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European Journal of Engineering EducationPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/ceee20

Matching Cognitive Styles to Computer-basedInstruction: An Approach for Enhanced Learning inElectrical EngineeringWAGEEH W. BOLES , HITENDRA PILLAY & LEONARD RAJPublished online: 05 Apr 2007.

To cite this article: WAGEEH W. BOLES , HITENDRA PILLAY & LEONARD RAJ (1999) Matching Cognitive Styles to Computer-based Instruction: An Approach for Enhanced Learning in Electrical Engineering, European Journal of Engineering Education,24:4, 371-383, DOI: 10.1080/03043799908923572

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European Journal of Engineering Education, Vol. 24, No.4, 1999 371

Matching Cognitive Styles to Computer-basedInstruction: An Approach for Enhanced Learning inElectrical Engineering

WAGEEH W. BOLES, HITENDRA PIlLAY & LEONARD RAJ

SUMMARY A n approach aimed at enhancing learning by matching individual students'preferred cognitive styles to computer-based instructional (CBI) material is presented. Thisapproach was used in teaching some components of a third-year unit in an electricalengineering course at the Queensland University of Technology. Cognitive style characteristicsof perceiving and processing information were considered. The bimodal nature of cognitivestyles (analytic/imager, analytic/uerbalizer, wholistlimagerand wholistlverbalizer) was exam­ined in order to assess the full ramification of cognitive styles on learning. In a quasi­experimentalformat, students' cognitive styleswere analysed by cognitive style analysis (CSA)software. On the basis of the CSA results the system defaulted students to either matched ormismatched CBI material. The consistently betterperformance by the matched group suggestspotential for further investigations where the limitations cited in this paper are eliminated.Analysing the differences between cognitive styles on individual test tasks also suggests thatcertain test tasks may better suit certain cognitive styles.

1. Introduction

Computer-based instruction (CBI) is becoming an integral part of our teaching andlearning process and a dominant educational delivery system in many parts of the worlddue to the continued innovations in multimedia technology and programming software[1]. However, despite the popularity of cm material there is a gap in understanding therelationship between multimedia and learning. Kozma [2] argues that any understand­ing of this relationship must be grounded in cognitive and social processes associatedwith knowledge construction. Owing to a lack of an understanding of the relationshipbetween learning and multimedia, the incentive for developing CBI packages has beenthe innovative alternative in information technology for presenting instructional ma­terial rather than learning theories [3]. Hence, Hedberg et al. [4] have called for a closerexamination of ways in which information is accessed and processed in current learningpackages as a basis for designing CBI packages for effective learning outcomes.

Many available CBI packages claim advantages such as providing increased accessi­bility, immediate feedback, interactive learning and a more flexible learning environ­ment. Many of these considerations are often dealt with in a physical sense; they fail toaddress cognitive aspects. For example, flexibility and learner control is seen as anoption for the learner to select what and when to learn [5]. Such options do not allowfor cognitive flexibility where individuals can choose instructional formats that are

0304-3797/99/040371-13 © 1999 European Society for Engineering Education

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congruent to their preferred way of perceiving and processing information. Similarly,navigation within CHI is often not aligned to cognitive processes associated withmaking connections between the learning processes and the content, or concerned withthe impact of different navigation systems on cognitive capacity and how it affectsindividuals' ability to access and process information [4]. It is argued that many of theseperceived benefits may in fact be better achieved through traditional methods ratherthan contrived use of technology-based learning environments [6,7]. Given the lack ofsufficient research on the role of cognition and learning in the design of CBI material,there are two related issues that are of interest to this paper. The first issue is the effectof individuals' preferred cognitive styles on learning and the second is concerned withcognitive load effects of reconciling CBI material that is incongruent with theirpreferred cognitive styles when learning.

2. Learning and Preferred Cognitive Styles

Learning is a constructive, cognitive and social process where the learner strategicallymanages the available cognitive, physical and social resources to construct knowledge[8]. Such construction requires individuals to direct attention to relevant aspects of thegiven information and relate it to previous experiences and knowledge, that is, trans­form the information. Individuals access and process information differently, hence thesuccess of any transformation process depends upon opportunities an individual has toaccess and process information in their preferred styles.

Preferred cognitive style is an individual's characteristic and a consistent approachto organizing and processing information. Keefe [9] defined cognitive style as"characteristic cognitive, affective, and physiological behaviours that serve as relativelystable indicators of how learners perceive, interact with, and respond to the learningenvironment". Many researchers investigated cognitive styles, resulting in a myriad oftheories and cognitive style types. A comprehensive analysis of the various labels,descriptors, classifications and methods of assessment by Riding and Cheema [10] ledto the formation of two principal cognitive style groups: the wholist-analytic (WA) andthe verbal-imagery (VI) dimensions. The WA continuum represents the manner inwhich an individual processes information, either in whole or in parts, whereas the VIdimension represents individuals who are inclined to represent information duringthinking verbally or in mental images. The two dimensions are independent of eachother in that the position of individuals on the WA dimension does not affect theirposition on the VI dimension. Individuals can have a single cognitive style or bebimodal, which is how the majority of people are. Bimodal groups are wholistlverbaliser(WV), wholistlimager (WI), analytic/verbalizer (AV) and analytic/imager (AI).

Evidence from research on the effect of cognitive styles on learning suggests thatcognitive style characteristics such as perception and processing of information enhancelearning outcomes [11-14]. Although most of these studies were conducted usingconventional instruction, it is plausible to assume that similar outcomes may eventuatewhen using CBI. The above studies argue that optimum learning outcomes areobtained when the instructional material can be transferred readily to learners' personalmodes of representation. More specifically, studies investigating an individual's positionon the WA dimension found that it affected .reading performance [15], learning fromstructured material [II] and occupational stress [16].

The recognition of individual learning preferences is increasingly becoming animportant consideration for designing and delivering instruction [II, 17, 18]. Ifindividuals have their own habitual ways of perceiving, representing and structuring

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Matching Cognitive Styles to CBl 373

information for learning, then "identifying a student's style ard providing instructionconsistent with that style contributes to more effective learning" [18]. Even thoughthere is a general acceptance by educators that students differ in how they perceive andprocess information and attempts to cater for it can be seen in the more traditionalinstruction, it has not gained the same recognition in the design of CEI material [19].

3. Designing CBI for Preferred Cognitive Styles

In designing instruction to accommodate preferred cognitive styles, two principal pointsare considered. First, the manner in which the instruction is formatted to allow easyperception of issues presented in given information. The second point to consider ishow individuals process information.

3.1 Perception of Instructional Material

Perception of instructional material involves the use of graphical images and/or verbal(text) information. This VI mix may affect the modes in which individuals representinformation during thinking. They may use mental images to represent given infor­mation or use verbal representations, as thoughts can be articulated in words orpictures. Verbalizers prefer information presented as words or verbal associations,whereas imagers represent information better with mental pictures of given infor­mation. The design of each CBI screen should reflect the preference of the cognitivestyle being catered for. The ratio of text to graphics, the nature of the text informationand the content of the information being presented need to be considered. Very vividnarrative text would suit the imagers, whereas a list of points would suit verbalizers.Procedural information is often serial and thus better presented in point form, whereasconceptual knowledge may require information from multiple sources. Similarly, thenature of images, because they often are not complete in themselves, requires additionalinformation, often in text form, to make them meaningful. It must be noted that it isnot a case of either/or in each of these situations, but of emphasis.

Recent developments in information technology allow the construction of realisticimages with simulated movements that can also incorporate overlays. Creative use ofthe technology, such as the use of layers progressively to build an image, can be aneffective method oflearning from images. Images that are poorly designed often requiresupplementary text information. Such images may not benefit the imagers. Similarly,the formatting of text information can also affect its benefit to verbalizers. There is anumber of options, such as bullet points, narrative prose and voice interactive systems.

Accessibility is also influenced by the nature of subject content [12). There arecertain types of content that are lent more favourably to certain cognitive styles. Forexample, imagine learning architecture by reading text material only. The spatial natureof the subject content will force individuals to engage in imagery to appreciate fullyspatial concepts in architecture. Verbalizers cope better with understanding and recallfrom prose passages, which may contain unfamiliar information, whereas imagers learnbest from passages with few unfamiliar terms and which are descriptive and illustrated[15]. Even the design of computer software menus has potential for catering for imagersand verbalizers. The use of icons may benefit the imagers, whereas word commandsmay benefit the verbalizers. Hence, the design of an optimum instructional material to

cater for either the verbalizer or the imager can be very involved.

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3.2 Processing Information

Individuals who are wholists tend to organize information into loosely clustered wholesso as to construct an overall understanding of the given information. By contrast,analytics tend to process information in clear-cut conceptual groupings and often focuson one of these groupings at a time [20]. They have the schema to integrate andconstruct meaning by first understanding the various sub-groups and then combiningthem to obtain a big picture. Strengths for wholists include their ability to see the 'bigpicture' of a situation and therefore have a balanced view of the given information.However, if wholists direct most of their cognitive resources to finding links betweenthe various elements of the instruction, then they will not have sufficient resources tolearn the whole picture. The down side for wholists is that they often find it difficult tobecome analytical and separate situations into parts. Analytics can decompose prob­lems into separate parts and may quickly diagnose a problem but they may not be ableto develop a big picture of the problem, that is, synthesize information. Ausubel [21]recognized that some individuals need to have an overview to assist them in theirlearning. Consequently, he developed the use of advance organizers. Advance organiz­ers can be images or text, but it is the manner in which the information is structuredthat assists the wholist and analytic to process information effectively. Satterly andTelfer [22] provided evidence that the use of advance organizers helped wholists todevelop a big picture of the given information, rather than having to engage in searchand construction processes from unfamiliarly structured information. Such a proceduremay not benefit the analytic-style person, who seeks detailed and highly structuredinformation to conceptualize [23]. For the analytics, consideration must be given tohow the information is broken down to spread it over a number of screens and finallypull it together as a unit of information. Maintaining information from a number ofscreens can be cognitively demanding, thus consideration must be given to the com­plexity and the amount of information that is spread over a number of screens or layers.Satterly and Telfer [22] examined various advance organizers and found that a carefuldesign of advance organizers may help different cognitive styles. They proposed threetypes of advance organizers: the linker, integrator and the analyser.

4. Research Design

Since we know from research that a 'preferred cognitive style' exists, then matching thestyle with the instructional format may enhance learning [11, 24]. If students can accessinformation in a format that matches their cognitive style, then the need to reorganizein accordance with their preferred style prior to learning is not necessary. The elimin­ation of this step in information processing presumably reduces the cognitive loadimposed by the task and enhances performance [25, 26].

The study reported here was designed to investigate the effect of matching preferredcognitive styles to instructional format on learning outcomes. It adopted a quasi-exper­imental design involving four groups of students: WI, WV, AI and AV. Each group waspresented with either matched or mismatched instructional material that was developedto cater for each of the four cognitive styles as identified by Riding and Mathias [15].This resulted in eight treatment groups. Considerations for the design of instructionalformats consisted of the use of advance organizers, text plus diagrams versus text plusspreading information across a number of screens, descriptive prose versus bullet pointsof specific information, and integrated diagrams and diagrams of discrete parts. Themore complex multimedia technology, such as voice and video integrated systems, was

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first session

CSACognitive Style Assessment

Matching Cognitive Styles to CBl 375

Students login

otne:sessions

Defaultedto respective GSAformat

FIG. 1. Format and presentation of the CBI material (rn, matched, mm, mismatched).

not possible due to funding limitations. This study included four sessions over 4 weeks.This would provide learners with sufficient time to respond meaningfully to thetreatment that in rum would allow reliable conclusions to be made. The mismatchedgroups were presented with their opposite cognitive style. For example, the oppositecognitive style for an AV was a WI. The instructional materials were developed inaccordance with Riding and Mathias's four cognitive styles [I5].

All students were assessed for their preferred cognitive style using the cognitive styleassessment software (CSA) [15]. CSA works on the basis of response times to a batteryof statements which are categorized into subsets, and a ratio for each subset iscalculated. The first subset measures the VI dimension by asking conceptual andappearance recognition questions. The other two subsets in the CSA assess the WAdimension. The first of these two subsets involves judging overall similarity of complexgeometrical shapes. The second subset requires a degree of disembedding of simpleshapes within complex geometrical figures. A detailed discussion of the rationale forCSA design can be found in Riding and Cheema [10] and Riding and Douglas [12].On the WA continuum a score equal to or greater than 1.35 was considered wholist anda score of less than or equal to 1.03 was categorized as analytic, with those falling inbetween 1.03 and 1.35 as intermediate. On the VI continuum, verbalizers were thosewith scores equal to or less than 0.99 and imagers were those who scored equal to ormore than 1.09. The intermediate group for the VI continuum fell in between 0.99 and1.09. The intermediate groups are those that cannot be definitely classified into one of

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376 W W Boles et al.

the four bimodal cognitive styles. They were not included in the study. Figure Iillustrates the organization of the study.

In accordance with the literature, it was hypothesized that students who receivedmismatched instruction would engage in extraneous search and reorganization pro­cesses, resulting in poor performance. Students' overall performance and performanceon the four individual test tasks for matched and mismatched instructional materialwere compared. The total time taken to accomplish the tasks was also recorded forcomparison. The second analysis was designed to investigate ifthe nature of the subjectcontent favoured any particular cognitive style (see [12]). The final analysis investigatedwhether there was any relationship between the nature of the test tasks and cognitivestyles.

4.J The Sample

One hundred and thirty-four undergraduate students who were enrolled in the digitalcommunications subject participated in the study. The sample size reported includesonly those students who had completed the task fully. Some students did not attend allCBI sessions. The students were allocated to four groups, WI, WV, AI and AV, basedon their preferred cognitive styles.

4.2 Material

Riding and Mathias's CSA software [15] was used to determine students' cogmtrvelearning styles. CBIs, corresponding to each of the four cognitive learning styles, weredeveloped for four specific topics: Module-I, Synchronization; Module-2, Multiplexingand Multiple Access; Module-3, Spread-spectrum Techniques; and Module-4, Lay­ered Protocols. One topic was presented each week over a 4-week period. All instruc­tional formats on each topic had the same subject content but varied in presentationstyle. For example, WI received a complete presentation of the different configurationsof the multiple access algorithm (MAA), a comprehensive diagram and some textinformation regarding the functions of the various components. AI were presented withthree separate screens containing the same information, which also included diagram­matic depictions, but they were presented as separate aspects of the MAA rather thanthe overall integrated view, which was the format of the WI lesson. Thus, studentsviewed step by step, rather than a complete piece of information. The WV and AV,because they represent information in words during thinking [23], were presented withtext format containing the equivalent information. The nature of the text informationalso varied in being either descriptive prose or just bullet points. Descriptive instructionallows individuals to construct whole pictures, whereas bullet points present details ofspecific points. Thus, consideration of the requirements of each cognitive style is clearlyevident in each lesson.

Figure 2 shows examples of how the materials were prepared to respond to therequirements of the different cognitive styles. It shows screen shots of a subsection fromModule-2, which addresses multiplexing and multiple access [27]. For both the AV andAI styles, the material was spread over three screens. The same material was presentedin one screen for both the WV and WI styles. Spreading the material over a number ofscreens responds to the analytic dimension, while concentrating the same material onone screen suits the wholist style. The use of either text only or text and graphics wasdone according to the verbalizer or imager styles, respectively.

The instructional material had a learning phase and a test phase. The learning phase

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Matching Cognitive Styles to CBl 377

consisted of an average of 25 screens of instructional material which students couldmove through backwards and forwards as they wished. The test phase consisted of tasksin recall, labelling, explanation and problem-solving. Scores in each task, as well as thetime taken for the learning and test phases, were recorded. The test tasks are:

(l) Recall: This consisted of five questions in each session and it required studentsto recall the words which linked individual parts to overall concepts.

(2) Labelling: This task required students to fill in missing blanks on a givendiagram. Each session had five missing items that students had to fill in.

(3) Explanation: This was a single question in each session. It required students torecall explanatory type of information.

(4) Problem-solving: This was also a single question in each session. A hypotheticalscenario was presented to students and they had to analyse the given situation,and design and compute a solution.

The CSA program and the instructional material were installed on the universitynetwork to allow the use of five computer laboratories simultaneously to conduct theexperiment. The test data and the time taken to complete the task were recorded eachweek and stored on a secure database within the university network system, which isinaccessible to students.

4.3 Procedure

Students were tested during their normal lecture times. A typical lecture is 3 hourslong. After a 2-hour lecture, students were asked to study the remainder of the subjectcontent via CBI. They were informed about the process of working through the CSAfollowed by the lesson and the test tasks. Following the introduction, students wereasked to log on to the system. At the log-on screen they entered their identificationnumber, age and gender. Upon entering their ID number during the introductorysession, they were presented with the CSA software to measure their 'preferredcognitive style'. The CSA took approximately 15 minutes, and depending on the result,students were defaulted to their respective instructional material. Students' CSA resultswere saved in a file with their ID number, thus for subsequent sessions on entering theID number the computer identified their preferred styles. The computer was pro­grammed to alternate between matched and mismatched instructional material whenallocating instructional material, giving no control to students on the choice of instruc­tional material. Students studied the lesson, taking as long as they needed, navigatingbackwards and forwards through the lesson. When they were satisfied with theirlearning they then proceeded to the test phase. Once they had exited the test phase theycould not return to the instructional material. This stopped them finding answers bycross-referencing with the instructional material. Responses to test items were recordedfor each student and for each sub-task. The whole process took approximately 70minutes per session. Students had to participate in four sessions, one every week over4 weeks.

S. Results and Discussion

A two-factor non-orthogonal design using an analysis of unweighted means was used toanalyse the data (28). This analysis was necessary due to the presence of unequalsample sizes.

In what follows, the two main parameters F(a,b) and P will be used to describe and

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378 W W Boles et al.

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FIG. 2. Sample technical material prepared in the different cognitive styles: (a) AV. (b)AI. For both the AV and AI styles, the material is spread over three screens. (c) WV. (d)WI. For both of the WV and WI styles, the same material is presented in one screen.

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Matching Cognitive Styles to CBI 379

analyse the data, where a and b are the degrees of freedom and the sample size lese thedegrees of freedom, respectively. The F value is a ratio that indicates how different thegroups are that are being compared. In computing this statistic, variance betweengroups and within groups is considered. For any effect caused by the treatment (e.g.matched or mismatched instruction) an F value greater than 1 is necessary. However,often F values can be greater than 1 and yet not be significant enough to make anygeneral claims. Thus, there is a need to establish a significance level, which isrepresented by the Pvalue. This value indicates the probability for a particular outcomeof an experiment to occur at a certain level. In the case of this study it was at the 95thpercentile, P= 0.05. For example, when comparing two groups (with a total of 100participants), an F(I,99) = 2.5 means that this F value was obtained for one degree offreedom, a = I, and b = 99 (which is the sample size less the degrees of freedom). If weconclude that there was no significant difference between the two groups, it means thatthe difference between the two groups, the F value of 2.5, was not large enough to beconsidered statistically significant at the P = 0.05 level.

We first investigated the effect of matching/mismatching students to their cognitivestyles. We then focused on comparing student performance based on their learningstyles. For the first study, students' scores on test tasks for matched and mismatchedcognitive style groups were compared. There was no significant difference between thetwo groups on total scores F(l ,119) = 2.795, P =0.05. Comparing scores for matchedand mismatched groups on individual sub-tasks also revealed no significant difference,however the mean score of the matched group on all sub-tasks was consistently betterthan that of the mismatched group. It is also interesting to note that this performancewas achieved in less time than for the mismatched group. The mean time taken over thefour CBI sessions for the matched group was 265.4 minutes, compared to 283.3minutes for the mismatched group. Hence, it may be plausible to suggest that thereappears to be a pattern (although not significant) in favour of the matched group.

While there was no significant difference between the matched and mismatchedgroups, our second study (comparing student performance based on their learningstyles) showed that a significant difference was found to exist between the four learningstyles F(3,119) = 4.450, P= 0.05. In considering the mean scores of each cognitivestyle, it is evident that the WV group performed better than all other groups. There wasno significant interaction between the different cognitive styles and the matched andmismatched treatments F(3, 119) = 0.979, P= 0.05.

An analysis of the effect of cognitive learning styles on each test task is summarizedin Table 1. A one-way analysis of variance indicated a significant difference between thestyles on: recall F(3,126) = 3.659, P= 0.05; labelling F(3,126) = 3.628, P= 0.05; andexplanation F(3,126) = 3.438, P= 0.05. The difference was not significant on problem­solving, F(3,126) = 2.58, P= 0.05. It appears that certain test tasks were favoured bycertain cognitive styles. Considering the mean scores of each cognitive style it is evidentthat the WV group performed best on the test tasks followed by the AI group.

The lack of any significant difference between the matched and mismatched groupsmay be due to a combination of factors. It is plausible to argue that the lecture sessionspresented just prior to the CBI experiment could have influenced the result. Theexperimental design had no control over the lecture session that was necessary for thecourse requirements stipulated by the University. The other consideration was the needto make the instructional material not too different as it was considered iniquitous,hence opening social justice debates within the university. It would be better if a wholenon-credit course could be identified so that the problems of assessment and equity donot limit the design of instructional material. Perhaps a more robust design dealing with

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TAB

LE1.

Mea

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tage

scor

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test

task

s,le

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dto

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tim

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ts('!

o)('!

o)('!

o)('!

o)('!

o)(m

in)

(min

)(m

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(n)

Mat

ched

inst

ruct

iona

lfor

mat

AI

72.3

80.8

80.6

58.5

54.6

189.

710

0.2

189.

920

AV

64.9

74.5

72.3

54.8

43.3

177.

592

.126

9.6

20W

I67

.277

.777

.149

.748

.814

8.6

77.7

226.

315

WV

73.6

88.8

81.1

63.1

58.4

183.

492

.727

6.1

12T

ota

l69

.279

.077

.456

.350

.617

5.7

91.4

265.

467

Mis

mat

ched

inst

ruct

iona

lfo

rmat

AI

67.6

79.0

77.8

50.5

49.8

200.

510

2.3

302.

821

AV

64.3

77.7

72.9

48.5

41.3

164.

610

4.5

269.

115

WI

56.2

63.4

63.0

47.4

36.2

180.

310

0.3

280.

616

WV

73.2

79.6

82.0

60.1

45.6

182.

498

.428

0.8

8T

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l64

.574

.973

.250

.543

.518

2.0

101.

828

3.3

60

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Matching Cognitive Styles to CEI 381

complete topics rather than part of a topic and free of pol'cy implications, and usingmultimedia (audio, video, text, diagrams), would result in larger differences betweenthe matched and mismatched groups, reaching significant levels. Support for the abovecan be seen in Liu and Reed [29], who argue that hypermedia learning environmentsmay greatly enhance the potential to accommodate individual learning preference.Nevertheless, the results of each sub-task consistently showed enhanced performanceby the matched group, achieving it in less time. This suggests potential for furtherinvestigation.

As cited by Riding et al. [30], the manner in which we perceive and processinformation can be influenced by the subject content. The subject 'digital communica­tions' required students not only to understand the details involved in each of the fourtopics but also to have an overview of how the details fit together within each topic andwith other topics. Subject contents s~gPIt relational understanding. Such learningfacilitates the transfer of knowledge to applications, which is what.the problem-solvingtask in this study tested. The results suggest that the WV performed better than theother cognitive styles. This is, perhaps, due to the WV advance organizers beingpresented as a single unit of information, which assisted students to read the detailedexplanations and make relational links between concepts and prior knowledge. Theexplanatory value of the diagrams used in the imager instruction must have been low[31]. The subject content required students to be able to identify the various compo­nents, understand their functions and reason through the protocol necessary to designsystems. Thus, the findings of this study, to some extent, support Riding and Douglas'sargument that subject types have an affinity to certain cognitive styles and acknowledgethe need to consider the nature of the subject matter in designing personalized CBIinstruction [30].

The analysis of test tasks, in this study, showed a significant difference between thecognitive style groups and the test tasks, suggesting that certain cognitive styles may suitcertain types of test tasks. This finding is also consistent with Riding and Calvey's [23]argument that the effect of cognitive style on performance depends on the nature of thetask. On recall tasks the WV and AI did better than WI and AV. This may be becausethe recall tasks required words that connected parts of information to the overallconcepts. Such words are easily recognized where the instruction provides opportuni­ties to see the overall concept as well as the details as found in WV and AI. The WVand the AI groups, in fact, have consistently performed better than the other groups.This may be due to the design of the instructional material, which influenced the typeof learning outcomes desired (analytic/quantitative or wholistlqualitative). The designof the problem task also favoured the WV because it concentrated on verbal answersand those that required an overall understanding rather than any detailed analysis.

6. Conclusions

This paper has presented a study aimed at enhancing learning by matching individualstudents' preferred cognitive styles to CBI material. The study found that there may bepotential for further research in considering personalizing CEl material. It also concurswith previous findings that subject content may have an affinity to certain cognitivestyles. This prompts research into the nature of subject content: What makes a subjectmore or less suited to certain cognitive styles? Although the CEl in this study usedlinear presentation, there are more possibilities for improving the CBI material, such asthe use of the voice, which may assist the verbalizers, animations, which may help theimagers and 'hotspots', for analytics who may wish to seek detailed information. The

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design of many f:BI materials can benefit greatly by considering the needs for personallearning styles. Further investigations, including student interviews and adding moreflexibility by allowing students to choose the presented materials according to their ownopinions, are currently being considered.

Acknowledgement

This study was made possible through funding as Queensland University of Technol­ogy Teaching and Learning and Meritorious grants.

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