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Learning style preferences: anexamination of differences amongststudents with different disciplinarybackgroundsFrances Hilla, Bland Tomkinsona, Anna Hileya & Helen Dobsona

a School of Mechanical, Aerospace & Civil Engineering, Universityof Manchester, Manchester, UKPublished online: 29 Sep 2014.

To cite this article: Frances Hill, Bland Tomkinson, Anna Hiley & Helen Dobson (2014):Learning style preferences: an examination of differences amongst students with differentdisciplinary backgrounds, Innovations in Education and Teaching International, DOI:10.1080/14703297.2014.961504

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Learning style preferences: an examination of differences amongststudents with different disciplinary backgrounds

Frances Hill, Bland Tomkinson*, Anna Hiley and Helen Dobson

School of Mechanical, Aerospace & Civil Engineering, University of Manchester,Manchester, UK

The context of this study is of students with backgrounds in a variety of engi-neering and social science disciplines, and from first degrees in different coun-tries, coming together to study Project Management. Tailoring teaching to allindividuals’ learning styles is not possible, but, in an attempt to learn how toteach better in ways that fit students’ different needs, the preferred learning stylesof engineering and humanities students are measured by use of the MemleticsLearning Styles Quiz. Individual scores are normalised, and individual students’preferences for one style over another are calculated. Statistical analysis showsthat the engineering students express a significantly stronger preference for a log-ical learning style over visual, verbal, aural, physical or solitary learning styles,and for a visual learning style over both verbal and aural learning styles, whereasstudents with a social science background expressed significantly stronger prefer-ences for a social learning style than for a logical learning style. Additionally,the learning style preferences of groups of engineering students in the UK and inMalaysia are compared, showing stronger preferences among the UK studentsfor logical and social learning styles, and among the Malaysians for a solitarylearning style.

Keywords: learning styles; engineering; memletics; international; social sciences

Introduction

Students differ one from another in many ways. Some of these are obvious: size,age, ethnic origin, gender. Some are less visible; for example: subjects previouslystudied, type and level of qualifications, command of the language of tuition, life/work experience, their personalities (Tomkinson, 2007), their cognitive style. Theseimpact on how students think and learn.

The concept of learning styles is a contested one. For some psychologists, ‘style’of learning is an outworking of personality (e.g. Busato, Prins, Anshout, &Hamaker, 1999). Coffield, Moseley, Hall, and Ecclestone (2004) throw doubt on thevalidity, partly by working from the lemma that its use is for determining individualteaching approaches to individual students, though this is not claimed by the propo-nents. More seriously, they found that many authors of tests were unwilling to sharedata on reliability. Salter, Evans, and Forney (2006), looking at the Learning StylesInventory (LSI) and the Myers–Briggs Type Indicator, found that learning stylesappear to be relatively stable over a two-year period. Gilbert and Swanier (2008)found that student preferences for learning mode changed during the course of a

*Corresponding author. Email: bland.tomkinson@manchester.ac.uk

© 2014 Taylor & Francis

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structured learning experience, although their report is a little unclear about the LSIemployed. The concept is also driven by different interpretations. Kolb’s LSI(1971), or the approach of Honey and Mumford (1983), are primarily about a neo-cognitivist learning cycle, with the preference for one or more stages of the cycleinitially being seen as secondary. For some, the concept is one of what mode stu-dents use to receive their information: perhaps best known is Fleming’s (1995)VARK model. A different approach was taken by Entwistle (1997) and his collabo-rators, with an emphasis on whether learning is seen as understanding facts ormerely remembering them. These are only two of a number of approaches:Sadler-Smith (1996) attempts to distinguish between learning preferences, learningstrategy, learning style, cognitive strategy and cognitive style: ‘

� Learning preference: the favouring of one particular mode of teaching overanother;

� Learning strategy: a plan of action adopted in the acquisition or imparting ofknowledge or skill through study, experience or teaching;

� Learning style: a distinctive and habitual manner of acquiring or impartingknowledge through study, experience or teaching;

� Cognitive strategy: a plan of action adopted in the process of knowing, con-ceiving or perceiving; and

� Cognitive style: a distinctive and habitual manner of knowing, conceiving orperceiving.

One way in which each of these five constructs differ is in terms of the degree towhich each may be observed and articulated.’ (p. 186).

Sadler-Smith envisages each of these as layers surrounding a central personalitydimension, with cognitive style as the inner layer and learning preference as theouter.

Cognitive style is described by Allport (1937) as a ‘typical or habitual mode ofproblem solving, thinking, perceiving and remembering’ (Cassidy, 2004, p. 420).Shaughnessy (1998) indicates that students learn significantly better through theirpreferred learning styles, and suggests that students may vary their choice ofresources but are encouraged to begin learning through their strengths whenever theacademic material is complex or difficult for them. However, a lengthy discussion oflearning styles is secondary to this article.

The literature on disciplinary differences in teaching and learning has tended tofocus on aspects of teaching, rather than aspects of learning. Sawir (2011), for exam-ple, suggests that ‘Staff members from particular disciplines hold specific instruc-tional beliefs about teaching. For example, the majority of staff members from thesoft disciplines … reported making more adjustments in their teaching and curriculato accommodate international students’ needs and expectations than those from thehard disciplines’ (p. 53). Entwistle and Tait (1995) concluded that disciplinary dif-ferences in learning styles are real but may be brought about by differences in teach-ing style and assessment. Neumann (2001, p. 138) suggests that ‘Student assessmentreflects … different goals. Hard areas require memorisation and application ofcourse material, while soft disciplines are more likely to have exam questions requir-ing analysis and synthesis of course content’.

People learn by listening, watching, reading, understanding processes, and byactively doing things. We learn together, and on our own, and each of us has our

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own preference of how we like to learn (Felder & Silverman, 1988; Kapadia, 2008).Students and teachers are themselves often unaware of their learning style prefer-ences, but an experienced teacher will vary the styles offered in order to connectwith all students. Students who are taught in ways that support them in their pre-ferred learning styles can be expected to enjoy learning and to proceed better aca-demically (Shaughnessy, 1998). But are there differences between students indifferent disciplines that should influence the techniques faculty use in teaching?Are engineering students’ and social science students’ learning likely to be opti-mised by different balances of teaching techniques? With students coming ontocourses such as Management of Projects from a diverse range of backgrounds, howcan a study of learning style preferences inform their faculty?

Disciplinary differences in approaches to learning

Much of the research into disciplinary differences has focussed on differences inteaching, rather than differences in learning. Epistemological and ontological differ-ences between disciplines have long been noted, but these can be seen as part ofsubject content rather than impacting upon approaches to learning. Neumann, Parry,and Becher (2002) reflect on how the salient features of hard and soft disciplinesimpact not only on teaching styles but also on what is to be learned, thus affectingthe ways of approaching different types of knowledge. Neumann (2001) also pointsto disciplinary differences in educational goals and, thus, in approaches to assess-ment. To the extent that learning styles adjust to the assessment approaches, thesedisciplinary differences can impact on student approaches to learning. This is under-lined by a number of essays in a collected work by Hativa and Marincovich (1995).Hativa and Birenbaum (2000) compared preferences between two groups of stu-dents: one from the social sciences and one from engineering. In this case, therewere clear distinctions in their preference for different styles of teaching. One con-clusion was that students with different approaches to learning are likely to definegood teaching in ways that reflect those approaches. However, the authors voicedsome concern that the results might be influenced by the ways in which the differentgroups had been taught. This present study looks at students with different disciplin-ary backgrounds who are learning together, so there can be no teaching style effect.

Using the LSI

The choice of instrument was dictated as much by utility as by issues of validityand reliability. The Memletics Learning Style Quiz (MLSQ; Advanogy, 2003) offersa means of comparing an individual’s learning styles preferences. The quiz offers 70statements, and requires the participant to choose whether the statement is ‘nothinglike me’, ‘partially like me’ or ‘very much like me’. Scores of 0, 1 and 2 are given,and the participant gains a score out of 20 in respect of each of seven Learning Stylepreferences: Visual, Aural, Verbal, Physical, Logical, Social and Solitary. These areidentified both by the type of input used, and the area of the brain that processes theinput (Davis, 2007). For this present study, the memletics questionnaire was chosenbecause it looks at a wider range of dimensions than many others and, more particu-larly, because there was comparative data available from other countries. However,the issues of validity and reliability remain ever-present. In a study of this nature, itwas not possible to independently verify the reliability and validity of the chosen

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instrument and published data is scant. The READI instrument, which is based inpart on the memletics questionnaire, has been subject to independent scrutiny andthis was reported by McLawhon and Cutright (2012). They suggest that validity hasbeen confirmed by a study by Atanda Research and reliability by Applied Measure-ment Associates of Tuscaloosa, though it has not been possible to verify theseclaims. In terms of validity, in this present study, the results obtained were fed backto students, who recognised them as appropriate – indicating a measure of facevalidity. The instrument was also applied to cohorts of the same course in differentyears, obtaining results for which there was no significant difference – implying adegree of reliability.

From their responses to the MLSQ, engineering students at three universities, inMalaysia, Finland and South Africa have previously been found to have social learn-ing style as their most preferred style, and verbal learning styles as their least pre-ferred style (Abidin, Ziegler, & Tuohi, 2011). This is also seen to be true ofBusiness and Information/Computing students at universities in Malaysia, Finlandand the UK (Abidin, Ziegler, & Tuohi, 2012). However, differences in the sequenceof other preferences were noted between students of the different disciplines: whileengineering students put logical learning style second to favourite, the business,information systems and information computer technology students preferred aurallearning style to logical. The data from this study also show that female students’average preference scores were higher in all three institutions studied, by up to 9%,and that there were differences greater than 16% between the scores of cohorts indifferent countries, with South African students indicating higher levels of prefer-ence than Malaysian, with Finnish students indicating lower levels of preferenceoverall (Table 1). This indicates that South African students, and female students asa whole, are more inclined to identify with learning styles than their peers fromMalaysia and Finland, and their male peers as a whole; pointing to a level ofvariable subjectivity in students’ interaction with the MLSQ.

Methodology

Memletics learning styles questionnaire

This paper uses a more statistically rigorous approach to the analysis of MLSQscores, in order to eliminate some of this variable subjectivity. In the study,Manchester students on two similar problem-based learning modules, one offeredfrom within Engineering, and one from within the Business School, answered theMLSQ. This paper analyses their preference scores in order to test the hypothesisthat the two groups will show significantly different learning preferences when theirscores are normalised, and when preferences for each style are compared topreferences for each other style.

Table 1. Mean values of learning preference scores given by male and female engineeringstudents at three universities (Abidin et al., 2011).

UTP, Malaysia CPUT, South Africa TUAS, Finland

Male 82.00 87.72 74.98Female 84.18 94.91 81.49

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Manchester engineering students’ MLSQ scores, and their preferences for differ-ent learning styles are then compared with those of engineering students at UTP,Petronas.

In total, 757 student responses have been used (Table 2).

Analysis

Four levels of analysis were conducted on the scores.Initially, the raw scores of different student groups were compared graphically

(Figure 1). Some groups scored their preferences lower overall than others. At theextremes, the electronics students at TUAS’ score totals averaged 72.9, while theBusiness Information Technology students at Petronas’ score totals averaged 88.1.This suggests that there is a difference in perception relating to scoring ofpreferences, and that the comparison is not, therefore a true reflection of students’preferences of one style over another.

The total scores of the individual subjects ranged between 45 and 127 (with amaximum available of 140). The extremes of the range are shown in Table 3.Student A, whose total score is 127, gives a very much higher preference rating forsolitary learning style than Student B, whose total score is 45. However, this is

Table 2. Institutions, programmes and numbers of participating students.

Institution Number of participants Programme

BU 109 Information systems, computing + mathematicsTUAS 53 ElectronicsTUAS 26 BusinessTUAS 28 Business information technologyTUAS 59 Information technologyTUAS 61 Information technology 2UTP 32 Information computer technologyUTP 77 Chemical engineeringUTP 100 Engineering 2Manchester 70 Interdisciplinary sustainable development (Eng)Manchester 142 Skills for sustainability and social responsibility (-Bus)

Figure 1. Averaged raw scores of student groups.

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Student A’s lowest preference, and it is reasonable to suggest that his/her depen-dence on solitary learning style may not be particularly high, whereas Student B,whose total score is so much lower, is expressing a comparatively higher preferencefor solitary learning style.

In order to reflect the dependence, or comparative preference, of each student oneach individual learning style, a second stage of analysis involved normalising thescores of the students to percentages of their individual total preference scores.

The normalised scores of groups of students could then be compared. A compar-ison of normalised scores, for the same student groups, is shown in Figure 2.

This enhances comparison of student groups’ preferences across different learn-ing styles; for example, it can be seen that the TUAS Business students express aparticularly high preference for social over solitary learning style, and that theManchester Engineering students express a strong preference for logical learningstyle over verbal. However, it will be observed that all groups express a compara-tively strong preference for social learning style (score 16.4), and a low preferencefor verbal style (12.6) (Table 4). This appears to be more a characteristic ofresponses to the MLSQ than a true reflection of the student groups. It indicates thatthese scores should be used carefully for comparison between different students’

Table 3. Two examples of individual students’ Memletics quiz scores, raw and normalised.

Visual Aural Verbal Physical Logical Social Solitary Total

Raw scores Student A 20 17 19 19 19 19 14 127Student B 6 7 7 8 7 2 8 45

Normalisedscores

Student A 15.7 13.4 15.0 15.0 15.0 15.0 11.0 100.0Student B 13.3 15.6 15.6 17.8 15.6 4.4 17.8 100.0

Figure 2. Averaged normalised scores of each student group.

Table 4. Averaged normalised scores for the seven Memletics learning styles for all groups.

Visual Verbal Aural Physical Logical Social Solitary

13.8 12.6 14.3 14.0 15.2 16.4 13.6

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preference differentials rather than for looking directly at students’ preferences.However, the averages presented in Table 4 are subsequently used to redress theimbalances caused by the apparent weighting of the MLSQ, and to look specificallyat the preferences of one group of students for one style over another.

A third stage of analysis was then performed, comparing pairs of groups, toestablish where there were statistically significant differences between theirexpressed preferences for a learning style.

A fourth stage compared groups’ relative preferences for one learning style overanother.

Manchester engineering vs. business students

Memletics scores of engineering and business students at Manchester were com-pared with a view to understanding their preferences. Both groups of students wereenrolled on an optional problem-based learning (PBL) module, and may not be rep-resentative of their full cohorts, but the contrast was nonetheless strong.

A comparison of the raw and normalised scores of the two groups (Figure 3)shows clear differences between the two groups, and slight differences between theirraw and their normalised scores. However, although on raw scores engineering stu-dents express lower preference for solitary learning style than Business students, thedirection of the difference is reversed on normalised scores. This is shown moreclearly in Figure 4.

The business students express significantly higher preferences for verbal(p = .001) and aural (p = .02) learning styles than the engineers, and the engineeringstudents express a very significantly higher preference for logical style (p = .0001)than the social scientists. Other differences were not statistically significant.

The significance levels were changed considerably by the normalisation (Table 5).None of the confidence levels are pushed past the .05 threshold, in either direction,but this is a clear possibility. This is an important consideration in asserting theimportance of normalisation of scores.

It was also considered important to explore the consistency of individualstudents’ comparative preference levels and the significance of these. Individual stu-dents’ comparative preferences were computed, giving the degree to which a student

Figure 3. Raw and normalised scores of business and engineering students at Manchester.

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preferred one learning style over another. This made it possible to test the series ofmatched differentials, of each style against each other style.

A primary aim of the investigation was to compare the learning stylepreferences, of engineering students with those of Business students. Once the pref-erence differentials had been computed for each student, their averages were tested(as matched samples) for significance.

The two groups’ average differentials were then compared, and the differencesbetween the two groups tested for significance (as non-paired, independent samples).About 10 of the 21 comparisons showed significant differences between the twogroups (Table 6).

From the substantially greater number of differentials achieving the significancethreshold, it can be seen that Manchester’s Engineering students have more clearlydefined learning style preferences than Business students.

Engineering students: Petronas vs. Manchester

Normalised scores of the engineering students on the PBL module at Manchesterand the engineering students at UTP were compared. The two groups’ preferencesshowed broadly similar profiles (Figure 5). However, Petronas students preferredaural and solitary learning styles significantly more than Manchester students(p = .03 and .001), whereas Manchester students preferred logical learning stylesignificantly more (p = .000003).

Figure 4. Difference in preference level (business students’ minus engineering students’preference levels).

Table 5. Differences between learning style preferences of Manchester Business andEngineering students, for both raw and normalised scores (+ve = preferred more by Businessstudents).

Visual Verbal Aural Physical Logical Social Solitary

Raw Difference .2 1.5 1.5 .6 −1.3 .6 .5p Value .6 .0003 .003 .2 .03 .3 .2

Normalised Difference −.4 1.3 1.3 .0 −2.3 −.1 .0p Value .4 .001 .02 .9 .0001 .6 .8

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The two groups of engineering students’ preference differentials were then foundto differ to a significant level of confidence (p = .05) in 11 of the 21 comparisons(Table 7).

In order to redress the imbalance between average scores for different learningstyles apparent in Table 4, the Manchester Engineering students’ scores were norma-lised with respect to these. Their significant preferences (p < .05), according to theMLSQ, for one learning style over another are shown in Table 8.

Discussion: how to use this knowledge…

There are two possible ways to respond to these findings. The first is to identify thepreferred learning styles of the group to be taught, and to develop teaching stylesthat convey the subject material through these styles. Thus, students with a strongpreference for solitary learning might be primarily set work to work on individually,and students with a preference for logical learning style might be set primarilymathematical tasks, with closed, set answers. However, these cannot be relied uponto achieve the full range of education needed by these students, who need to be ableto work in groups, and on open-ended problems.

Table 6. Stronger preferences shown by Manchester students in one discipline than theother.

Discipline More preferred style Less preferred style Preference differential p-Value

Engineers Logical Verbal 3.7 .000002Logical Aural 3.6 .0001Logical Physical 2.3 .0063Logical Visual 1.9 .0082Verbal Visual 1.7 .018Aural Visual 1.6 .022Social Verbal 1.4 .046Logical Solitary 1.4 .0005Solitary Verbal 1.3 .047

Business Social Logical 1.6 .0048

Figure 5. Learning preferences of Manchester and Petronas engineering students (normalisedscores).

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The alternative is to use a series of teaching styles, as advocated by Coffield et al.(2004, p. 122), who suggest that ‘Four learning styles per class may not be toodifficult to achieve during a course of study and the variety would help to providestudents with an enjoyable experience’. Vermunt (1998) suggests that deliberate mis-matching of teaching method to preferred learning styles may create ‘constructivefriction’, and Grasha (1984) argues that students need to be ‘stretched’ to learn, andthat even students with strong learning style preferences find a variety of teachingapproaches helps avoid boredom. Lack of provision for a student’s learning style maylead to unmotivated, truculent behaviour, often identified by the tutor as laziness(Coffield et al., 2004). Open-ended group tasks, which engage both independent andcollaborative learners, using strengths in social, solitary, organisational (logical)learning styles may usefully be added to a traditional lecture-based course, whichmay otherwise be more focussed on verbal and visual learning. Within this, it will beimportant to give support for students whose skills in these learning styles are lessdeveloped. However, many ‘real-life’ tasks, and employment routes, reward anyonewho has developed these capabilities.

Lowery (2009) suggests that, depending on the situation, each different learningstyle has its own strengths and weaknesses, and stresses the importance for alllearners to develop proficiency in all learning styles. She notes that ‘some studentsindicated that they were beginning to realise the need to broaden their own learning

Table 7. Stronger preferences shown by engineering students in one university than theother.

University More preferred style Less preferred style Preference differential p-Value

Manchester Logical Physical 3.1 .00020Logical Aural 2.6 .000053Logical Verbal 1.8 .0014Social Solitary 1.8 .0045Logical Visual 1.5 .025Social Aural 1.4 .021Logical Solitary 1.3 .032Visual Aural 1.2 .037

Petronas Solitary Visual 1.6 .0062Social Logical 1.3 .032Solitary Verbal 1.2 .029

Table 8. Significant preferences of Manchester engineering students for one learning styleover another.

Over

Prefer

Visual(%)

Verbal(%)

Aural(%)

Physical(%)

Logical(%)

Social(%)

Solitary(%)

Visual . – – – 10 – –Verbal – . – – 13 – –Aural 9 6 . 6 19 9 –Physical – – – . 13 – –Logical – – – – . 5 –Social – – – – – . –Solitary – – – – 13 – .

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style preferences’ (p. 57). Teachers will naturally favour their own preferred styles,but will benefit from broadening their styles in order to work optimally with allmembers of their classes.

Students have been seen to report a positive view of groupwork, a view which issupported by analytical observation (Bessant, 2012). This gives opportunity todevelop skills through the full range of learning styles, with each supporting eachother. Students from 3 UK Universities, asked to appraise their skill developmentover a problem-based group learning module endorsed strongly their improvementin ‘working in a team on a group task’, ‘problem solving when presented with atask’, ‘researching around a topic or issue’ (pp. 7–8) etc. Although these do notrelate specifically to the MLSQ Learning styles above, these skills areas cross thelearning styles boundaries. Student comments on study skills included

During group discussions many people never speak up because they’re afraid that peo-ple will judge them, and I was afraid this would be the case. However, in my groupeveryone is included. We communicate and discuss together without facing difficultiesof disagreements. Working in groups is a great advantage. Personally it encourages meto be more focussed because it combines different ideas and provides innovative solu-tions to all kinds of problems and approaches (Keele).

I developed my professional skills using this course as a catalyst and I gained knowledgeabout different work culture being in a multidisciplinary team (Manchester).

This last highlights the crossover of this teaching and learning mode with thevalues of intercultural competencies (Goldfinch et al., 2012). In the same waythat students need support to develop their capacity to work across cultures, theyneed to learn to work with colleagues with other sets of learning and operatingstyles. Groupwork, in mixed teams, can support both of these dimensions. Inparticular, engineering students, with a high preference for a logical learningstyle, with a single ‘correct’ answer, may benefit from tasks which make use ofmultiple approaches, and for which the ‘solution’ is more ‘open’. This enables arange of interpretation, and is open to valuing input from many culturalperspectives.

Conclusion

The analysis of results of the MLSQ has been addressed, and normalisation ofscores has been applied to achieve a clearer and stronger picture of learning styledifferences between cohorts of students. Significant differences have been found,with engineering students in Manchester clearly preferring logical and social learn-ing styles over visual and verbal styles more strongly than their business peers,whose preference for a social learning style over a logical learning style was signifi-cantly stronger. Petronas Engineering students were found to prefer a solitary styleof learning more than their Manchester counterparts.

Further research across a wider range of students, either within one university, orinternationally, would add value and depth to these findings. Specifically, analysis ofMLSQ scores from students across all disciplines would enable consideration of theweighting of scores between learning styles, and would allow normalisation in orderto compare students’ preferences without this apparent bias factor. However, thisshould be done with care, as cultural differences would be likely to deliver differentbalances of style preferences for cohorts from different regions, and normalisation

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should be undertaken in the knowledge of the cohort against which scores arenormalised.

The diversity of learning style preferences found in the study, and echoed in theliterature, strongly indicates a need for a variety of teaching styles to be employedin working with these undergraduates. Use of a mixture of group learning tasks withopen-ended questions is advocated, to better engage students across a range of learn-ing style preferences, as well as to enhance their subsequent employability andprofessional skills.

Notes on contributorsFrances Hill was a project officer for the study. She has acted as a group facilitator for thecourse unit on Project Managing Humanitarian Aid and is also a senior lecturer at the Centrefor Alternative Technology, Machynlleth. Her research interests include energy demandreduction in buildings, consumer impacts on savings from renewable technologies and stu-dent learning style preferences.

Bland Tomkinson is a visiting lecturer who designed and implemented the Project ManagingHumanitarian Aid course unit. Previously, he was the University Adviser on PedagogicDevelopment. His research interests include assessment, change management, learning styles,PBL, sustainability and trans-national learning. He has published widely on these and othertopics.

Anna Hiley is an architect who worked in the construction industry for over 20 years. She isa lecturer and deputy director of the MSc programme in Management of Projects. Herresearch interests are in pedagogic research, focusing on the development of interactive meth-ods to ‘teach’ the design process, and the fostering of high level transferable skills, such ascreative problem-solving.

Helen Dobson is a lecturer in the Management of Projects. She was the leader of the courseunits on Interdisciplinary Sustainable Development and on Skills for Sustainability and SocialResponsibility. Her pedagogic research interests mainly focus on the engagement of studentsin experiential and practice-learning, interdisciplinary teamwork, problem-based learning,reflective practice and key employability skills.

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