of a newly-developed screening test for dyslexia in the Dutch language. Their research suggested that this validity was strong, leading to the conclusion that it would be useful as a dyslexia identifier in HE contexts. Of particular interest was that the self-report questions that had been included in their data collection instrument also returned high construct validity, and significantly, an even higher predictive validity than the other tests that had been included in the screener (ibid). It might be reasonably argued that the Dyslexia Index Profiler developed for this current study demonstrates a similar approach, and may also warrant further development as a dyslexia identifier in university settings, should this be considered valuable.

 
 
 
 
 
 
 

II     Explaining ABC differences in relation to Dyslexia Index

 

The results in the Factor Matrix are rich in significance and implication (Table 26, sub-section 4.6(I)). Overall, they add depth to the evidence presented to reject both Null Hypotheses presented earlier (sub-section 2.3) and support the principle tenet of this current study, arguing that students at university with unidentified dyslexia, quasi-dyslexia or elements of dyslexia, may be best left in ignorance of the fact. This is because to advise that they consider taking a dyslexia screening test, possibly leading to a full dyslexia assessment which may subsequently indicate that they have a dyslexic learning difference, might adversely affect their academic confidence, their academic self-efficacy and perhaps their academic achievement. Thus, the results presented so far are important, and may be controversial, not least because the current convention is grounded in the belief that identifying dyslexia in students is fundamental to providing access to specialist study support to aid their learning in environments that they may find challenging. The outcomes presented in this current suggest that this belief may be misplaced. 

 

Finding meaning from the results in the Factor Matrix

Interpretative Phenomenological Analysis?

 

To make sense of the results presented in the Factor Matrix, qualitative data provided in questionnaire responses have been used to enrich the discussion which follows. It was considered that applying an Interpretative Phenomenological Analysis (IPA) to this data may be an appropriate procedure, as IPA is typically used to explore, interpret and understand a phenomenon in people - dyslexia in students in this current study - from the perspectives of the lived-experiences of the individuals of interest (Reid et al., 2005). Hence IPA could be relevant to aspects of this current study in that there is interest in understanding how students with dyslexia make sense of their learning and study experiences at university, and how they attach meaning to the life events that occur in this context (Smith et al., 2009).

 

Of particular interest has been trying to understand the ways that such students perceive how their dyslexia impacts on their academic confidence. However, although IPA attempts to uncover themes in qualitative data, it is generally conducted with small, purposive samples of typically fewer than ten participants (Hefferon & Gil-Rodriguez, 2011), and that there is always some danger of the analysis being merely descriptive rather than more deeply interpretive (ibid). Hence in this current study, although some of the analysis ideas formulated in IPA are utilized – for example in identifying thematic narratives as a means to support the quantitative outcomes of the data analysis, since this qualitative data is being drawn from the complete datapool (n=166) rather than by selecting a small, representative sample, adopting a more formal IPA process was considered inappropriate.

 

Exploring ABC differences on an ABC factor-by-factor basis

According to Dx Overall:

 

The results at the foot of Table 26 present effect size differences and t-test outcomes in ABC Factors for the three research subgroups Base, Test, and Control, according to overall Dyslexia Index values. The contributions made to the overall ABC24 effect size differences and t-test outcomes by each of the five ABC Factors are shown, and from this an interesting picture emerges: Firstly, it can be seen that very little, if any contribution is made by the two ABC Factors 4, Attendance, and 5, Debating, with small or negligible effect sizes between the Test and the Control subgroups and between the Control and the Base subgroups. This seems to be indicating that dyslexia, quasi-dyslexia or non-dyslexia makes little difference to students' academic confidence in relation to their attendance regimes, and the ways in which they interact academically with their peers, and with their teachers in one-one settings.

 

This is not to say that there are no differences in attendance regimes and peer, and lecturer interactions between dyslexic, quasi-dyslexic or non-dyslexic students, it is indicating that academic confidence differences do not appear to be impacted by such differences in students’ regimes and interactions. Whereas the greatest contributions to the overall effect size come firstly from ABC Factor 2, Engagement, where an effect size of g=0.61 is supported by a t-test outcome revealing that the mean ABC24-2 of the Test subgroup is significantly higher than for the Control subgroup and secondly, from ABC Factor 3, Academic Output (g=0.41), where although the t-test does not return a significant result it could be considered as ‘marginal’, the effect size was ‘moderate’.

ABC24-2, Engagement, is concerned with the processes and action-activities of study, and includes such dimensions as following themes and debates and asking questions in lectures, and 'presenting' to student peers.

 

This moderate to large effect size between ABC24-2 values for the Test and the Control subgroups suggests a substantial difference in academic confidence between dyslexic and quasi-dyslexic students for this factor. If we are to take 'academic confidence' in this context as a reflection of self-confidence in academic contexts, then we can identify this difference as possibly marking how self-appraisal of efficacy can be strongly influenced by social comparisons (Bandura, 1997b). This may be particularly significant for students with dyslexia who feel socially stigmatized as a consequence of internalizing their learning difference as a disability that is perceived negatively in peer comparison situations (Murphy, 2009; Dykes, 2008). Hence such students may be less likely to participate and engage in study action-activities as this could lead to their dyslexia being revealed to their classmates. One respondent typifies this obfuscation of dyslexia:

  • "I don't like feeling different because people start treating you differently if they know you have dyslexia and normally they don't want to work with you because of this ... I don't speak in class because I am not very confident at answering questions in case I get them wrong and people laugh" (Respondent #85897154; RG:DI; ABC = 47.3; Dx = 797.89).

 

Similar examples of reticence in voicing opinions in the company of peers, and clear feelings of social disenfranchisement, were evidenced in a recent study exploring feelings and attitudes to university study of a group of students with dyslexia similar to those in this current study: “… all participants talked about wanting to take part but often choosing not to, because the risk of looking stupid or incompetent was too great” (Cameron, 2016, p231).

 

Another respondent from the dyslexic group demonstrated the lasting impact of feelings of difference stemming from experiences in earlier schooling:

  • "I do have to battle with elements of doubt ... particularly influenced by bullying at primary and secondary school to do with 'stupidity' and 'slowness' and my seemingly unrelated comments to topics at the time" (Respondent #87564798; RG:DI; ABC = 49.2; Dx = 751.23)

 

although some students with dyslexia clearly benefit from an inner strength that can enable them to mitigate earlier ridicule and build sufficient levels of confidence to tackle university study:

  • "When I was at school I was told that I had dyslexia; when I told them I wanted to be a nurse they laughed at me and said I would not achieve this and I was best off getting a job in a supermarket. Here I am now, doing nursing!" (Respondent #48997796; RG:DI; ABC24 = 84.6; Dx = 835.65)

 

The large effect size of g=1.19 between the Base and the Control subgroups for this ABC Factor 2 is the second-largest of all the effect size differences between these subgroups and together with the mean ABC Factor 2 values of 66.9 (Base) and 45.8 (Control) is highly indicative of the differences between the academic confidence of strongly non-dyslexic and strongly dyslexic students in action-activities in study such as engaging with lecturers or presenting work or ideas to small groups of peers.

ABC24-3, Academic Output, encompasses academic performance, including dimensions such as writing in an appropriate style, attaining good grades and producing good quality coursework. A moderate effect size of g=0.41 between the Test and the Control subgroups also indicates a measurable difference between the academic confidence of quasi-dyslexic and dyslexic students for this ABC Factor. Although at face value this also appears to be indicating that students with identified dyslexia present lower levels of academic confidence in relation to performing at a good standard academically in comparison to their quasi-dyslexic peers, without controlling for other variables such as academic aptitude or legacies from prior academic history and attainment, this outcome is viewed cautiously.

 

However a significant difference between the ABC Factor 3 values of the Test subgroup and the Base group is suggested by a large effect size (g=1.15) with ABC24-3 values of 59.9 (Test) and 79.9 (Base) (t(81)=5.56, p<0.001; not shown in Table 26). Given the sample sizes of these subgroups of n=47 (Test) and n=44 (Base) being respectable, this is a strong indication that for students with dyslexia, their academic confidence in relation to their academic performance outcomes is strongly depressed in comparison to their non-dyslexic peers. This may be related to attitude towards tackling difficult work where high standards are expected. One respondent from the non-dyslexic group wrote:

  • "As soon as I get a piece of coursework I try to get it done to a high standard ... Overall I don't think I pick things up quick[ly]. I'm more of a hard worker than a natural learner. Some of my friends can interpret data straight away whereas I have to take my time to understand it" (Respondent #60017207; RG:ND; ABC24 = 90.2; Dx = 466.90)

 

which also indicates that this student may have not only developed a good work-study ethic, but also appears to evidence an understanding of his own meta-learning processes and displays the academic confidence to apply them.

According to Dyslexia Index Factor 1: Reading, writing, spelling

In sifting the datapool according to Dx Factor 1, Reading, Writing, Spelling, the Dyslexia Index Profiler appears to offer a concurrent identification of these conventionally accepted aspects of dyslexia to other dyslexia identifiers, this being indicated by 79% of participants in research group DI returning substantive levels of dyslexia-ness (Dx > 592.5) on dimensions that constitute this factor in the Dx Profiler. However by considering how this Dx Factor distributes students in research group ND, it is notable that nearly twice as many would be categorized with levels of dyslexia-ness that would sift them into the Test research subgroup were this the only criteria, in comparison to the number sifted into this research subgroup according to the overall Dyslexia Index value.

 

This appears to be suggesting two things: firstly that issues with reading, writing and spelling occur quite commonly amongst non-dyslexic students, and secondly, other Dx Factors aside from Factor 1 appear to be making a greater contribution to the overall Dyslexia Index value criteria that sifts apparently non-dyslexic students into the Test subgroup of students presenting high levels of dyslexia-ness – that is, quasi-dyslexia. This outcome may be indicating that the more conventionally-applied, dyslexia screening tools are weighted towards identifying dyslexia through apparent weaknesses in literacy skills because those who do not present such weaknesses, but who are indicated as having significant other challenges in their academic learning management competencies, are not identified as dyslexic. It is also possible that this bias towards identifying deficits in literacy skills is a legacy of child-focused dyslexia identifying processes, where issues in acquiring reading skills in early years learning are well documented as possible indicators of dyslexia. But also that a continued application of this dyslexia-identifying process is less appropriate in tertiary education contexts. Hence this may be adding to the argument suggesting that there is merit in developing the Dyslexia Index Profiler as a dyslexia screening tool in HE contexts.

 

When examining ABC values corresponding to Dx Factor 1, it can be seen that there is a negligible ABC (overall) effect size between the Test and the Control subgroups (g=0.11), corresponding to just a small absolute difference in ABC mean values (58.5, 56.9 respectively). This is suggesting that were dyslexia attributable to only the literacy family of dimensions, only a slight difference in academic confidence may be observable between students who know about their dyslexia, and students who may have an unidentified dyslexia, or may be quasi-dyslexic, or who are simply 'garden variety' poor readers and spellers (Stanovich, 1996, p157). These may be individuals whose literacy difficulties resemble dyslexia but may not be attributable to dyslexia.

 

However the effect size between dyslexic students in the Control subgroup and non-dyslexic students in the Base subgroup is large (g=0.87), which, together with a significant difference between the mean ABC values, indicates that competency in literacy skills is likely to have an important impact on academic confidence. This appears to be adding to the argument that while competency in literacy remains a significant conduit for academic ability to be gauged at university, students who know that they struggle in this area will be further impacted by reduced academic confidence - altogether not an unsurprising deduction, but one which nevertheless spotlights that were curriculum delivery and especially assessment processes broadened to reduce the reliance on literacy skills, courses would become more accessible and inclusive, and those who have alternative, and certainly better-for-them processes for expressing their ideas and communicating their knowledge, would not be so obviously disadvantaged.

 

This conclusion is amplified by the results in ABC Factors 2 and 3 which return similar effect size differences between the Test and the Control subgroups of g=0.31, 0.29 respectively. Although these are smaller, being close to the small-moderate boundary value (g=0.3), and are accompanied by marginal t-test outcomes in both case, the effect sizes between the strongly non-dyslexic students and the strongly dyslexic students for both of these ABC Factors are larger than any other ABC-Factor/Dx-Factor combination (g=1.36, g=1.14 respectively). Hence in taking the three, ABC Factors (1,2,3) which are most indicative the academic learning management skills and competencies of students’ study processes, there is clear evidence that weaknesses in literacy skills may have a critical impact on academic confidence.

 

For ABC Factor 4, Attendance, we see a small effect size of -0.24 between the Test and the Control subgroups. ABC Factor 4 is an evaluation of study behaviours relating to attending lectures and being on time for them, and attending tutorials. This result seems to be indicating that the quasi-dyslexic students in this datapool may be the least diligent in attending their classes. Through this Dx Factor sifting process, these students comprise 39% (n=35) of the non-dyslexic research group (n=98). This effect size difference could be an indication that the quasi-dyslexic students in this datapool are indeed unidentified dyslexics, and that these students appear to be presenting a tendency to avoid lectures, classes and tutorials more than their dyslexia-identified peers, perhaps because they find them particularly challenging.

 

Any number of reasons may account for this, but it is possible that these students may be unaware that the learning challenges they experience may be related to an unidentified dyslexia, and hence have not had the opportunity to learn about strategies and coping processes that may help. Their dyslexia-identified peers are likely to have had access to these through targeted study-skills learning development sessions, which equip them with a variety of strategies and study-aid devices and techniques, to enable them to engage more effectively with formal teaching situations, and hence, they are less deterred from attending them. For ABC Factor 5, Debating, the effect size is negligible between the Test and the Control subgroups (-0.07), although it is at the low-moderate boundary between the Base and the Control subgroups (g=0.29). Although this ABC Factor comprises just two dimensions: relating to verbal engagement with peers, and in one-to-one tutorials with lecturers, there may be evidence in this result that is connected to weaker levels of confidence in formulating ideas and making valuable contributions to discussions amongst the students with dyslexia, not least due to fear that these will appear muddled or even incomprehensible to their peers or tutors. Comments from some students in the current study support this conjecture, for example:

  • “I get the words confused in my head … I think of the right word but the wrong one comes out” (Respondent #74355805, RG:DI, ABC=30.63, Dx=699.18);

  • “I do battle with elements of doubt … particularly to do with ‘slowness’ and ‘stupidity’ and my seemingly unrelated comments to topics at the time” (Respondent #87564798, RG:DI, ABC=47.17, Dx = 751.36);

  • “I don’t speak in class because I am not very confident in answering questions in case I get them wrong and people laugh” (Respondent #85897154, RG:ND, ABC=54.83, Dx=797.79).

According to Dyslexia Index Factor 2: Thinking and Processing

 

Sifting the datapool according to Dx Factor 2 reduced the sample size of the Test subgroup to n=16 (from n=18), suggesting 16.3% of the non-dyslexic students are quasi-dyslexic if this criteria alone is the determining attribute. However a similar reduction is also observed in the sample size of the Control subgroup which reduces to n=39 (from n=47) which may be suggesting that the family of dimensions that constitute Dx Factor 2 are less critical in determining dyslexia-ness overall because fewer students are then presenting levels of dyslexia-ness that is above the boundary value of Dx > 592.5. In the complete datapool, these students would then represent 33.1% of the total number of research participants (n=55/166) whereas in applying the boundary value, Dx > 592.5, to each research group - that is, to the group of declared dyslexic students (RG:DI) and to the group of declared non-dyslexic students (RG:ND) - and then combining these, this would represent a total of n=65/166, being 39.2% of the total datapool. Thus if the attribute for determining whether a student is dyslexic or not according to the Dyslexia Index Profiler were solely based on the family of dyslexia dimensions related to thinking and processing, fewer students would be considered as dyslexic, thus indicating that were dyslexia considered as primarily a thinking and processing difference, it may be less prevalent.

 

However for this Dx Factor, it is notable that there is a significant difference between the mean ABC values of the Test and the Control subgroups in ABC Factor 3, Academic Output, with a moderate-to-large effect size of g=0.66; and for ABC Factor 2, Engagement, a marginally non-significant difference in mean ABC values with a moderate effect size of g=0.43. This suggests that even were Thinking and Processing the determining factor of dyslexia, a student who is identified is likely to present substantially lower levels of academic confidence than were they not to be identified. This is in aspects of their studies at university that are part of the academic processes of engaging with academic materials both independently, with their teachers, with their peers, and in assessment processes. A theme which emerged out of respondents' comments suggests that some felt inadequately prepared for independent learning or finding out more about their own learning processes, characteristics that are recognized as desirable in university study, with some observing that tutorial sessions for study or academic skills missed the target. Two respondents from the dyslexic group said respectively:

  • "... universities provide support with tutorials geared at helping the individual with learning but somehow they seem to expect that a person understands what they find difficult ...[but] because they have been living with it their whole lives [they] can't see objectively what is 'wrong' " (Respondent #87564798; RG:DI; ABC = 49.2; Dx = 751.23);

  • "I find independent learning quite difficult and would prefer more in depth help from tutors to give a clear[er] idea of what is accept[able]" (Respondent #17465316; RG:DI; ABC = 56.5; Dx = 719.63);

Another respondent, in this case from the non-dyslexic group said:

  • "Ways that studying at university can be improved is by far, to teach students how to learn. We're always taught the content for a specific subject but has anyone ever taught a student on how to learn?" (Respondent #52289216; RG:ND; ABC = 56.9; Dx = 570.73);

 

These comments support the idea suggested above that gauging students academic competencies across a range of dimensions, both attributable to academic confidence and to dyslexia-ness, could create a firmer foundation upon which to build effectively targeted learning development, by reflecting a lack of progress in how some institutions deal with student pre-conceptions about what it is to study at university and be an independent learner. At an institutional level, this may be a consequence of inadequate responses to the changing academic needs of student communities, particularly as a result of the surge in those now attending universities through widening participation initiatives that aim especially to enrol learners from traditionally poorly-represented backgrounds. For many of these students, the transition to university initiates a conflict in values bringing a challenge to an earlier-established identity, and poses a threat to familiar ways of knowing and doing (Krause, 2006 in Brownlee et al., 2009). Processing information and then thinking about it are rightly considered to be critical components of learning, and if there are now indications that many students attending university feel unprepared for these cognitive demands, this may also be a reflection of the style and structure of their prior learning experiences. In the UK at least, these may have become increasingly reversive towards old learning structures grounded in rote in order to meet demands for greater accountability and in response to institutional academic competitiveness. In tandem with this may be an equally increased dependency on supplementary subject tutoring and exam coaching, where learners are taught how to ‘pass’ rather than encouraged to learn, think and reflect on knowledge gained, a process which is likely to lead to greater social divisiveness and counter social mobility in educational contexts because the coached students are more likely to gain university places (Smith, 2003; Griffin & Hu, 2015).

 

Nevertheless, some recent evidence suggests that targeting interventions at a much earlier stage in learning careers may be a more advisable approach to finding ways to close the socio-economic gap in HE participation so that attainment at Level 1 (GCSE) amongst poorer students is raised to levels that are closer to their less-disadvantaged peers (Vignoles & Murray, 2016), and hence the academic gap is narrowed in advance of entry to university.

 

Thus evidence from the data collected in this project indicates a substantial disparity in academic confidence between dyslexic and non-dyslexic learners in the factors related to engagement, and to academic output, not only overall, but also when the datapool is sifted according to the Dyslexia Index Factor 2 criteria, Thinking and Processing. Very large effect sizes are recorded between the Control and the Base subgroups in these two ABC factors (g=1.09, g=1.12, ABC24-2, -3 respectively) with the differences in absolute mean values being considerable (ABC24-2: 46.6 (Control), 65.3 (Base); ABC24-3: 59.0 (Control), 78.0 (Base)). Also indicated is a moderate effect size between dyslexic and quasi-dyslexic subgroups in overall ABC (g=0.45; ABC24: 57.3 (Control); 64.0 (Test)) with a marginally non-significant difference between the means. This indicates that when the datapool is sifted according to Dx Factor 2, Thinking and Processing, the academic confidence of dyslexic students in the Control subgroup is moderately depressed in comparison to quasi-dyslexic students in the Test subgroup. Furthermore, the academic confidence of dyslexic students in the Control subgroup is substantially depressed in comparison to non-dyslexic students in the Base subgroup (t(70)=4.44, p<0.001; not shown in Table 31), g=0.95 'large'; ABC24: 57.3 (Control); 70.6 (Base)).

 

According to Dyslexia Index Factor 3: Organization and Time Management

 

When the datapool is sifted according to Dx Factor 3, Organization & Time management, the proportion of students in research group ND (those with no declared dyslexia), whose Dyslexia Index value places them into the Test research subgroup, rises substantially from 18% of their parent research group to a new value of 50%. This is stating that half of students with no declared dyslexia are nevertheless presenting levels of dyslexia-ness that are comparable to declared dyslexic students, in dimensions that are gauging levels of organization and time-management in their academic study behaviours. Given that 51% of students in research group DI (those with declared dyslexia) are sifted into the Control research subgroup by the same criteria, this outcome suggests that proportionally as many students with dyslexia as those without, consider themselves to have poor levels of organizational and time-management competencies in their studies. It is known that this aspect of academic learning management commonly presents issues for students with dyslexia at university (Mortimore & Crozer, 2006; Kirby et al., 2008; Olofsson et .al., 2012; MacCullagh et al., 2017) but it is of note that the non-dyslexic students in this project appear similarly challenged.

 

This may be suggesting that weaknesses in developing effective, strategic competencies in organizational and time-management skills are widespread amongst student communities, and not limited to those with specific learning difficulties. Furthermore, notable effect size differences arise between the Test and the Control subgroups in all five factors of ABC, supported with significant differences identified through t-test outcomes in all but ABC Factor 5, Debating, where the result is marginally non-significant. There are several features that warrant comment: Firstly, this datapool sifting process has produced a Test research subgroup that is the most sizeable (n=49/98 = 50%) in comparison to the four other Dx Factor sifting processes, together with the smallest Base research subgroup (n=8/98 = 8.2%). In other words, in using Dx Factor 3 as the marker for dyslexia-ness, 50% of the non-dyslexic research group would be classified as quasi-dyslexic. Secondly, effect size values between the Test subgroup and the Control subgroup range from g=0.38 in ABC Factor 5: Debating, with a marginally non-significant difference between the sample means, to an effect size of g=0.89 in ABC Factor 2: Engagement and a significant difference between the means. Given that effect size differences are one-tailed, these results indicate that students with reported dyslexia exhibit significantly lower levels of academic confidence when sifted according to the Organization & Time Management factor of Dyslexia Index. Recall that Dx Factor 3 comprises the dyslexia dimensions: 'I think I am a highly organized learner', 'I find it very challenging to manage my time effectively', and 'I generally remember appointments and arrive on time'. Given that in total, 50.6% (n=84/166) of the complete datapool present significant levels of dyslexia-ness when gauged through this Dx Factor alone, this implies firstly that issues with organizational skills and time management are by no means endemic amongst merely the dyslexic student community at university, but that developing into an organized and time-efficient learner may be challenging for significant proportion of all students.

 

But of particular note is the outcome showing an effect size of g=0.78 for ABC24 overall between the Test and the Control subgroups when these are determined by Dx Factor 3. Supported by a significant difference between the mean ABC24 values, these results indicate that quasi-dyslexic students are presenting a substantially higher level of academic confidence than their dyslexia-identified peers when viewed through the lens of organization and time management. In the context of the useful definition of academic confidence from Sander and Sanders' earlier, (2003) study stated as “... the mediating variable that acts between individuals' inherent abilities, their learning styles and opportunities afforded by the academic environment of higher education” (p4), these results suggest that being identified as dyslexic significantly depresses academic confidence.

 

This might also be indicative of the ineffectiveness of dyslexia-supporting learning development strategies accorded to students with dyslexia at university which are designed to assist with organization and time-management, assuming that these have been recommended and made available to identified dyslexic students by their HE institutions. This implies that a student not knowing that they may be dyslexic appears to be better in relation to the study-skill attribute of organization and time management. Comments returned in the questionnaire appear to confirm that issues with organization and time management are common across the student community: One respondent from the dyslexic student group located their dyslexia in the context of organizational challenges thus:

  • "My dyslexia affects my organization abilities mostly. I'm strong academically ... despite quite strong learning difficulties, because I have a good memory. [But] I am chronically late, disorganized and often have large dips in academic confidence" (Respondent #99141284; RG:DI; ABC24 = 33.8; Dx = 496.66)

 

Another respondent, in this case from the non-dyslexic group, provided a similar reflection, who with an overall Dx=346.15 is located in the Base subgroup although presented a Dx Factor 3 value of Dx=576.29:

  • "I have issues with procrastinating, time management and making an effective plan of knowing where to start ... I leave starting my work to the last minute and ... I leave little time for editing and improvements" (Respondent #21294241; RG:ND; ABC24 = 80.5; Dx = 346.15)

 

A notable comparison between these two respondents is that although both are expressing similar comments thematically, they present widely different levels of Academical Behavioural Confidence, ABC=33.8 for the dyslexic student against ABC=80.5 for her non-dyslexic peer.

Another respondent echoed experiences of poor levels of institutional support:

  • "I think there could be more support for students with learning difficulties. As of yet, the dyslexic team haven't been very helpful or supportive" (Respondent #61502858; RG:DI; ABC24 = 61.9; Dx = 633.07)

 

although without knowing more about this student’s circumstances and of the study support regimes offered by the Dyslexia Support Team at the home university, it would be inappropriate to draw further conclusions.

 

However, a different picture appears to emerge when examining the differences between the dyslexic students in the Control subgroup and the non-dyslexic students in the Base subgroup when these are determined according to Dyslexic Index Factor 3. In all ABC24 factors except ABC24-4, Attendance, the effect size between these subgroups is small or negligible, contributing to an effect size difference in overall ABC24 of virtually zero. The exception was for ABC24 Factor 4, Attendance, where a large negative effect size between these two sets of students was observed. Although this result was marginally non-significant (not shown in Table 26)), these outcomes seem to be suggesting, at least at face value, that disorganized and poorly time-managed non-dyslexic students may, perhaps unsurprisingly, also be less diligent in attending their teaching classes and tutorials in comparison to their dyslexic peers. However with such a small sample size of non-dyslexic students in the Base subgroup (n=8) these outcomes can not be considered as properly indicative of any significant differences.

According to Dyslexia Index Factor 4: Verbalizing and Scoping

 

The picture which emerges when Dx Factor 4 is applied as the sifting criteria for establishing the three research subgroups is also interesting. Firstly, it can be seen that there is a moderate effect size (g=0.61) between the Control and the Test subgroups’ ABC overall values, where the absolute difference in mean ABC24 is a substantial and significant difference. The principle contributor to this effect size is again arising from ABC24 Factor 2, Engagement, as has been reported above (when the datapool is sifted according to Dx Factors 2, Thinking and Processing, and 3, Organization and Time-management).

 

But also substantial contributions are shown from the ABC Factors 1, Study Efficacy (g=0.38) and 3, Academic Output (g=0.44). For ABC24-2, Engagement, the effect size between the Control and the Test subgroups is large (g=0.81) with another substantial difference in absolute ABC24-2 mean values which was significant; For ABC24-1, Study Efficacy, a low-moderate effect size (g=0.38) reflects the modest differences between absolute ABC24-1 mean values although this was also significant. For ABC24-3, Academic Output, the respective absolute ABC24-3 mean values, effect size and t-test outcomes reflected similar differences. For the remaining ABC24 Factors 4 and 5, negligible effect sizes are observed between the Test and the Control subgroups.

 

However it is also important to note that with the exception of ABC24-4, ABC levels across the other four factors return broadly similar mean values between the Base subgroup of strongly non-dyslexic students and the quasi-dyslexic Test subgroup where, when the data were sifted according to Dx Factor 4, generated a Test subgroup sample size of n=40, representing approximately 41% of students in the non-dyslexic group

 

These outcomes suggest that when students are categorized into subgroups according to Dx Factor 4 - which generates Control and Test subgroup sample sizes that are similar (n=40 (Control), n=48 (Test)) - there are marked differences in academic confidence between the Control and Test subgroups in three of the five factors of ABC. The ABC dimensions in these factors relate to the ways in which students are efficacious, aware of and able to meet their assessment targets, but particularly in relation to their levels of engagement, where there exists a highly significant difference in academic confidence in this factor. Dx Factor 4 comprises only two dyslexia dimensions in the sifting criteria, these being indicators of the approaches students use to appraise theories, ideas or tasks in their study courses, and the ways in which they express a preference to communicate what they know and how they might interpret this knowledge to others verbally rather than in writing.

 

However, given that these two (dyslexia) dimensions might be considered as markers of atypical and more holistic thinking that is subsequently reflected by likely challenges they might experience in translating this thinking into ordered, structured and linear writing, it is significant that the quasi-dyslexic students are presenting higher levels of academic confidence than their dyslexic peers. Again, this suggests that students who know about their dyslexia and who may be receiving study support in one form or another, remain challenged by academic processes that are core components of their study courses, despite any learning support that they may be receiving.

 

An explanation for this may be that it is the very help they are receiving - well-meaning as it will no doubt be - that may be a factor in reduced academic confidence, although as no direct evidence about dyslexic students' access to or receipt of support was queried in the research, this is conjecture. However, interesting supporting evidence for this does emerge from some of the comments students provided in the earlier, Masters' dissertation pilot study. For example, students complained:

  • "Extra support is not given in the right way. How does extra time in exams help? It doesn't reflect what would happen in the real world. Changing the assessment techniques would be better" (Respondent QNR #7; Dykes, 2008, p82);

  • "I did not use dyslexia support at all last year. I would prefer to ask for help when needed and I find the extra time in having to organize dyslexia support well in advance is not helpful" (Respondent QNR #28, ibid, p86);

  • "I am unable to use support study sessions as I am already finding it hard to keep up with coursework and don't have time" (Respondent QNR #34, ibid, p89);

  • "Going for help with studies takes up more of my time when I'm already struggling with too much work and not enough time; and it rarely helps as I can't explain why I'm struggling - otherwise I would have just done it on my own in the first place" (Respondent QNR #20, ibid, p99);

 

It can be seen that systemic failings related to how support services are delivered appeared to have been an factor that influenced some students' uptake of them. Hence it is possible that the negative effect of this impacts equally negatively on students academic confidence, because they may perceive that study-skill support they are entitled to is inadequately provided and inappropriately targeted. In this current study, conducted nearly a decade later and with students at a different institution, not dissimilar comments arose:

  • "[Support] should not just be for one type or group of people such as those with particular learning difficulties. [I] think that puts many people off as soon as they see the term 'learning difficulties' " (Respondent #71712644; RG:DI; ABC24 = 86.6; Dx = 592.48)

  • "Lecturers need to be more supportive instead of referring me to learning support" (Respondent #67632469; RG:DI; ABC = 41.7, Dx = 682.21)

 

And evidence was also provided which identified atypical preferences for thinking about and accessing academic work and how to communicate knowledge:

  • "I am a visual person and for me it's easier to remember something if I am shown an image of that thing" (Respondent #90023507; RG:DI; ABC24 = 38.3; Dx = 748.93)

  • "I usually use very visual ways to learn, for example drawing funny pictures to remember medication names ... and more interactive lectures would benefit me" (Respondent #74355805; RG:DI; ABC = 30.6, Dx = 699.15)

  • "I found audio recording lectures was quite helpful; also when lectures were interactive or when images or films were included I got a better understanding of the subject" (Respondent #16517091; RG;DI; ABC = 59.7; Dx = 339.92)

  • "I thoroughly enjoy seminars and lab classes and feel that I benefit much more academically in this setting [in comparison to] some days I have three consecutive hours of lectures ... after a while my attention wavers and I struggle to focus" (Respondent #39243302; RG:ND; ABC = 56.5; Dx = 345.22)

  • "I can sometimes have all-or-nothing thinking which makes it difficult to be critical and explain in detail - Sometimes it feels as if my mind spirals when I think about one topic for too long and I lose track of my original idea/thought" (Respondent #69417357; RG:ND; ABC = 56.6; Dx = 334.95)

 

These add to this current study’s argument in support of a timely revision of processes of curriculum delivery and assessment mechanisms, so that these might be more in line with the ethos of UDL, discussed in sub-sections 1.1 and 2.1(III). Evidence here suggests that the academic confidence of students with dyslexia is likely to be less negatively impacted were UDL more widespread in university learning, but it is also shown that atypical thinking and information processing preferences or difficulties occurs amongst non-dyslexic students too.

According to Dyslexia Index Factor 5: Working memory

 

Individuals with dyslexia are cited in literature as often experiencing differences in memory function, commonly evidenced by scores in Digit Span and Letter-Number Sequencing sub-tests of wider assessments, for example as part of the Weschsler Adult Intelligence Scale-IV (WAIS-IV - Egeland, 2015). There is not the scope in this project for a wider discussion about the relationship between working memory and/or short-term memory (STM) and dyslexia, as this would be more than enough for a project in its own right. Not least this may be due to a persistent discussion in the literature about whether these two descriptors of memory function are different, or are representing broadly similar cognitive processes. Cowan (2008) described STM as the capacity to readily access a limited amount of information recently acquired, and this may be compromised in dyslexic individuals in comparison to their non-dyslexic peers because the space available for storing immediate-access information may be reduced.

 

However, the true nature of these apparent deficits remains indistinct (Trecy, et al., 2013). In this current study, working memory is adopted as the more appropriate memory descriptor to use, not least because it may be more instrumental as one of the core, cognitive functions in planning and carrying out behaviours (Miller et al., 1960). But also, because working memory is important for gaining a good grasp of written ideas. For example, complex and lengthy sentence structures frequently found in academic writing require the reader to retain partial understanding of the sentence while reading forward to its conclusion (Cowan, 2008), which is clearly of consequence to individuals with dyslexia.

 

Thus, gaining some measure of students’ perceptions of their memory challenges was considered pertinent. Hence just two dimensions were included in the Dyslexia Index Profiler which attempted to acquire at least a cursory overview of working memory capabilities (Dx dimensions #13, relating to following directions to get to places and #12, remembering things like phone numbers). In sifting the datapool according to Dx Factor 5, Working Memory, some useful differences have emerged. Firstly, this sifting process had the effect of nearly doubling the number of respondents from the non-dyslexic group to be classified as quasi-dyslexic, changing the sample size of the Test subgroup to n=31. Comparing the sample mean ABC24 overall values between the Test and the Control subgroups showed a moderate effect size (g=0.40) between the absolute ABC values that was marginally non-significant. This indicates that overall, the academic confidence of quasi-dyslexic students exceeds that of dyslexic students when Dx Factor 5, Working Memory, is the defining criteria for dyslexia. The difference is more pronounced between the Control and the Base subgroups, represented by a large-moderate effect size (g=0.61) and a significant t-test outcome (ABC24=60.3 (Control), 69.2 (Base) g=0.61, t(68)=2.60, p=0.006 (not shown in Table 31)).

 

This indicates that non-dyslexic students are presenting substantially higher academic confidence in comparison to their dyslexic peers when the data are re-analysed according to Dx Factor 5, Working Memory. In both comparisons, it is again ABC Factor 2, Engagement, which appears to be making the most significant contribution to the overall effect size differences (g=0.64 Control/Test, ABC24-2: 48.9/61.2; g=0.70 Control/Base, ABC24-2: 48.9/62.1) which in both cases is supported by t-test outcomes indicating significant differences (Control/Test: t(64)=2.61, p=0.006; Control/Base: t(71)=3.03, p=0.002 (not shown in Table 31)). Some respondents reported issues of memory in their questionnaire responses:

  • "I find exams [particularly] stressful as I feel [they] are a memory test even though they may be posed as 'not a memory test' ... [and] my anxiety gets in the way of my concentration and memory for exams" (Respondent #44317730; RG:DI; ABC24 = 54.6; Dx = 563.23)

  • "My learning difficulty is related to my working memory" (Respondent #99268333; RG:ND (DNI); ABC24 = 47.9; Dx = 654.82)

  • "Having dyslexia ... sometimes affects the memory where in the moment you forget everything and don't know what you need to write" (Respondent #11098724; RG:DI; ABC = 62.4; Dx = 679.84)

 

Amongst all comments submitted however, those that referred to memory constituted only a small proportion (5/78 = 6.4%). With hindsight, a better appreciation of cognitive load theory (Sweller, 1988) and a sharper review of the working memory : short-term memory debate would have helped for designing these two dimensions of the Dyslexia Index Profiler more incisively. That may have generated outcomes that were more meaningful and relevant to the wider issues in the literature related to memory function in students with dyslexia. In the absence of these, outcomes derived from the analysis of this aspect of the data is viewed cautiously.

 

5.5  Predicting dyslexia

 

Multiple regression analysis has been used in education contexts to attempt to predict whether dyslexia exists amongst students with suspected dyslexia. For example, Tops et al. (2012) analysed data collected from a sample of 200 Dutch university students which was split equally between those with a known dyslexia and a Control subgroup of those with no known dyslexia nor any previous evidence of it. Based on several independent variables, such as for assessing STM, phonological awareness, and rapid-naming skills, a predictive model was generated. The subtests were drawn from the wide range of assessments regularly associated with attempts to identify dyslexia. An important element of the research design matched each dyslexic individual with a control-group data-partner using matching criteria of age, gender and field of study. Though not explicitly stated, it is presumed that this was intended to eliminate the likelihood of confounding analysis results that might otherwise be attributable to these variables.

 

The most important feature of the study was the derivation of a prediction equation that enabled a probability indicator of dyslexia to be generated, based on each individual's tests scores outputs. The research outcomes confirmed the view that the literacy difficulties associated with dyslexia extend into adulthood, indicated that the inherent phonological deficits persist in undergraduate students, and that these high-functioning adults were not able to compensate completely for them. However, it was also stated that since the process of regression analysis is data-driven, the results are applicable to the dataset from which it was derived and that generalizations could only be cautiously drawn.

 

But the most important concluding statement was that although the prediction model could be useful in educational settings, it did not indicate the causes of individuals’ dyslexia; and also that in comparison to the control group, students with dyslexia presented differences on just the variables in the model and there may be other measures that would be more optimal. It was claimed that this study was the first to bring prediction analysis to the field of dyslexia research (ibid) in order to convert multi-test data into interpretable dyslexia probabilities. Perhaps at the time, the authors were unaware of a prior study which had also attempted to create a multivariate predictive model for identifying dyslexia, albeit in young learners rather than for adults (le Jan et al., 2009). Although there were methodological differences between the two studies, not least where le Jan's study utilized a combination of PCA together with logistic rather than multiple regression analysis, the outcome was also a predictive model. The research conclusions claimed high levels of sensitivity and specificity.

These examples demonstrate the application of multi-variable regression analysis as valuable in dyslexia research, which is complementing the rationales of the multi-factorial approaches to understanding dyslexia outlined in Section 2. Hence this analysis approach was considered to have value in this current study. However, rather than use this process to predict dyslexia, the aim has been to explore the predictive validity for indicating levels of ABC based on Dyslexia Index, given that the Dyslexia Index Profiler also uses a multivariable design. Whilst this is interesting in itself, the greater value will be to use the generated prediction equations based on the research groups and subgroups in this project to add further evidence to the research hypothesis that students with quasi­-dyslexia, which may be unidentified dyslexia, return higher than expected levels of ABC than their dyslexia-identified peers. Table 27 (in sub-section 4.7) showed differences between the observed and expected mean ABC values for the research groups and subgroups for the five regression models that were derived, which supported other evidence presented in this thesis that students with quasi-dyslexia presented higher than expected academic confidence in relation to their dyslexia-identified peers. It is recognized that this extension of the research design can only be tentatively explored within the scope of this thesis, but early indications are that there may be merit in pursuing this research direction in a future project.

 
 

6.1  Summarizing the purpose of the research

 

This study has focused on trying to understand more about how the academic confidence of university students with dyslexia may be affected by their dyslexia. The research stems from a desire to apply scientific process to anecdotally observed evidence about how dyslexic students tackle their studies in comparison to their non-dyslexic peers. At two different university settings in my professional positions as an academic guide, my experience of working with both groups of students to develop their learning (and metalearning), indicated that considerable differences exist in attitudes and behaviours in relation to academic study. It is acknowledged that these can arise through a very wide diversity of individual circumstances and learning situations, both immediate and of historical or other origin.

 

However, the learning difference of dyslexia uniquely sets apart a substantial minority of students from their mainstream peers as a consequence of the ways that their dyslexia is said to impact on their academic studies, not least in comparison to learning impacts attributed to other minority-group characteristics, such as ethnicity, social class or cultural differences. This is because dyslexia is considered to present unique challenges in literacy-based education systems, challenges which are based on the assumption that dyslexia is fundamentally an issue associated with literacy capabilities. The evidence for this is substantial, and not a point of specific argument in this thesis. However, there is also considerable evidence that in high-functioning adult learners, as typically seen at university, many of the earlier literacy challenges inherent to a dyslexic individual’s learning processes may have been strategically ameliorated, leaving other dimensions of dyslexia to emerge; these may then have a greater impact on actions and behaviours in academic study.

 

Many learners face real issues that appear to be directly related to their approaches to their academic challenges (Klassen, 2006). Examples have been cited in the literature review above, but Klassen’s view, one which resonates with the themes in this project, is that confidence can be one of the blockages that is the source of many learning challenges, because academic confidence is the bridge that connects an individual learner's self-efficacy beliefs to their absolute performance in an academic task. This is an important idea because it implies that academic confidence is a constituent, success-forecast component of the processes that students progress through, when they are travelling from facing a specific academic task demand, to the academic output that is the endpoint.

 

This process seems to be partly a function of metacognitive knowledge and partly a function of intrinsic capabilities. Some significant studies have explored these in dyslexic students. For example, Butler (1998, 1999) found that dyslexic students struggle with analysing task requirements, and they often focus on lower-skill competencies such as spelling and grammar, while not recognizing the need for organizational capabilities or writing in a particular register. One of the outcomes of this current study, however, suggests that this may not be unique to students with dyslexia, where evidence has been presented to indicate that many students find organizing and managing their academic workload to be challenging. Tunmer and Chapman (1996) claimed that dyslexic students can be less metacognitively aware than their non-dyslexic peers, but this may be more of a manifestation of dyslexic students' knowledge, or perhaps just perception, that both their own and maybe more significantly, external expectations of the quality of their academic output is reduced; these feelings may be driven by the stigma associated with the disability label (Ho, 2004).

 

Reduced expectations may be a consequence of experiences in earlier learning, where they perceived that less was being demanded of them academically or worse, that educational opportunities were being denied to them because of their dyslexia (Shifrer, 2013; Shifrer et al., 2013; Hornstra et al., 2014). Alternatively, another explanation may be that their disability status has littered their learning experiences with teachers who consistently misjudge their academic potential by being more focused on managing their apparent disability (Hurwitz et al., 2007).

 

An additional explanation for why dyslexic students may poorly judge academic challenge complexity, might be that the tasks they are faced with are presented in such a way that inherently make deciphering what to do especially challenging for individuals characterized as particularly neurodiverse thinkers. That is, for students with dyslexia, gaining this appreciation for 'sizing up the task', may be more a function of the manner in which the task's academic context is framed as much as any research-reported deficit in metacognitive awareness (Borkowski et al., 1989).

 

Several conclusions drawn in this thesis have alluded that these issues may be widespread across student communities and not necessarily more prevalent amongst those with dyslexic learning differences. But what does appear to be widespread in dyslexic learners, is the enduring legacy of being ‘othered’ as a result of ‘differences’ in learning contexts, especially where this extends to stigmatization, which consequently has a detrimental impact on learning confidence in approaching learning. Hence this thesis has attempted to demonstrate that it may be the negative effects that are associated with being identified as dyslexic that may have an abiding effect on academic confidence.

 

6.2  Summarizing the research outcomes

 

This research used a self-report questionnaire, completed online, by university students predominantly at one UK institution, to gauge academic confidence and dyslexia-ness. Academic confidence was assessed using the existing ABC Scale developed by Sander and colleagues in the early 2000s with later modifications. Dyslexia-ness was assessed using an especially-developed Dyslexia Index (Dx) Profiler which framed dyslexia using a multi-factorial approach. By collecting background data about the more general demographical distribution of the students in the datapool, it was established that the sample could reasonably be considered as a typical cross-section of a student community at a UK HE institution.

 

The data collected permitted two research groups to be established: one group of self-declared dyslexic students, the other, students who declared no known dyslexic learning differences. From these, three subgroups were derived using the criteria of dyslexia-ness established from the output of the Dx Profiler. These were: students with known dyslexia, validated by high levels of dyslexia-ness, (Control subgroup); students with no known dyslexia validated by presenting low levels of dyslexia-ness, (Base subgroup); and students with no known dyslexia but who presented high levels of dyslexia-ness, (Test subgroup).

 

The research questions asked firstly whether or not university students who know about their dyslexia present significantly lower academic confidence than their non-dyslexic peers; and secondly whether students who indicated no formally identified dyslexia but who showed strong evidence of dyslexia-like learning and study profiles, present higher levels of academic confidence than their dyslexia-identified peers. From these, a further research question emerged which asked whether or not the manner in which students with dyslexia learned of their dyslexia impacted on their levels of academic confidence.

 

By comparing mean-average data for ABC between the subgroups, it was first established that in this datapool of students, there was a large effect size of g=1.04, together with a highly significant difference between ABC means of strongly dyslexic students in the Control subgroup and strongly non-dyslexic students in the Base subgroup (t(89)=4.94, p<0.001). Hence the null hypothesis that there is no difference in academic confidence between dyslexic and non-dyslexic students was rejected, in favour of the alternative hypothesis that non-dyslexic students present a significantly higher level of academic confidence than their dyslexia-identified peers.

 

Using the same analysis processes, it was further established that there was a medium effect size, g=0.48, supported by a significant difference between ABC means of strongly dyslexic students in the Control subgroup and strongly quasi-dyslexic students in the Test subgroup (t(63)=1.743, p=0.043). Hence the second null hypothesis that there is no difference in academic confidence between dyslexic and quasi-dyslexic students was also rejected in favour of the alternative hypothesis that quasi-dyslexic students present a significantly higher level of academic confidence than their dyslexia-identified peers.

 

Furthermore, the additional null hypothesis that the way in which dyslexic students learn about their dyslexia has no impact on their academic confidence was rejected, where a moderate effect size, g=0.59, was indicated between students whose dyslexia was diagnosed as a disability or as a difficulty, and those who learned about their dyslexia otherwise, supported by a significant difference in mean ABC values (t(30)=2.16, p=0.019).

By exploring data from both metrics more deeply through PCA, interesting additional features were revealed:

 

Firstly, on a Dx factor-by-factor basis, there were generally no statistically significant differences in mean Dx values between the Test and the Control subgroups. This indicated that quasi-dyslexic students were presenting very similar levels of dyslexia-ness to their dyslexia-identified peers. Hence comparing ABC between these subgroups was justified. Secondly, statistically significant differences between mean Dx factor levels were shown between both the Base and Control subgroups and between the Base and Test subgroups for all Dx factors except Factor 3, ‘Organization & Time Management’. It was concluded that this showed evidence to suggest that this feature of academic learning management presents difficulties and challenges to students across the learning community, and is not specific to students with dyslexia. In considering ABC on a factor-by-factor basis, in all ABC factors except ABC Factor 4, Attendance, where mean levels of ABC were only marginally different across the groups and subgroups, sharp differences in mean values were revealed between the Test and Control subgroups. Especially strong differences were revealed between the Base and Control subgroups.

These processes of PCA enabled an intriguing factor matrix to be created, which presented differences in ABC factor values between the Base, Control and Test subgroups when these were reconstructed according to Dx factors. The matrix enabled a fresh scrutiny of effect sizes between ABC factor mean values of the subgroups to be possible. In particular, it showed that when the datapool was re-organized according to Dx Factor 3, Organization & Time Management, significant differences were revealed in four of the five ABC factors between the Test and the Control subgroups with marginally non-significant differences in the remaining ABC Factor, ‘Debating’. This further evidences that identifying dyslexia in university students may negatively impact on their levels of academic confidence since for this Dx Factor 3, the quasi-dyslexic students in the Test subgroup showed considerably higher levels of ABC in all factors in comparison to their dyslexia-identified peers in the Control subgroup.

 

It is also of note that when re-organizing the data according to Dx Factor 4, Verbalizing & Scoping, significant differences emerged between the mean ABC levels of the Test subgroup in comparison to the Control subgroup in three of the five ABC factors. These factors of Study Efficacy, Engagement, and Academic Output all represent dimensions associated with academic process, and mean levels of ABC for quasi­-dyslexic students were significantly higher than for their dyslexia-identified peers. Of these three factors, the difference between these subgroups’ mean levels in ABC Factor 2, Engagement, was very highly significant with an absolute difference of 16.9 ABC points (ABC24-2: Test: 66.0; Control: 49.1).

 

6.3  Limitations of the research

 

Although the datapool represented a moderately large sample (n=166), when this was sifted into research groups and subgroups, the outcome led to a small Test subgroup of 18 participants who presented quasi-dyslexia. Whilst this represented a substantial proportion of the parent group (18.4%), possibly suggesting that unidentified dyslexia amongst university students is as high as nearly one in five students, it is sufficiently small as to limit the generalizability of conclusions drawn. Therefore, only tentative explanations for differences between quasi-dyslexic students and their dyslexic peers have been offered. Furthermore, the datapool was drawn almost exclusively from one HE institution, and although it was shown that the demography of the students who chose to participate represented a good cross-section of adults likely to be studying in HE more widely, the generalization of results should be tempered accordingly.

 

The most critical limitation of the study should be attributed to the use of the Dx Profiler as the discriminator for finding students with quasi-dyslexia. This was an innovative and possibly controversial instrument for gauging dyslexia-ness, in itself a term that was inaugurated in this study, and although an exhaustive process of development led to confidence in the Profiler’s ability to meet the design objective of this study, it remains untested outside this datapool of students. It has emerged, however, as a robust tool for this purpose and it is recommended that further development of the Dx Profiler is warranted.

 

Other limitations might be attributed to processes of statistical analysis used, especially the abundant use of the t-test for differences in independent sample means. However, it is argued that this has been counterbalanced by treating these results as secondary to effect sizes, and that taking these two outcome measures together as complementary to each other adds validity to conclusions drawn from statistical outputs. Furthermore, a deeper interpretation of the regression analysis would have been desirable although exploring this in greater detail has been limited by institutional constraints imposed on the scope and length of this thesis.

 

6.4  Concluding remarks

 

What are the outcomes of this research saying about the academic confidence of students at university? What is suggested about the nature of dyslexia in students at university? And what has been revealed about the inter-relationship of these two variables?

The aim of this project has been to establish the effects that attributing the label of dyslexia to a particular set of learning and study profiles might have on academic confidence. This is important because it may contribute to a reduced likelihood of gaining strong academic outcomes due to academic confidence, as a sub-construct of academic self-efficacy, being widely reported as a potential marker for academic performance (Honicke & Broadbent, 2016).

 

In short, when it comes to guiding learners towards a good degree at university, this project has tested the notion of whether is it better to label an apparently dyslexic person as ‘dyslexic’, or not. If not, then it follows that dyslexic students may be better left in ignorance of their ‘learning difference’ because by taking this course of action, the prospects of gaining an academic outcome comparable to that of their non-dyslexic peers may be greater. This may mean that these individuals should be encouraged to battle on as best they can within the literacy-based system of curriculum delivery in which they are studying, despite it not being suited to their learning profiles, strengths and preferences. In taking this course of action, there would be no recourse to ‘reasonable adjustments’ that identify them as ‘different’ which may be desirable because it has been shown that the identification itself might be more damaging to their academic prospects than the challenges they may face that are attributable to their dyslexia. 

 

However, it has been shown that dyslexia remains difficult to define because it can comprise a variety of arguably identifiable characteristics and dimensions which can occur together in multiple combinations. Some of these profiles of dimensions are observable in many non-dyslexic students too. In academically capable individuals, the more conventionally considered characteristics of dyslexia associated with weak literacy skills can have been significantly ameliorated, either through strategically modifying intrinsic approaches to learning, consciously or unconsciously, or through use of external support resources in the form of digital and assistive technologies such as spell-checkers or text-to-speech applications. The outcome is that many of the earlier issues that a dyslexic individual might have faced in their learning-experience history may be less significant than they were. This can be demonstrated when dyslexia is considered as a multifactorial learning difference, whereby individuals can present significant levels of dyslexia-ness in some factors but not necessarily in others.

 

This leads to an acknowledgement of the view that dyslexia might be best considered as an information processing difference at university rather than predominantly a literacy-skills disability, although the literacy demands of academic study continue to present disadvantageous conditions for many students with information processing differences, because curricula are still broadly delivered and assessed in literacy-based formats. As universities open their doors to a broader spectrum of students through widening participation and alternative access schemes, which have also seen a substantial rise in numbers of students with learning differences choosing to enter HE, it is reasonable to suppose that many of these new faces, in addition to the more traditionally-seen ones, would benefit academically were there a better institutional-level understanding of the impact that individual differences have on educational engagement and ownership of learning (Conley & French, 2014). Adopting the principles of UDL would meet many of these objectives by ensuring a more accessible, flexible and adaptable learning provision at university that would enable not only students with dyslexia, but all students to engage more equitably with learning using the academic and functional capabilities that they bring to their institutions.

To aid this process, a more appropriate way to repackage dimensions of dyslexia in a contemporary university-learning context may be to consider these as academic learning management dimensions, not least because many of these are widely observable across university student communities. By characterizing any student’s blend of academic learning management dimensions through a profile approach, a better understanding can be gained of strengths and weaknesses. As a result, this could be the agent for learning development strategies to be designed and individually-tailored that would capitalize on strengths and ameliorate weaknesses, and hence enhance the effectiveness of learning, enable students to gain a working understanding of their own metalearning, and to reflect, perhaps with help, on how this knowledge about how they learn best, can be developed, enhanced and actioned.

 

The current study has demonstrated that when this profile approach is related to a similar process of gauging an individual’s academic confidence, the outcome can be a useful and detailed appraisal of a student’s blend of learning characteristics, attitudes, actions and behaviours in relation to their academic studies at university. This could be a basis upon which comprehensive, personalized learning plans could be developed, not just for students with dyslexia, but for anyone studying at university. Since academic confidence is “a mediating variable that acts between individuals' inherent abilities, their learning styles and opportunities afforded by the academic environment of higher education” (Sander & Sanders, 2003, p4), gaining a greater understanding of how it impacts on academic outcomes would be a conduit for enhancing these outcomes and creating a more fulfilling and less stressful learning experience. Granted, this may challenge the scope of strategic planning for the future of tertiary-level, high-quality learning, not least because it may be considered so radical, difficult and expensive to implement, that attempts to create such socially inclusive learning environments and opportunities may be inhibited by organizational and systemic factors that are resistant to change (Simons et al., 2007).

 

Striking a balance between flexible, adaptable and inclusive teaching and learning activities and the essential job of universities to foster climates of research innovation and academic excellence is becoming ever more challenging, not least because funding is uncertain, and other initiatives aimed at providing alternatives to full-time university study are on the increase, for example, multi-stakeholder degree apprenticeship schemes (Mulkeen et al., 2017), or first degrees being offered by non-university providers in ways that financially undercut, and hence undermine the viability of the more traditional providers. Even setting aside advocacy for UDL, it is difficult to see how this constitutes a joined-up, national strategy for tertiary-level learning.

 

6.5  Directions for future research

 

It is considered that this study is the first to explore specifically the relationship between academic confidence and dyslexia amongst university students. As such, it has had to cut new ground in formulating and designing a research process to enable meaningful outcomes to emerge. Further work in exploring links between these two constructs in HE contexts is called for, not least to validate some of the outcomes in this current study. Specific amongst the innovative processes that have been developed to address the research hypotheses, has been the creation of a new metric to gauge dyslexia in a wider, multifactorial way – the Dyslexia Index Profiler.

 

It is considered that this instrument warrants further use and development, partly so that data generated can add to that collected in this current study, not least to void the limitation of single-institution data, but also to enable a wider critique of its data-collecting mechanisms to be validated. It is hoped that one strand of future development of the Dx Profiler may be to establish it as another tool in the arsenal of dyslexia screening tools, especially as it has attempted to present dyslexia in a more neutrally-nuanced way which it is believed would be less intimidating because it is not focused on gauging deficits, or grounded in disability agendas.

 

But it is also suggested that by blending the two metrics of ABC and Dyslexia Index, these dimensional appraisals of students’ academic learning management capabilities at university might be developed into a useful tool for learning development practitioners working with the wider university community. By doing so, this would further weaken the stigmatization of difference, whether due to dyslexia or not, and begin to move universities towards the aspirations of Universal Design for Learning, especially if Learning Development, could be more widely incorporated into the curricula of all academic disciplines studied at university.

 

Andrew Dykes B.Ed., M.A., M.Sc., CELTA, FHEA

Academic confidence and dyslexia at university

A PhD Research Project October 2014 - May 2019

Middlesex University, London

Andrew Dykes B.Ed, M.A, M.Sc, FHEA

ad1281@live.mdx.ac.uk; academic@ad1281.uk

+44 (0)79 26 17 20 26