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Mining students' learning patterns and performance in Web-basedinstruction: a cognitive style approachSherry Y. Chena; Xiaohui Liuaa School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge,Middlesex, UK

First published on: 14 March 2009

To cite this Article Chen, Sherry Y. and Liu, Xiaohui(2011) 'Mining students' learning patterns and performance in Web-based instruction: a cognitive style approach', Interactive Learning Environments, 19: 2, 179 192, First published on: 14March 2009 (iFirst)To link to this Article: DOI: 10.1080/10494820802667256URL: http://dx.doi.org/10.1080/10494820802667256

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Mining students learning patterns and performance in Web-basedinstruction: a cognitive style approach

Sherry Y. Chen* and Xiaohui Liu

School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge,Middlesex UB8 3PH, UK

(Received 25 August 2008; nal version received 14 November 2008)

Personalization has been widely used in Web-based instruction (WBI). To delivereective personalization, there is a need to understand dierent preferences ofeach student. Cognitive style has been identied as one of the most pertinentfactors that aect students learning preferences. Therefore, it is essential toinvestigate how learners with dierent cognitive styles interact with WBIprograms. This paper presents an empirical study, which examines the eects ofcognitive styles on students learning patterns and the eects of learning patternson their learning performances. Ridings cognitive style analysis was used toidentify the students cognitive styles. Data mining, especially a clusteringtechnique, was used to analyze the results. It was found that eld independentstudents frequently used an alphabetical index whereas eld dependent studentsoften chose a hierarchical map. Such learning patterns also have great eects ontheir performance, especially for eld dependent students.

Keywords: cognitive styles; data mining; Web-based instruction

1. Introduction

Web-based instruction (WBI) has become increasingly attractive in educationalsettings and will remain predominant for the delivery of learning material in thefuture (Liu, Chiang, & Huang, 2007). The reason for such popularity is that the WBIoers many advantages over traditional classroom-based training, such as providingremote access from everywhere (Anido, Llamas, & Fernandez, 2001). Thus, learnersfrom dierent places can read course contents through a computer network (Changet al., 1998). On the one hand, such exibility provides much convenience to learners.On the other hand, there is great diversity among learners who may come fromheterogeneous backgrounds, in terms of their knowledge, skills and needs (Chen &Macredie, 2004).

Because of such diversity, research into individual dierences has mushroomed inthe past decade. In particular, cognitive style has been identied as one of the mostpertinent individual dierence elements, because it refers to a persons information

*Corresponding author. Email: sherry.chen@brunel.ac.uk

Interactive Learning Environments

Vol. 19, No. 2, March 2011, 179192

ISSN 1049-4820 print/ISSN 1744-5191 online

2011 Taylor & FrancisDOI: 10.1080/10494820802667256

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processing habits, capturing an individuals preferred mode of perceiving, thinking,remembering and problem solving (Messick, 1976). It has also been suggested thatmatching cognitive styles to the design of instructional programs can lead to betterlearning performance (Ford & Chen, 2001). In this vein, the study reported in thispaper aims to examine the inuences of cognitive styles on student learning.To obtain a complete understanding, both learning patterns and learningperformance will be investigated in this study. By doing so, a clear picture can beconstructed to illustrate whether their learning patterns are suitable for them. To thisend, two research questions were investigated: (a) what are the eects of cognitivestyles on the students learning patterns and (b) what are the eects of the studentslearning patterns on learning performance. Answers to these two questions aresought by using a data mining approach to analyze students learning patterns,because data mining has been successfully applied in many elds to help discoverknowledge and make decisions, including bioinformatics (Liu & Kellam, 2003),nancial analysis (Thawornwong & Enke, 2004) and search engines (Zhang & Dong,2002).

The paper is structured as follows. Section 2 presents research rationales byanalyzing the problems of existing work in the eld. Section 3 describes themethodology used to conduct the empirical study and the techniques applied to theanalysis of the corresponding data. Subsequently, the grouped students learningpatterns are presented in Section 4, where the eects of cognitive styles on studentslearning patterns and the relationships between learning patterns and learningperformance are discussed. The paper then proceeds to Section 5, which discusses theimprovement of the design of WBI and other Web-based applications using thendings of this study. Finally, conclusions are drawn and future work is identied inSection 6.

2. Research rationales

The Internet, especially the Web, was designed as an information space (Oberleet al., 2005), where everyone can access information. Each individual has dierentpreferences for content delivery. In recent years, personalization, which tailorscontent, structure and/or presentation to match the unique and specic needs of eachindividual (Fink & Kobsa, 2000), has been an increasingly popular approach on theWeb. In particular, it has been widely used in WBI (Kobsa, Koenemann, & Pohl,2001). In the past, personalized WBI programs mainly focused on a learners priorknowledge (Surjono &Maltby, 2003). More recent personalized WBI programs havebeen made to try to accommodate the preferences of learners with dierent cognitivestyles (Mitchell, Chen, & Macredie, 2005). However, the delivery of personalizationis complex because the adaptation to each individual requires the understanding oftheir preferences (Ardissono et al., 2005) and prediction of their behavior(Deshpande & Karypis, 2004). Therefore, it is necessary to investigate how learnerswith dierent cognitive styles interact with WBI programs, so that eectivepersonalization can be provided.

Within the area of cognitive styles, eld dependence has emerged as one of themost widely studied dimensions with the broadest application to problems ineducation (Messick, 1976), because it reects how well a learner is able torestructure information based on the use of salient cues and eld arrangement(Weller, Repman, & Rooze, 1994). The key issue of eld dependence lies within the

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dierences between eld dependent and eld independent learners, which arepresented below:

. Field independent learners: the individuals tend to exhibit more individualisticbehaviors because they are not in need of external referents to aide in theprocessing of information. They are more capable of developing their owninternal referents and restructuring their knowledge, are better at learningimpersonal abstract material, are not easily inuenced by others and are notoverly aected by the approval or disapproval of the superiors.

. Field dependent learners: the individuals are considered to have a more socialorientation than eld independent persons, because they are more likely tomake use of externally developed social frameworks. They tend to seek outexternal referents for processing and structuring their information, are betterat learning materials with human contents, are more readily inuenced by theopinions of others and are aected by the approval or disapproval of authoritygures (Witkin et al., 1977).

Several studies investigate the relationships between the degree of elddependence and students learning patterns and learning performance. In terms oflearning patterns, Wang, Hawk, and Tenopir (2000) found that students with strongeld dependence tendency more easily got confused on the Web than those withstrong eld independence tendencies. Similar results were obtained by Chen andMacredie (2004), which showed that eld independent students appreciated the factthat WBI programs allowed them to study topics in any order, whereas elddependent students felt confused over which options they should choose. Regardinglearning performance, Cacciamani (2002) found that eld independent studentsoutperformed eld dependent students in learning from WBI. Ghinea and Chen(2003) showed that eld dependent individuals performance was hindered bymultimedia tools that required the students to extract cues by themselves.

The aforementioned studies indicated that cognitive style plays an essential rolein students learning patterns and learning performance in WBI. However, they paidless attention to the relationships between learning patterns and learningperformance. Therefore, it is unknown whether the learning patterns used by themare advantageous or disadvantageous to their learning. To address this issue, there isa need to deepen the study of the relationships among cognitive style and learningpatterns and their impacts on learning performance. To conduct such a study thatinvolves multiple factors, traditional data analysis methods may not be very ecient.For such a task, the use of data mining may be more appropriate because datamining can discover hidden relationships and generate rules for the prediction ofcorrelations (Gargano & Ragged, 1999). Much of the work in data mining can bedivided into three major categories based on the nature of the informationextraction: clustering, classication and association rules (Chen & Liu, 2004).Clustering, a major exploratory data analysis method (Tukey, 1977), is concernedwith the division of data into groups of similar objects. Each group, called a cluster,consists of objects that are similar to each other and dissimilar from objects in othergroups (Roussinov & Zhao, 2003). For instance, Wang et al. (2004) have developed arecommendation system for the cosmetic business. In the system, they segmented thecustomers by using clustering algorithms to discover dierent behavior groups, sothat customers in same group would have similar purchasing behavior. Classication

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refers to the data mining problem of attempting to discover predictive patterns wherea predicted attribute is nominal or categorical. The predicted attribute is called theclass. Subsequently, a data item is assigned to one of a predened set of classes byexamining its attributes (Changchien & Lu, 2001). In other words, the objective ofclassication is not to explore the data to discover interesting segments, but rather todecide how new items should be classied. For example, Esposito, Licchelli, andSemeraro (2004) built student models for an e-learning system based on the studentslevel of performance: good, sucient or insucient. Association rules that were rstproposed by Agrawal and Srikant (1994) are mainly used to nd out the meaningfulrelationships between items or features that occur synchronously in databases (Wu,Zhang, & Zhang, 2002). This approach is useful when one has an idea of dierentassociations that are being sought out. This is because one can nd all kinds ofcorrelations in a large data set. For instance, Cunningham and Frank (1999) appliedassociation rules to the task of detecting subject categories that co-occur intransaction records of books borrowed from a university library. As shown by theaforementioned studies, data mining opens new capabilities and opportunities fordata analyzes, and so this study will adopt a data mining approach, especially usingclustering to analyze students learning patterns and learning performance in aWBI program.

3. Methodology design

3.1. Research design

Sixty-ve learners participated in the empirical study. Participants were under-graduate students in a UK university and they volunteered to take part in theexperiments. A request was issued to students in lectures, and further by email,making clear the nature of the study and their participation. All participants had thebasic computing and Internet skills necessary to operate a WBI program.

The participants took part in the study on one-to-one basis, and there was nocommunication between the participants. First, the participants cognitive styleswere identied by Ridings (1991) cognitive styles analysis (CSA), because eachdegree of eld dependence is positively measured and the CSA oers computerizedadministration and scoring (Ford & Chen, 2001). The CSA measures what Ridingand Sadler-Smith (1992) refer to as a Wholist/Analytic (WA) dimension, which isequivalent to eld dependence/independence. Ridings (1991) recommendations arethat WA scores below 1.03 denote eld dependent individuals; scores of 1.36 andabove denote eld independent individuals; students scoring between 1.03 and 1.35are classed as intermediate. In this study, categorizations were based on theserecommendations.

After taking the CSA, the participants were asked to take a pre-test. This wastimed, allowing the students a maximum of 15 minutes. Subsequently, all of theparticipants interacted with the WBI programs (see Section 3.2) for about 90 minutes.This was then followed by the post-test, again with a 15-minute time limit. The pre-test and post-test were designed to assess the participants level of understanding ofthe subject content both before and after using the WBI programs. Both included 20multiple-choice questions, each with four dierent answers and a dont knowoption, from which the students could choose only one. In addition to the post-testscore, the students learning performance was also measured based on gain score,which was calculated as the post-test score minus the pre-test score.

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3.2. WBI programs

The subject content of the WBI program used in this study emphasizes the practicalskills of designing Web pages, i.e.How to Use HTML. The WBI program encouragesfree exploration of the instructional material by providing the learners with severalnavigation tools, including an alphabetical index, a hierarchical map, a main menuand section buttons. In addition, there were rich hypertext links within the text. Inthis way, the learners were able to adopt learning strategies by themselves. Figure 1illustrates the design of the WBI program.

3.3. Data analysis

Raw experimental data are in the form of Web access logs. To discover therelationships between students cognitive styles and their learning patterns, it isnecessary to extract the potentially useful information from the raw data. Therationale of selecting useful information is based on a comprehensive review by Chenand Macredie (2002), which examined the eects of cognitive styles on studentlearning. In addition to their ndings, the characteristics of the raw data and Websitestructure were also taken into account to draw the attributes that reveal studentslearning patterns in WBI. There are eight attributes in all, including the number ofpages each learner has browsed, the number of visited pages respectively describingoverviews, examples and detailed description, the number of times navigationtools such as main menu, hierarchical map and alphabetical index are used, andthe number of repeated visits the learners make while navigating to the WBIprograms.

Among three data mining techniques described in Section 2, clustering wasselected for analyzing data because it can form groups that share similarcharacteristics (Nolan, 2002). In particular, k-means was employed to producegroups of students that shared similar learning patterns. k-Means is one of the

Figure 1. The WBI program.

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simplest clustering algorithms for grouping objects with similar features. In k-means,the number of clusters, k, is xed before the algorithm runs. The algorithm randomlypicks k cluster centers, assigns each point to the cluster whose mean is closest in aEuclidean distance sense, then computes the mean vectors of the points assigned toeach cluster and uses these as new centers in an iterative approach (Hand, Mannila,& Smyth, 2001).

In detail, the rst step is to dene k centroids (centers), one for each cluster.Another parameter called seed (S) is used to generate the random numbers for theassignment of the initial centroids. Following that, the algorithm takes each datapoint and associates it to the closest centroid. The next step starts when all the datapoints are assigned to clusters; it is the re-calculation procedure of k new centroids.For these k new centroids, a new binding has to be done between the same data setpoints and the closest new centroid. These two steps are alternated until a stoppingcriterion is met, i.e., when there is no further change in the assignment of the datapoints (Evgenia, Markus, & Christine, 2004). The outcome of the algorithm revealsthe centroid or means vector of each cluster as well as statistics on the number andpercentage of instances assigned to dierent clusters. Thus, centroids can be used tocharacterize the behavior of each one of the formed clusters.

Because k-means is sensitive to how clusters are initially assigned, it is necessaryto try dierent values for the seed number S and evaluate results in order to ndwhich combination ts better the data (Bandyopadhyay & Maulik, 2002).Furthermore, a dierent combination of the abovementioned attributes was usedto evaluate results for the best performance of the algorithm. The factors adopted toevaluate the performance of the algorithm were the following: (a) the value of thesum of squared errors, (b) the percentage of clustered instances for each cluster and(c) the mode value of each attribute within the cluster. Results indicated that thealgorithm produces more ecient outcomes for k 3 clusters for both datasets.

In addition to k-means, analyzes of variance (ANOVA), which is suitable to testthe signicant dierences of three or more independent groups (Stephen & Hornby1997), was used to identify whether learning patterns, learning performance andcognitive styles were signicantly dierent among the three clusters. The detaileddierences among the three clusters were illustrated with the mean and standarddeviation of each attribute. Frequency counts and percentages were applied toexplain the distribution of each cognitive style among the three clusters.

4. Results and discussions

4.1. Overall

According to the test results from the CSA, the participants were an almost equalmix of eld independent, intermediate and eld dependent students. The WA scoresranged from 0.69 to 1.89. Table 1 presents the distribution of the sample and themean and standard deviation of the WA scores of each cognitive style.

4.2. Learning patterns

Table 2 reveals the attributes that characterize each cluster. The percentage oflearners within each cluster is satisfactory for the total number of 65 instances.Clusters can be characterized as well balanced: Cluster 1 (N 21): 32%, Cluster 2(N 22): 34% and Cluster 3 (N 22): 34%. The mean values of the above

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attributes are shown in the table, which have indicated that the learners are groupedaccording to the following trends:

. Cluster 1 (C1): learners often use the hierarchical map and access the examples,visit many pages and make a lot of repeated visits.

. Cluster 2 (C2): learners often use the alphabetical index and access the detaileddescriptions and visit some pages and make some repeated visits.

. Cluster 3 (C3): learners rarely use any navigation tools and display options,visit fewer pages and make few repeated visits.

To identify the role of cognitive styles on determining the clusters, ANOVA wasused to obtain statistical signicance of cognitive styles. The results indicate thatcognitive style (F(2,62) 4.47, p 5 0.05) was a signicant factor in determining theclusters representing students learning patterns. Figure 2 illustrates the proportionof each cognitive style group within each cluster. The majority of eld dependentlearners (N 12, 57%) appear in Cluster 1, where learners often used thehierarchical map (F(2,62) 4.98, p 5 0.05). The hierarchical map uses graphicsto give a global picture of the subject content (Nilsson & Mayer, 2002) and it isbenecial to eld dependent learners, who tend to take a holist approach thatconcentrates on building a conceptual overview. In contrast, eld independentlearners (N 13, 56%) mainly emerge in Cluster 2, where the learners visited thealphabetical index many more times (F(2,62) 6.07, p 5 0.05). The alphabeticalindex is useful for locating specic information (Chen & Macredie, 2002). Thisnding is in line with that of the study by Chen and Macredie (2004), who found thateld independent learners are more interested in using tools that could facilitate thelocation of specic information without going through a xed sequence.

Table 1. The description of the sample.

Cognitive styles Mean SD

Field independent (N 23) 1.67 0.29Intermediate (N 21) 1.15 0.19Field dependent (N 21) 0.89 0.13Overall 1.24 0.20

Table 2. Learning patterns of each cluster.

Cluster (C) Overview Example Details Menu Map Index Repeat Pages

C1 Mean 6.4 11.8 7.0 7.2 10.4 4.5 43.7 74.8SD 0.2 1.7 1.6 0.2 1.9 0.4 9.0 11.5

C2 Mean 6.0 4.8 15.3 3.8 4.1 10.5 33.0 65.0SD 1.0 0.8 1.3 0.2 0.7 1.8 7.4 9.5

C3 Mean 5.9 4.7 6.4 2.8 4.2 4.3 21.7 50.0SD 1.5 1.6 1.9 0.2 0.3 0.2 7.1 7.3

Overall Mean 6.1 7.1 9.6 4.6 6.2 6.4 32.8 63.2SD 0.9 1.3 1.6 0.2 0.9 0.8 7.8 9.4

SD Standard deviation.

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The other signicant dierence is that the learners in Cluster 1 often choose theexamples (F(2,62) 5.18, p 5 0.05) where those in Cluster 2 frequently look at thedetailed descriptions (F(2,62) 6.19, p 5 0.05). In other words, eld dependentlearners might favour to use the examples whereas eld independent learners mightprefer to examine the detailed description. It may be because eld dependent learnersrely on salient cues (Anastasi, 1988) and operate best where analyzes are alreadyprovided (Lyons-Lawrence, 1994). Examples are down-to-earth visual materials, sothat they would be useful for eld dependent learners to transfer knowledge into anactivity. On the other hand, eld independent learners have a preference for aserialist approach that concentrates primarily on procedural details when processinginformation in a learning context (Pask, 1976, 1979).

4.3. Learning performance

Table 3 shows the students learning performance of each cluster. ANOVA indicatesthat there are signicant dierences among students of the three clusters, in termsof their pre-test scores (F(2,62) 6.12, p 5 0.005) and post-test scores (F(2,62) 6.64, p 5 0.005) and gain scores (F(2,62) 5.24, p 5 0.01). In other words, theydid not have a similar level of preliminary understanding of the subject content andthey did not perform equally well after they took the WBI program. These results

Table 3. Learning performance of each cluster.

Cluster (C) Pre-test Post-test Gain score

C1 Mean 2.8 12.8 10.0SD 0.8 3.5 3.7

C2 Mean 1.0 9.0 8.0SD 0.6 1.5 1.7

C3 Mean 0.5 6.0 5.5SD 0.2 0.7 1.8

Overall Mean 1.4 9.2 7.8SD 0.2 2.7 1.7

SD Standard deviation.

Figure 2. Cognitive style in three clusters. FI eld independent; FM intermediate;FD eld dependent.

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suggest that there are close relationships between students learning patterns andtheir learning performance.

On the basis of the pre-test scores, students in Cluster 1 had the highest level ofpreliminary understanding. Likewise, the best learning performance also emerges inCluster 1, where the learners obtained the highest post-test score and gain score. Onthe other hand, students in Cluster 3 had the lowest level of preliminaryunderstanding. Furthermore, the worst learning performance also emerges inCluster 3, where the students had the lowest post-test scores and gain scores. Thesendings suggest that the students preliminary understanding does not only inuencetheir learning performance, but also has great eects on their learning patterns.

Figures 3 to 5 illustrates the scores of the pre-test and the post-test of threecognitive style groups in each cluster. According to the pre-test scores, the dierencesof the preliminary understanding of the subject content among the three cognitivestyle groups are at a low level. To examine the post-test scores, the results indicatethat eld independent students of the three clusters performed almost equally well.Regarding intermediate students, the dierences of learning performance amongthree clusters are small. However, eld dependent students showed dierent learningperformance across the three clusters (F(2,18) 3.56, p 5 0.05). It suggests that thelearning patterns did not inuence the learning performance of eld independent andintermediate students while they had signicant impacts on eld dependent students.As shown in Figure 5, the best learning performance of eld dependent students isdemonstrated in Cluster 1, where the hierarchical map was frequently used by thestudents. As discussed in Section 4.1, the hierarchical map can provide an overallpicture of the subject content, which is useful to eld dependent students who tend totake a global approach for their learning (Witkin et al., 1977). It suggests that theappropriate use of navigation tools, especially for the hierarchical map, can enhancethe performance of eld dependent students. In contrast, the worst learningperformance of eld dependent students is found in Cluster 3, where the studentsrarely use any navigation tools. In general, navigation tools can help studentsinteract with WBI programs by telling them what information is held within theprogram and helping them to quickly and easily nd the information they seek(Park & Kim, 2000). In particular, such help is important to elddependent students, because they are more reliant on salient cues in learning

Figure 3. Learning performance of eld independent students among three clusters.

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(Davis & Cochran, 1989). Thus, the absence of the appropriate use of navigationtools might hinder their learning performance.

5. Implications for Web design

As discussed in the previous section, cognitive style has a signicant eect on studentlearning within WBI. Field independent learners and eld dependent learners havedierent characteristics, which inuence their preferences in using WBI programs.The following two subsections discuss how to accommodate the dierent preferencesof eld independent learners and eld dependent learners by providing exibility andpersonalization. These design solutions can be used for the improvement of thedesign of WBI and other Web-based applications, such as digital libraries, searchengines and electronic journals.

5.1. Flexible WBI

One of the ndings in this study is that dierent navigation tools are suitable to eldindependent learners and eld dependent learners. The alphabetical index seems to

Figure 5. Learning performance of eld dependent students among three clusters.

Figure 4. Learning performance of intermediate students among three clusters.

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suit the former more, whereas the hierarchical map appears to suit the latter. Thisimplies that WBI should provide dierent types of navigation support for eachcognitive style. More specically, WBI should be exible enough to oer multipleoptions to match with the diverse needs of dierent cognitive style groups. Toaddress this issue, we propose an idea of building a exible WBI, which can providemultiple functionalities to accommodate the dierent needs of eld independentlearners and eld dependent learners with a single design (Chen, Magoulas, &Dimakopoulos, 2005). One of the ways for developing exible WBI is to allow thelearners to see both navigation tools at the same time by using multiple frames. Inother words, the alphabetical index is displayed in one frame, whereas thehierarchical map is presented in the other frame. By doing so, a learner can choosea navigation tool, which is more appropriate to him/her.

The other way is to provide the variants of an alphabetical index. Twoapproaches can be applied to design the variants of the alphabetical index. One isthat the alphabetical index presents terms in a nested structure where major termsare listed in an alphabetical order, secondary terms are alphabetically put under themajor terms and minor terms are alphabetically listed under the secondary terms.The other approach is that the major terms, secondary terms and minor terms arelisted together in an alphabetical order, but they are annotated in dierent fontstyles. For example, the major terms are made with bold style, the secondary termsare with italic style and the minor terms are with regular style. These two variants ofthe alphabetical index are able to help not only eld independent learners to locatespecic details with secondary and minor terms, but also eld dependent learners tobuild a global picture with major terms.

5.2. Personalized WBI

In addition to developing exible WBI, personalization is the other way to supportthe needs of dierent cognitive style groups. To provide a personalized WBI, it isnecessary to identify learners cognitive styles. Three approaches can be applied toidentify learners cognitive styles. One is to monitor learners navigation patternswith data mining techniques, such as decision trees or neural networks(Frias-Martinez, Chen, & Liu, 2007). Another approach is to identify their preferrednavigation patterns by conducting interview. Alternatively, external surveys, such ascognitive style analysis (Mitchell, Chen, & Macredie, 2005) or group embeddedgures test (Triantallou, Pomportsis, & Demetriadis, 2003), can be used to obtainsuch information. According to the cognitive styles identied by the aforementionedapproaches, the arrangement of navigation tools and content presentation can beautomatically tailored to match with the needs of each individual. In other words,dierent types of design will be used to support eld dependent learners and eldindependent learners.

The results of this study indicated that eld dependent learners tend to see theoverall picture and ignore the details and so the personalized WBI can provideeld dependent learners with overview diagrams, sheye views and hierarchicalmaps that show the whole picture of the context (Linard & Zeilger, 1995). On theother hand, eld independent learners tend to focus on details and to be moreanalytical in their learning patterns. Thus, the personalized WBI should supportField Independent learners with eective shortcuts. For example, an alphabeticalindex, keyword searching or other tools that can help to nd specic information

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can be applied in personalized WBI to support eld independent learners (Chen &Macredie, 2002).

6. Concluding remarks

This paper presents an empirical study, in which a data mining approach was appliedto answer two research questions. The rst research question is what are the eects ofcognitive styles on the students learning patterns? The answer to this research questionis that dierent cognitive style groups demonstrate dierent learning patterns and thelearning patterns appear to be consistent with the characteristics of the cognitive styles.Regarding the second research question, what are the eects of the students learningpatterns on learning performance? the answer to this research question is that thestudents learning patterns may have an eect on their learning performance,especially eld dependent learners. In summary, there are close relationships amongcognitive styles, learning patterns and learning performance and these three issuesshould be considered in the future improvement of WBI programs.

The empirical study described in this paper has shown the importance ofunderstanding cognitive styles in the development of WBI. However, they were onlysmall-scale studies. Further work needs to be undertaken with a larger sample toprovide additional evidence. Given any dataset, there are often no strict rules thatimpose the use of a specic method over another in its analysis. Therefore, there is aneed to analyze students learning patterns using other clustering algorithms or evenother data mining approaches, e.g. classication and association rules. It would beinteresting to see whether similar results will be found by using those methods. Inaddition, the results of such studies could be integrated to build robust user modelsfor the development of eective individualized WBI programs that can accom-modate the need of each individual learner.

Notes on contributors

Sherry Y. Chen is a Reader in the School of Information Systems, Computing and Mathematicsat Brunel University. She obtained her PhD from Department of Information Studies,University of Sheeld, UK in 2000. Her current research interests include human-computerinteraction, data mining, digital libraries, and educational technology. She has published widelyin these areas. Dr. Chen is the editor of several research books, including Adaptable andAdaptive Hypermedia Systems and Advances in Web-based Education: Personalized LearningEnvironments. She is a member of the editorial boards of seven computing journals.

Xiaohui Liu is Professor of Computing at Brunel University where he directs the Centre forIntelligent Data Analysis, conducting interdisciplinary research concerned with the eectiveanalysis of data. He was the founding chair of the international conference series on IDA(1995), a keynote speaker at the International Conference of the Royal Statistical Society(2002), and vice chair of the IEEE International Conference on Data Mining (2004). ProfessorLiu has over 200 refereed publications in data mining, bioinformatics, intelligent systems andtime series. He was appointed as Honorary Pascal Professor at Leiden University in theNetherlands in 2004.

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