cognitive styles and virtual environments

Cognitive Styles and Virtual Environments

Nigel FordDepartment of Information Studies, University of Sheffield, Western Bank, Sheffield, S10 4DP, UK.E-mail: [email protected]

Virtual environments enable a given information spaceto be traversed in different ways by different individuals,using different routes and navigation tools. However, weurgently need robust user models to enable us to opti-mize the deployment of such facilities. Research intoindividual differences suggests that the notion of cogni-tive style may be useful in this process. Many such styleshave been identified. However, it is argued that Pask’swork on holist and serialist strategies and associatedstyles of information processing are particularly prom-ising in terms of the development of adaptive informa-tion systems. These constructs are reviewed, and theirpotential utility in “real-world” situations assessed. Sug-gestions are made for ways in which they could be usedin the development of virtual environments capable ofoptimizing the stylistic strengths and complementingthe weaknesses of individual users. The role of neuralnetworks in handling the essentially fuzzy nature of usermodels is discussed. Neural networks may be useful indynamically mapping users’ navigational behavior ontouser models to enable them to generate appropriateadaptive responses. However, their learning capacitymay also be particularly useful in the process of improv-ing system performance and in the cumulative develop-ment of more robust user models.

Introduction

Virtual environments allow greater flexibility of naviga-tion than do their physical counterparts. Unlike print, virtualinformation sources enable users to take different routesthrough a given body of subject matter, and to permitdifferent levels of autonomy in so doing (from system-prescribed sequencing of information to learner-controllednavigation). The same information space may be traversedin different ways by different individuals, using differentroutes and navigation aids. Shifts in perspective on a prob-lem and alternative pathways may be instigated rapidly andrepeatedly.

Current technology allows the development of informa-tion systems that offer flexibility in terms of routes throughsubject content and a rich set of navigational tools enablingvarying levels of user and program control. However, we

urgently need robust user models to enable us to optimizethe deployment of such facilities. Research into individualdifferences suggests that system efficiency and effective-ness may be enhanced by adapting to individually differentneeds on the part of users.

Many individual differences have been identified (Jonas-sen & Grabowski, 1993; Riding & Cheema, 1991). A num-ber of these differences have been studied empirically interms of their effects on user behavior and/or performancewhen interacting with complex information. One of themost extensive series of studies of the processes involved indeveloping understanding of complex academic subjectmatter are those conducted over more than 25 years byGordon Pask and his associates.

Pask’s work is particularly interesting because: (a) heused realistically complex academic subject matter; (b) hiswork is particularly mappable onto aspects of system de-sign; and (c) he found dramatic effects in terms of learningeffectiveness when information was presented to individu-als in ways structured to match and mismatch the cognitivestyles he identified.

Pask’s Information Processing Stylesand Strategies

In a series of experiments (Pask, 1976a, 1976b, 1976c,1979, 1988; Pask & Scott, 1972, 1973), Pask and his col-leagues monitored the routes taken by learners throughcomplex academic subject matter that ranged from biolog-ical taxonomies, the menstrual cycle, the operon, spy net-works, reaction kinetics, and Henry VIII’s reign. In theseexperiments, people used one of two basic approaches.What he termed “holists” tended to adopt aglobal approachto learning, examining interrelationships between severaltopics early in the learning process, and concentrating firston building a broad conceptual overview into which detailcould subsequently be fitted.

“Serialists,” on the other hand, tended to use a predom-inantly local learning approach, examining one thing at atime, and concentrating on separate topics and the logicalsequences linking them. The overall picture emerged rela-© 2000 John Wiley & Sons, Inc.

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE. 51(6):543–557, 2000

tively late in the learning process. When learning materialthat entailed theoretical and corresponding “real-world” ex-amples and applications, the serialist worked through eitherthe theoretical or the real world topics, only bringing themtogether late in the learning process when absolutely nec-essary to achieve understanding. The holist, on the otherhand, constantly moved between theory and real world rightfrom the start. Holists also tended to look further ahead inthe hierarchy of topics making up the subject (Entwistle,1981; Robertson, 1977). In those experiments where learn-ers were required to test out hypotheses as they learned,holists progressed by testing out more complex (multipredi-cate) hypotheses than serialists. Pask also reported thatserialists were less willing to tolerate uncertainty duringlearning.

Pask identified two types of holist—irredundant andredundant—the latter using individualistic ways to remem-ber the information to be learned. This entailed makingextensive use of information that was in a strictlylogicalsense unnecessary for learning the required subject matter,and in some cases made up. In these experiments, Paskfound that individuals were consistent in their use of holistor serialist strategies. In other words, they tended to employthe same strategy across different tasks.

In subsequent studies, Pask found evidence that holistand serialist approaches were linked to more fundamentalcomponents of understanding (Pask, 1976c, 1979). Theyrepresented different but equally valid routes to achievinghigh levels of understanding, the holist approach beingparticularly linked to “description building,” the serialistapproach to “procedure building.” Both description buildingand procedure building are necessary to achieve full under-standing. Description building entails the construction of anoverall conceptual map—a description of what may beknown in a subject area. Procedure building relates to mas-tering operational details—the evidence and logical argu-ments necessary to support the larger picture. These twocomponents may be compared to the way an architectdesigns a building. The architect (Entwistle, 1981, p. 93):“ . . . has tobuild up the overall plan (description building)and also to work out the detailed processes, and the logistics

of these processes, (operation and procedure building)whereby the plan can be converted into an actual building.”

In normal (nonexperimental) conditions, Pask argues thatindividuals may tend generally to prefer, and be better at,one or the other. People displaying a holist-like style em-phasize description building. Those displaying a serialist-like style emphasize procedure building. Such tendenciesmay be considered styles as opposed to strategies, the latterbeing choices made on particular occasions. People better atdescription building and tending to employ a holist strategyPask termed “comprehension learners.” Those better at pro-cedure building and tending to use a serialist strategy hetermed “operation learners.” The extreme comprehensionlearner may engage in description building, but stop short ofadequate procedure building. He or she may then display thecharacteristic learning pathology termed “globetrotting” byPask. This entails overgeneralization—building a concep-tual overview that is not supported by the evidence whensubjected to detailed scrutiny. The characteristic pathologyof the extreme operation learner, who does not adequatelyengage in procedure building, is what Pask termed “improv-idence”—basically failing to see the wood for the trees,resulting in fragmented knowledge. Learners who displayneither pathology, succeeding in engaging in high levels ofboth description and procedure building, Pask called “ver-satile learners.” The defining characteristics of holist com-prehension and serialist operation learners are shown inTable 1. The relationship between comprehension, opera-tion, and versatile learning styles and learning outcomes isshown in Figure 1.

Pask found that these differences had implications for theinteraction between information presentation and learningoutcomes. First, the differences he identified can be mapped

TABLE 1. Defining characteristics of holist and serialist approaches.

Serialist operation learning Holist comprehension learning

Linear/sequential—concentrating on one topic at a time Holistic/parallel—having many topics “on the go” at the same timeLocal focus Global focus“Small-step” learning “Large-step” learningNonessential data a distraction Enrichment data valuedReproductive, emphasising relatively verbatim memory Elaboration and transformation into personal meaningHigh certainty in concept development Low certainty in concept developmentConcentrates on simple chains of logical argument Seeks patterns of interrelationships including analogiesKeeps theory and “real world” separate until late in the learning process Integrates theory and “real-world” aspects throughout learningSimple hypotheses Complex hypothesesGeared to procedure building Geared to description buildingOverall picture emerges late in the learning process Overall picture emerges early in the learning process“Improvidence” pathology—fragmented understanding “Globetrotting” pathology—overgeneralization

FIG. 1. Learning approaches and learning outcomes.

544 JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE—April 2000

directly onto the design of learning materials. Second, Paskfound in a series of further experiments that learning out-comes were dramatically affected when individuals werepresented with learning materials that were matched andmismatched with their information processing style. Paskand Scott (1972) note that:

Since human beings are highly adaptable it may be possiblefor an individual withanysort of competence to learn, in theend, according to any teaching strategy. But the experimentsshow, very clearly indeed, that the rate, quality and dura-bility of learning is crucially dependent upon whether or notthe teaching strategy is of a sort that suits the individual” (p.221).

Pask developed a series of tests of learning styles (fromwhich holist and serialist tendencies could be inferred),notably theSpy Ring Historyand theSmugglerstests. Theseare complex, lengthy to administer, and very demanding onlearners. Relatively few studies using these measures (otherthan those conducted by Pask and his colleagues) have beenreported (see, e.g., Coombs, Gibson, & Alty, 1982). En-twistle (Entwistle, Hanley, & Hounsell, 1979) developed aself-completion inventory designed to assess, among a num-ber of other constructs, Pask’s learning styles. Althoughquick and easy to administer, and benefiting from reliabilitydata and norms derived from large scale studies, as dis-cussed further in another section, this instrument may nothave (indeed was not designed to have) predictive value interms of holist and serialist competence. Ford (1989) de-vised a measure specifically designed to assess holist andserialist biases. Clarke (1993) investigated the reliability ofthis instrument, and it has been used in a variety of studies(e.g., Ford, 1989; Ford & Chen, in review). However, itsreliability has not been widely studied, and no publishednorms are available.

Links with Other Constructs

Deep (Transformational) and Surface (Reproductive)Learning

Researchers in Sweden (Marton & Saljo, 1976), England(Entwistle, 1981; Entwistle, Hanley & Hounsell, 1979) andAustralia (Biggs, 1978) have established and elaborated animportant difference in the way students approach the taskof learning. Entwistle (1981) describes the basic differencein relation to reading an academic article. Students adoptinga deep approach:

. . . started with the intention of understanding the meaningof the article, questioned the author’s arguments, and relatedthem both to previous knowledge and to personal experi-ence, and tried to determine the extent to which the author’sconclusions seemed to be justified by the evidence pre-sented (p. 77).

The intent of students adopting a surface approach:

. . . was to memorize those parts of the article which theyconsidered to be important in view of the types of questionsthey anticipated afterwards. Their focus of attention wasthus limited to the specific facts or pieces of disconnectedinformation which was rote learned. (p. 77)

Although Pask (1976b) notes that comprehension andoperation learning may both eventually lead to deep under-standing—and are thus conceptually independent of deepand surface levels of learning—there is, nevertheless, someempirical evidence that comprehension learning is related tosome extent with deep learning, operation learning withsurface learning.

Both description building (the forte of the holist-likecomprehension learner) and procedure building (the forte ofthe serialist-like operation learner) are necessary compo-nents of full understanding. However, when one or other isnot complemented (and appropriately constrained) by theother, characteristic learning pathologies may be displayed.The characteristic pathology of the extreme operationlearner (namely, fragmented items of information not trans-formed and integrated into a conceptual whole) is in ex-treme form compatible with the notion of surface learning(namely, relatively rote memorization of items of informa-tion lacking integration and transformation into personalmeaning). Indeed, Pask notes that the serialist strategy ismore memory-intensive than its holist counterpart, and re-call of learned information by serialists is more verbatimthan that of holists. The characteristic pathology of theextreme comprehension learner is, however, not consistentwith the notion of surface learning, but compatible with thatof deep learning, as defined in terms of “intention to under-stand.” While the extreme comprehension learner may notachieve accurate understanding, he or she will achieve per-sonally meaningful understanding (typically entailing per-sonally generated but relatively untested and inaccurateovergeneralization).

As noted in the previous section, Entwistle (Entwistle etal., 1979) devised a self-report inventory designed to assessa range of students’ approaches to studying including com-prehension, operation, and versatile learning styles. Theinventory relates to students’ perceptions of how they nor-mally go about studying at university. Using a sample of767 British undergraduates, Entwistle applied factor analy-sis to identify three main approaches to studying. The firstfactor loaded on both deep and comprehension learning, thesecond factor on both surface and operation learning. Inter-estingly, these approaches were also linked with differencesin motivation, deep comprehension learning being linkedwith intrinsic motivation, surface operation learning withextrinsic motivation. The factor loadings are shown inTable 2.

Field Dependence/Independence

A number of researchers have pointed out striking sim-ilarities between Pask’s learning styles and Witkin’s field-

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE—April 2000 545

dependent/independent cognitive styles. Field dependence/independence is a relatively well-established construct, andhas been the subject of much research for over 30 years.Relatively field-independentindividuals are more adept atstructuring and analytic activity relative to their field-de-pendentcounterparts. Relatively field-dependentindividu-als thrive more in situations where learning is structured andanalysed for them. They tend to prefer a “spectator” ap-proach to learning rather than the hypothesis-testing ap-proach favored by more field-independent learners. Theyoperate with a relatively external frame of reference.

In essence, the distinction is that relatively field-inde-pendent individuals tend to experience the components of astructured field analytically, as discrete from their back-ground, and to impose structure on a relatively unstructuredfield. By contrast, relatively field-dependent individualstend to not be as good at such structuring and analyticactivity, and to perceive a complex stimulus globally as agestalt. This dimension is far-reaching, extending from per-ceptual through intellectual and social functioning. Witkin,Moore, Goodenough, and Cox (1977) published a detailedreview of the educational implications of field-dependence/independence. Riding and Cheema (1991) also includefield-dependence/independence in a comparative review ofcognitive styles that also includes Pask’s holist/serialistdistinction.

Several researchers have suggested that the constructs ofPask and Witkin are both manifestations of a commonwholist/analytic “family” (e.g., Brumby, 1982; Coan, 1994;Fowler, 1980; Miller, 1987; Riding & Cheema, 1991). Suchobservations suggest a direct field-dependent/holist andfield-independent/serialist correspondence. However, fewempirical links have been reported. Pask (1979) investi-gated possible links, but found no significant correlations.As Riding and Cheema (1991, p. 204) note, in their com-parative review of cognitive styles:

Whilst the style has many educational implications, it isunfortunate that as far as the authors are aware, asyet, . . . noempirical evidence exists linking the holist-seri-alist dimension with other styles of the wholist-analyticfamily.

There are also conceptual problems that make difficultthe notion of any one-to-one correspondence between the

two sets of constructs. Although holists operate in a globalfashion (as do field-dependent individuals), serialists in ananalytic fashion (as do field-independent individuals), seri-alists also tend to rely on and preserve the structure ofinformation as originally presented (as do field-dependentindividuals). Holists are relatively active learners, capableof navigating information spaces relatively independentlyand tending to impose their own structural framework onthe material (as do relatively field-independent individuals).

However, using a relatively new measure of field depen-dence/independence (described below) which, unlike previ-ous instruments measures field-dependence directly, as op-posed to inferring it from low levels of field-independence,Ford (1995) found significant correlations between field-dependent/independent cognitive styles and holist/serialistcompetence as measured in matching/mismatching experi-ments using Pask’s learning materials. Using the same in-strument, Ford and Chen (in review) also found directcorrelations between field-dependence/independence andholist/serialist biases measured using a shortened version ofthe Study Processes Questionnaire.

Field dependence/independence is relatively quick andeasy to measure. A number of instruments have been de-veloped, in particular Witkin’sGroup Embedded FiguresTest (GEFT). More recently, Riding’s (1991)CognitiveStyles Analysis,offering computerized administration andscoring, has been designed to overcome a limitation affect-ing the most widely used measures of field dependence/independence. Tests such as Witkin’s GEFT derive scoresfor field independenceby requiring subjects to locate simpleshapes embedded in more complex geometrical patterns.However, levels of fielddependenceare inferred from poorfield-independenceperformance—from poor performanceon this disembedding task. TheCognitive Styles Analysismeasures what the authors refer to as a wholist/analyticdimension, noting that this is equivalent to field depen-dence/independence (Riding & Sadler-Smith, 1992).

However, theCognitive Styles Analysisdiffers from testssuch as the GEFT in that its wholist/analytic test consists oftwo subtests. In the first, subjects are required to judge thesimilarity of a series of complex geometrical figures—a taskrequiring field-dependentcapacity. The second subtest re-quires subjects to determine whether a simple shape iscontained within a more complex geometrical figure (as inthe GEFT)—a task requiring the disembedding capacityassociated with fieldindependence.In this way, field-de-pendent competence is positively measured rather than be-ing inferred from poor field-independent capability.

Divergent/Convergent Thinking

Pask (1976b, 1979) found significant correlations be-tween holist biases and scores for divergent thinking. In-deed, the “divergent thinker,” according to Hudson (1968)excels in tasks requiring him or her to think tangentially,making the sort of connections between concepts morecharacteristic of the holist than the serialist (Kennet &

TABLE 2. Factor loadings for deep/surface learning, comprehension/operation learning, and intrinsic/extrinsic motivation from Entwistle’sstudy.

Factor 1 Factor 2

Deep approach 62Comprehension learning 73Intrinsic motivation 54Surface approach 67Operation learning 67Extrinsic motivation 61


Cropley, 1975). The convergent thinker excels at tasksrequiring a heavier emphasis on logical processes. This typeof thinking is characterized by features reflecting more theserialist approach, entailing relatively less uncertainty inrelation to concept formation and the building up of under-standing in a logical step by step way.

Divergent (relatively creative) entails generating newideas by identifying themes by which otherwise discreteentities become integrated. The identification of such inte-grating themes typically depends on the recognition ofanalogous relationships between concepts (Holyoak & Tha-gard, 1996; Keane, 1988; Mayer, 1992; Minsky, 1986;Pask, 1976c, 1979). The processing of analogies—centralto creative thought—arguably entails relatively holistic andparallel—as opposed to more logical and sequential—infor-mation processing (Hofstadter, 1995; Mitchell, 1993).Rather than entailing the application of logic to work out,for example, the next step in an argument, this type ofprocessing entails perceiving ways in which different enti-ties (arguments, patterns and relationships) are similar anddissimilar.

Hemispheric Differences

Certain characteristics of the holist comprehensionlearner and the serialist operation learner also seem to echoeaspects of hemispheric differences reported in experimentswith split-brain patients. In his review of research, Gazza-niga (1998) notes that, when asked to recall informationpresented to them:“ . . . the left hemisphere generates manyfalse reports. But the right brain does not; it provides muchmore veridical account” (p. 38).

He goes on to report that:

. . . the left hemisphere actively places its experiences in alarger context, whereas the right simply attends to the per-ceptual aspects of the stimulus.. . . These findings all sug-gest that the interpretative mechanism of the left hemisphereis always hard at work, seeking the meaning of events. It isconstantly looking for order and reason, even where there isnone—which leads it continually to make mistakes. It tendsto overgeneralize, frequently constructing a potential past asopposed to a true one.. . . It turns out the right hemisphere-. . . does not try to interpret its experience and find deepermeaning. It continues to live only in the thin moment of thepresent . . . But the left, when asked to explain why it isattempting to figure the whole sequence, always comes upwith a theory, no matter how outlandish (p. 38).

It is interesting to compare these comments with Paskand Scott’s (1972) description of holist and serialist learnersfrom their original experiments:

There are two subcategories of holist calledirredundantholistsandredundant holists.Students of both types imagean entire system of facts or principles. Though an irredun-dant holist’s image is rightly interconnected, it contains onlyrelevant and essential constitents. In contrast, redundant

holists entertain images that containlogically irrelevant oroverspecific material, commonly derived from data used to“enrich” the curriculum, and these students embed the sa-lient facts and principles in a network of redundant items.Though logically irrelevant, the items in question are ofgreat psychological importance to a “redundant holist,”since he uses them to access, retain and manipulate what-ever he was originally required to learn (p. 258).

Holists, eitherirredundantor redundantcommit mistakesdue to simple over-generalization. . . .Serialists fall intodifficulties if they fail to distinguish the wood from the treesand consequently try to assimilate masses of sparsely re-lated irrelevant information” (pp. 275–276).

. . . holists are distinguished from serialists in terms ofthe number of inferential statements they produce . . . it ispossible to distinguish the serialist from the holist by atendency, on the part of a serialist, to preserve the order ofthe programme presentation format which is absent in theholist. Presented with a holist programme the serialist isunable to preserve the complete order but he does manage topreserve sequentially arranged fragments (p. 284).

Serialists relied heavily on memorisng information rela-tively verbatim, as opposed to transforming it and integrat-ing it into a wider context of personal meaning.

These points of similarity provide at most circumstantialevidence of the possibility that the distinctions identified byPask may have some fundamental biological origin.

Utility of the Constructs

Linking psychological theory, based on the results ofexperiments often conducted in laboratory conditions, to“real-world” system design is problematic. Landauer (1987,1991), for example, points out that effects found in labora-tory conditions may be insignificant in scale in “real-world”situations—and thus of little value to applied system design.

Although the content Pask used consisted of realisticallycomplex academic subject matter, rigorous learning condi-tions not at all typical of real study contexts were en-forced—particularly in the matching/mismatching experi-ments, which would seem to have the greatest implicationsfor applied system design. Learners were not allowed toprogress to a new frame of information until they hadsuccessfully answered questions relating to the previousone. They were also required to engage in a completelyerror-free run through the learning materials before goingon to the tests.

This section attempts to address such criticisms by con-sidering the extent to which Pask’s constructs may be validin “real-world” situations. It does so by reviewing evidenceof: (a) the manifestation of the constructs across a range ofdifferent activities; (b) their existence, and incidence, in lessconstrained, “real-world” contexts; and (c) the scale of theireffects.


Manifestation Across Different Activities

Pask’s constructs have been observed in a number ofstudies relating to hypertext navigation, and to the searchingof commercial CD-ROM databases.

Hypertext Navigation

Ellis, Ford, and Wood (1992, 1993) report an experimentthat investigated strategic differences in the use of naviga-tional tools in a large hypertext-based database. Forty post-graduate students were tested for comprehension, operation,and versatile learning styles using Entwistle’s (1981)ShortInventory of Approaches to Studying.Holist and serialistbiases were measured using theStudy Preference Question-naire.

The hypertext system used in this experiment allowedlearners considerable freedom in their exploration of thesubject matter. Navigation tools were provided in the formof a global concept map, keyword index, menus, and abacktracking facility. The subject matter of the hypertextwas the European Single Market. The students were giventhe task of using the system to answer a number of questionsrequiring (a) specific factual recall, and (b) generalizationusing information from more than one location in the hy-pertext. All interactions were automatically logged.

Significant differences were found relating to the use ofdifferent navigation tools between learners typed by holistand serialist biases as measured by theStudy PreferenceQuestionnaire.Holists made significantly greater use of theconcept map: serialists made significantly greater use of thekeyword index. The map was particularly suited to globalorientation—keeping track of where one was in relation tothe overall structure of the subject matter. The index wasparticularly suited to finding specific information. No suchinteractions were found, however, when scores for compre-hension and operation learning on theShort Inventory ofApproaches to Studyingwere used. No statistically signifi-cant differences were found in relation to scores on thelearning tests. It is interesting to note the lack of correlationbetween scores on the two instruments used to measurelearning style. The problem of measurement of Pask’s con-structs is discussed later.

Ford and Chen (in review) also report very similar dif-ferences in hypertext navigation relating to the use of a mapand index in a hypertext system. Using Riding’sCognitiveStyles Analysisand theStudy Processes Questionnaire,theyfound significant correlations between holist bias and fielddependence, serialist bias and field independence. They alsofound significant correlations between field-dependent/in-termediate/field-independent cognitive style categories anda range of navigational behaviors—but not learning perfor-mance.

The hypertext system was divided into a number ofsections, each of which included subsections including“Overview,” “Examples,” and “Detailed techniques.” Theentire contents of the system were structured into eight

hierarchical levels. When individuals were classified as fielddependent, intermediate, or field independent usingRiding’s (1991) recommended classification ofCognitiveStyles Analysisscores, field-dependent individuals (relativeto field-independent individuals): (a) made greater use ofthe Map; (b) made less use of the Index; (c) made less useof the Back/Forward buttons; (d) made greater use of the“Overview” sections; (e) made less use of the “Detailedtechniques” sections; (f) made greater use of the “Exam-ples” sections; (g) made greater use of the Section buttons(“high level” buttons giving access to the three main sec-tions of the program); and (h) spent a greater proportion oftheir time studying topics high in the subject hierarchy(levels 1, 2, and 3 in a hierarchy of eight levels) and asmaller proportion of their time studying lower levels (lev-els 4 and 5).

Database Searching

Holist and serialist differences have also been reportedrelating to the searching of commercial CD-ROM data-bases. A British Library funded study (Ford, Wood, &Walsh, 1994; Wood, Ford, & Walsh, 1992) investigatedcomprehension, operation, and versatile learners’ searchingof a CD-ROM database for document references to helpthem with their coursework.

In this study, 67 postgraduate students conducted 275searches on Silver Platter’s CD-ROM–based Library andInformation Science Abstracts (LISA) database. The ver-sion of the database contained 105,482 bibliographicrecords. Students were randomly assigned so that half thesample were searching first with feedback (defined below),half without. The converse applied to the other half of thesample. Feedback took the form of postings—informationon how many document references were indexed (andwould, thus, be retrieved) by each keyword and keywordcombination used in the search. This is a standard facility inlarge commercially available database systems. It enablessearchers to assess how many documents their search wouldfind, before actually retrieving any. Students selected theirsubjects from a list of genuine coursework topics.

Students’ searching strategies were classified in terms ofrelative breadth and depth. A high use of the BooleanoperatorOR to link keywords represents a relatively broadstrategy: a high use ofANDa relatively narrow strategy. Forexample, a search forcomputers OR educationwould re-trieve documents oncomputers,on education,and onbothsubjects combined. A search forcomputers AND educationwould be narrower, because it would retrieve documentsonly on both subjects. Other measures of the breadth ornarrowness of search includedtruncation,which broadens asearch because it entails using a shortened word stem (e.g.,comput*) to retrieve all words beginning with that stem(e.g.,computers, computing, computation,etc.); and use ofdescriptorsand index, a high use being indicative of arelatively broad search insofar as it relates to the use ofgeneric terms (terms selected to stand for a variety of


synonyms). Search effectiveness was measured in terms ofprecision, number of relevant references retrieved,andrel-ative recall.Precision is the number of relevant items re-trieved, divided by the number of items retrieved includingany irrelevant ones. Relative recall is the number of relevantitems retrieved, divided by the total number of relevantitems retrieved by all searchers for a particular search.

Both with and without feedback (postings), comprehen-sion learners displayed a broader approach than operationlearners in that they made significantly greater use ofOR,descriptors,and index.They also examined more retrievedsets than operation learners—again, compatible with thenotion of a broader approach. Without feedback, they alsoused moretruncation than operation learners. However, inthe “with-feedback” condition, they also used made moreuse of AND—a finding not in accord with the notion ofcomprehension learners searching more broadly than oper-ation learners.

In the “no-feedback” condition, significant differences inperformance were observed. Both comprehension and op-eration learners searched significantly less effectively thanversatile learners. Versatile learners outperformed compre-hension learners in what might be expected to be their forte,namely number of relevant references retrieved (character-istic of relatively broad searches). They also outperformedoperation learners in their expected forte—precision (char-acteristic of relatively narrow search approaches). With theavailability of feedback, both comprehension learners andoperation learners increased the effectiveness of theirsearching relative to versatile learners. The superiority ofversatile learners, in terms of number of relevant referencesretrieved in relation to comprehension learners, and preci-sion in relation to operation learners, disappeared.

In a further British Library funded study (Wood, Ford,Miller, Sobczyk, & Duffin, 1996), 105 undergraduate stu-dents carried out on-line searches of CD-ROM databases forinformation on topics relating to their coursework. The mostappropriate database for each particular topic was selected.These included Inspec, Biological Abstracts, Social Sci-ences Index, Compendex, ABI-Inform, General SciencesIndex, and Modern Languages Association.

Search strategies, results, and effectiveness were re-corded. Students completed the full version of Entwistle’sstudy inventory. This includes the subscalesRelatingIdeas—characteristic of the comprehension learner—andUse of Evidence—characteristic of the operation learner.The combination ofRelating IdeasandUse of Evidenceischaracteristic of the versatile learner. The learning pathol-ogy associated with operation learning is assessed by thesubscaleUnrelated Memorising.A subscale relating toGlo-betrotting—the characteristic pathology of the comprehen-sion learner—was also included.

Relating Ideaswas significantly linked with the use of arelatively broad range of different keyword terms persearch, and with the introduction of a relatively broad rangeof new terms during each search. Its pathological counter-part,Globetrotting,was linked with being aware of broad-

ening and narrowing techniques, and with being generallydissatisfied with search results. Students scoring high onUse of Evidencetended to be relatively satisfied with theextent to which they avoided retrieving irrelevant refer-ences, and generally satisfied with search results. Studentsdisplaying high levels ofUnrelated Memorising—the inef-fective aspect of an operation learning approach—alsotended to be unaware of broadening/narrowing search tech-niques, to be unsatisfied with their ability to avoid retrievingirrelevant references, and to consider their own knowledgelevel to be low.

The essential differences between comprehension learn-ing and operation learning styles were further distilled byexamining the characteristics of those people who scorednot just high on comprehension learning but at the sametime low on operation learning—and vice versa. A singlemeasure was derived for a global/analytic bias relating topositive aspects of learning wherebyUse of Evidencescoreswere subtracted fromRelating Ideasscores for each person.Using this measure, comprehension learners used a broaderrange of new terms per search, and used a greater range ofdifferent terms per search. They retrieved more relevantreferences, but were less satisfied with their search results.When pathological aspects of learning were examined, bysubtracting Unrelated Memorising from Globetrottingscores, the feature distinguishing comprehension learningfrom operation learning was the level of awareness ofbroadening and narrowing techniques.

Less Constrained Conditions

Although differences in learning strategy have beenlinked with differences in performance in tightly controlledexperimental conditions, it may be that in less constrained,often collaborative and longer term learning contexts, indi-viduals may be able to compensate for or avoid negativeeffects associated with the adoption of certain approaches.The dramatic effects of matching and mismatching infor-mation presentation with learning styles found by Pask andScott (1972) were obtained in constrained experimentallearning conditions. However, the distance between the“real world” and the experimental constraints and condi-tions of the studies discussed in the previous section wasmuch less than in the case of Pask’s original experiments.

In the study of hypertext navigation conducted by Ellis,Ford, and Wood (1992; 1993), students were given a fairlynaturalistic brief—namely, to prepare a report for an imag-inary manager on a particular aspect of the European SingleMarket. They were then allowed to interact freely with thehypertext database. All actions were automatically and un-obtrusively logged. In the study by Ford and Chen (inreview), students volunteered to use the hypertext system inorder to learn how to write Web pages with HTML—a skillof direct relevance to, but at that stage not yet introduced in,their library and information management courses. The taskthey were given was to write a home page. Although ob-


served by a researcher, they were allowed to interact freelywith the system without other constraints.

In the first on-line searching experiment (Ford et al.,1994), the search topics were “real” in that they relateddirectly to the students’ coursework and dissertations. Thestudents searched the databases freely, the interactions be-ing logged automatically. However, they were asked tosearch the same topic twice—once with postings, and oncewithout (the order being varied across the sample). Clearly,particularly in the second of each pair of searches, thisrepresented an artificial constraint not encountered in nor-mal searching.

In the second online searching experiment (Wood et al.,1996), topics upon which each student searched were con-structed in consultation with a member of staff from thatstudent’s department in an attempt to make them relevant toeach student’s real study needs. Students again searchedfreely, interactions being automatically logged.

Ford and Ford (1992) report an experiment with 30postgraduate students designed to discover how they mightgo about learning from an extremely unconstrained com-puter-based environment. A system was created which pre-served the characteristics of a computer-based environment,yet which freed itself from the constraints of current tech-nology. Although not realising it at the time, the studentswere, in fact, interacting via a computer screen with twohuman subject experts.

The students were asked to interrogate the system, usingwhatever language and approach they wished, to learn aboutPRECIS—a document indexing system. At the end of thesession, they were asked to write what they had learnedabout the system. Transcripts were marked on the basis ofcorrectly recalled information. All interactions were loggedand analyzed. Questions asked by the students were classi-fied according to level and type. Level related to the depthof detail in the subject matter hierarchy (from overview atone extreme, to the most detailed indexing procedures at theother), to which each question referred. Questions were alsoclassified according to the type of information sought.Broad types of question were established, namelydescrip-tive (questions inviting straightforward factual answers de-scribing some concept or aspect of the PRECIS system);analytic (questions reflecting a degree of analysis of thematerial being learnt on the part of the questioner);focus-sing (questions seeking, in essence, to set limits, or explorethe bounds of some concept or function); andconcrete(“down-to-earth,” practical requests to see some aspect ofthe PRECIS system in action).

Given this considerable freedom and lack of direction inthe way they went about learning, the students displayeddistinct learning strategies which are remarkably similar toPask’s constructs. Two approaches correlated with rela-tively successful learning. The first represented a relativelypassive intake of information—a high proportion of descrip-tive, and a low proportion of analytic questions—coupledwith an attention to low level procedural detail. The secondwas characterised as relatively active—a low proportion of

descriptive, with a high proportion of focussing questions.This approach was also characterized by a concentration onoverview material, as opposed to lower level proceduraldetail. A third approach correlated with relative failure. Thiswas characterized by concentrating on middle level material(between overview and low-level detail) while asking rela-tively passive descriptive questions, and/or relatively active,procedurally detailed concrete questions.

The two successful strategies identified are very similarto Pask’s comprehension and operation learning styles. Op-eration learners concentrate on lower level procedural de-tails, and tend to adopt a serialist, relatively passive learningapproach. Comprehension learners, on the other hand, con-centrate on less procedurally detailed, overview informa-tion, and tend to adopt a holist, relatively active learningstrategy.

Ford (1989) also attempted to replicate Pask’s matching/mismatching experiments in slightly less constrained con-ditions than those imposed in Pask’s original studies. Twen-ty-five postgraduate students were pretested for learningstyle using theShort Inventory of Approaches to StudyandStudy PreferenceQuestionnaire. They then learned two setsof subject matter from two teaching packages: one designedto suit a holist strategy, the other designed to suit a serialiststrategy. The materials were based on those used by Paskand Scott in their original experiments. The subject matter,designed to minimize students’ prior knowledge, related tospecies of imaginary Martian creatures—“Clobbits” and“Gandlemullers.” The teaching programmes were publishedby the Open University for thePersonality and Learning(E201) course. Learning was assessed by means of a simpletest of factual recall.

When students were classified as having biases towardsholist or serialist strategies according to theStudy Prefer-ence Questionnaire,learning was significantly better inmatched conditions than in mismatched conditions. No sig-nificant results were found relating to style using scores ontheShort Inventory of Approaches to Study.It is interestingto note that significant effects were found for matching andmismatching, even for students classified as versatile ac-cording to theShort Inventory.It may be that students whoare versatile (i.e., who can combine both comprehensionand operation learning elements to achieve full understand-ing), nevertheless, learn more effectively, concentrating onone or other element first or predominantly. These findingsechoe those of Pask and Scott’s original experiments, inwhich learning in mismatched conditions was seriouslydisrupted relative to learning in matched conditions. Theirresults were extreme, with no overlap in the scores. If theirsample included versatile learners, they were not immune tothe effects of matching and mismatching.

The same holist and serialist learning materials wereused in a further study of 38 students (16 undergraduate, 22postgraduate) by Ford (1995). However, this study exploredthe effects of matching and mismatching holist and serialistlearning materials with field-dependent/independent cogni-tive style. Significant differences in performance were


found for students working in what were hypothesized asmatched conditions (field-dependent students with holistmaterials, field-independent students with serialist materi-als) and mismatched conditions. Riding’sCognitive StylesAnalysiswas used to measure field-dependent/independentcognitive style.

Incidence

If the majority of the population is versatile, then theimplications of Pask’s styles for applied system designwould seem to be diminished in that versatile learners,according to Pask (1979, 1988), are able to engage in bothdescription building and procedure building, combiningthem to achieve deep understanding—without the need forsupport in the form of an adaptive information system. Thissection reviews studies that tell us something about theincidence of Pask’s styles.

Entwistle (1981) conducted a study of 2208 British un-dergraduates and published mean scores for comprehension,operation, and versatile learning. He did not include detailsof the relative incidence of each of the different styles in thissample. However, a greater mean score for comprehensionlearning (13.04) relative to operation learning (12.45)among arts students, a closer balance amongst Social sci-ences students (13.64 and 13.48, respectively), and a greatermean score for operation learning (13.48) relative to com-prehension learning (12.64) among science students sug-gests that the incidence of different styles may differ acrossdisciplines.

Based on this study, Entwistle recommended that scoresfor comprehension, operation, and versatile learning stylesthat are more than half a standard deviation above the meanmay be considered “high,” those a full standard deviation as“very high.” Using this classification, the studies by Ford(1989) and Ford, Wood, and Walsh (1994) further classifiedindividuals as comprehension, operation, or versatile learn-ers if they scored as “high” or “very high” on one style,while at the same time scoring lower on the other measuresof learning style. Thus, for example, an individual scoring“high” or “very high” on comprehension learning but lowerfor both operation and versatile learning would be classed asa comprehension learner.

Using Entwistle’s classification, Ford’s (1989) study of25 postgraduate students found that 13 (52%) were classi-fied as “high” or “very high” on versatility. Of these 13versatile students, 4 also scored a standard deviation ormore above Entwistle’s means for comprehension learningbut not for operation learning, 2 scored similarly for oper-ation learning but not comprehension learning. Of the re-maining seven versatile learners, two also scored a standarddeviation or more above the mean for both comprehensionand operation learning.

The previously described British Library funded study(Ford et al., 1994), using the same classification procedure,found that in a sample of 67 postgraduate students, 54%were versatile, 20% operation, and 16% comprehension

learners. Ten percent had a mixed classification. This studyalso reported the incidence of learning styles by discipline,which were broadly similar to Entwistle’s findings in rela-tion to the direction of balance of styles across differentdisciplines. Forty-six percent of comprehension learnerswere arts students, 36% science students, and 9% socialscientists (9% failing to indicate discipline). Forty-six per-cent of operation learners were science students, 38% arts,and 16% social science. Fifty-three percent of versatilelearners were from the arts, 28% from science, and 19%from the social sciences. Ten percent of the sample had amixed classification, and could not be categorized as clearlycomprehension, operation, or versatile learners.

Scale

Small differences in learning performance may correlatewith differences in learning style to a statistically highlysignificant level. However, such significance may derive notfrom the scale of performance differences, but rather fromthe consistency with such differences are found in differentstylistic groups. Very small, but consistent differences maybe statistically very significant. Just what such differencesmay signify—if anything—for performance by individualsin genuine (as opposed to experimental) learning situationsis harder to determine.

This section provides details of the scale of effects in thestudies described in the previous sections. In a number ofthese studies, data relating to scale (as opposed to statisticalsignificance) were not published. In these cases, where theoriginal data are available relevant figures have been com-puted for the present article.

In Pask’s original experiments, the effects of matchingand mismatching learning style with information presenta-tion style were dramatic. The sample size was extremelysmall, the study using only 12 people. However, there wasno overlap in the scores—i.e., the score of every personlearning in matched conditions was higher than the highestscore of any person learning in mismatched conditions.Table 3 shows the scores for the 12 people. The test con-sisted of a 20-item test of factual recall. Mean score for thematched condition (holists working with holist materials,serialist with serialist materials) was 18.83 (standard devi-ation 1.17). Mean score for the mismatched condition was9.5 (standard deviation 2.43).

As described in the Less Constrained Conditions section,Ford (1989) attempted to replicate Pask’s findings with 25

TABLE 3. Test scores from Pask and Scott’s original experiment.

Holists Serialists

Matched Mismatched Matched Mismatched

20 11 20 619 9 17 819 13 18 10

Mean 19.33 Mean 11 Mean 18.33 Mean 8


postgraduate students, using a 25-item test of factual recall.When students were classified as holist or serialist using theStudy Processes Questionnaire,mean score in matchedconditions was 18.35 (standard deviation 6.86), that in mis-matched conditions 14.40 (standard deviation 5.72). Ford’s(1995) subsequent study with 38 students (16 undergradu-ate, 22 postgraduate) used a 48-item test of factual recall.Significant differences in performance were found for stu-dents working in what were hypothesized as matched con-ditions (field-dependent students with holist materials, field-independent students with serialist materials)—mean scoreof 27.13—and mismatched conditions—mean score of10.92.

As described in the Hypertext Navigation section, Fordand Chen (in review) found significant differences in nav-igation behavior by individuals classified as field dependentand field independent using Riding’sCognitive Styles Anal-ysis.The scale of differences are shown in Table 4.

Table 5 shows differences reported by Ford, Wood, andWalsh (1994) in the study of CD-ROM database searchingintroduced earlier. Significant differences were found forboth information-seeking strategy and performance in termsof the number of relevant references retrieved and precision.In the second study by Wood, Ford, Miller, Sobczyk, andDuffin (1996), data was not published relating only to thestatistical significance—not to the scale—of differences insearching behaviour and outcome.

Implications for Virtual Environment Research

Problems

Global and analytic styles of information processinghave been observed in a variety of activities. However,more research is required before the extent to which theymay be useful in the development of virtual environmentscan be assessed. We need to know more about the size of theeffects of these information processing styles in the “realworld” as opposed to constrained experimental conditions.We also need more valid and reliable measures of theconstructs that will produce consistent results in less con-strained conditions than those in which Pask’s originalexperiments were conducted.

Scale of Effects

The largest effects of the information processing stylesdiscussed here have been observed in highly constrainedexperimental environments. In Pask’s experiments, match-ing style with information presentation resulted in a 46.7%difference on a 20-item test compared to mismatching.Ford’s less constrained replications resulted in a differencein the first experiment of 15.8% on a 25-item test, and in thesecond experiment of 33.78% on a 48-item test. Perfor-mance effects have also been observed in relation to data-base searching conducted in much less constrained condi-

TABLE 4. Mean differences (and standard deviations) in hypertext navigation behavior.

Field-dependent mean score Field-independent mean score Mean score difference

Map 7.95 (SD: 4.39) 4.04 (SD: 3.10) 3.91Index 4.71 (SD: 2.95) 8.13 (SD: 3.24) 3.42Back/forward buttons 2.76 (SD: 1.95) 4.88 (SD: 1.90) 2.12Section buttons 4.76 (SD: 2.23) 2.92 (SD: 1.87) 1.84“Overview” 5.71 (SD: 2.03) 5.12 (SD: 1.62) 0.59“Examples” 8.62 (SD: 4.04) 4.92 (SD: 3.71) 3.7“Detailed techniques” 7.29 (SD: 4.44) 11.68 (SD: 4.16) 4.39Level 1 (% time spent) 15.35 (SD: 0.07) 10.08 (SD: 0.07) 5.27Level 2 (% time spent) 23.71 (SD: 0.10) 13.20 (SD: 0.12) 10.51Level 3 (% time spent) 47.59 (SD: 0.15) 28.55 (SD: 0.22) 19.04Level 4 (% time spent) 10.15 (SD: 0.12) 24.08 (SD: 0.22) 13.93Level 5 (% time spent) 01.14 (SD: 0.04) 14.69 (SD: 0.16) 13.55

Figures relate to number of accesses unless otherwise indicated.

TABLE 5. Mean scores (and standard deviations) for CD-ROM database searching behavior and outcomes.

Operation learners Comprehension learners Mean score difference

“AND”s 3.11 (SD: 2.30) 4.52 (SD: 3.30) 1.41“OR”s 1.32 (SD: 2.26) 2.96 (SD: 4.57) 1.64Index 0.14 (SD: 0.45) 1.04 (SD: 1.97) 0.9Descriptors 0.14 (SD: 0.59) 2.70 (SD: 4.72) 2.56Sets examined 2.07 (SD: 2.54) 3.13 (SD: 2.22) 1.06No. of relevant references retrieved 7.26 (SD: 7.48) 9.87 (SD: 7.91) 2.61Precision 47.72 (SD: 30.72) 33.82 (SD: 26.10) 13.9


tions. However, these differences were only observed whenpostings information was withheld from learners. Compre-hension learners retrieved an average of 2.61 additionalrelevant references compared to operation learners. Withinthe context of a mean of 9.87 relevant documents retrievedin total, this represents 26.44%.

In the relatively unconstrained studies of hypertext use,no differences in performance were reported. However,significant differences in navigation strategies were ob-served. These differences were small. The largest relating tonavigation tools was 3.91 more clicks on the Map byfield-dependent individuals; that relating to sections of thehypermedia system was 4.39 more accesses of the “Detailedtechniques” sections by field-independent individuals; thatrelating to depth in the subject hierarchy was 19.04% moretime being spent accessing level 3 by field-dependent indi-viduals.

Consistency and Measurement

In studies conducted in less constrained constrained con-ditions, measures of Pask’s information processing styleshave not produced consistent results. Different instrumentsdesigned to measure similar constructs (e.g., holist bias andcomprehension learning, and serialist bias and operationlearning) have shown no significant correlation. A giveninstrument may produce correlations with information pro-cessing behavior in one study, and not in another.

In Ford’s (1989) replication of Pask’s matching/mis-matching experiments, a total of 82 subjects completed thetwo learning style instruments—theStudy Preference Ques-tionnaireand Entwistle’sShort Inventory of Approaches toStudying.25 of these students went on to take part in thelearning experiments. Sixty-seven of these subjects dis-played some bias in scores on both instruments (i.e., be-tween comprehension and operation learning in the case oftheShort Inventory;and holist and serialist strategies in thecase of theStudy Preference Questionnaire). The others hadtied scores on one or other of the instruments. However, inonly 39 of the 67 cases did scores on the two instrumentscorrelate (i.e., comprehension learning with a holist bias, oroperation learning with a serialist bias).

TheShort Inventory of Approaches to Studyingprovidesinformation primarily—but not exclusively—on the pres-ence of description building and procedure building. It doesnot set out to assess the extent to which one or othercomponent of learning is used first or mainly in the learningprocess. TheStudy Preference Questionnaire,on the otherhand, is concerned solely with the sequence in which thecomponents are used. In this way, a versatile student may,nevertheless, display a preference for a holist or serialiststrategy despite successfully engaging in both descriptionbuilding (the forte of the comprehension learner) and pro-cedure building (the forte of the operation learner) toachieve full understanding. However, one would expectsome correlation between holist biases and comprehensionlearning, and between serialist biases and operation learn-ing. Such correlations did not appear.

In the studies of hypertext navigation, global learnersmade significantly greater use of the map, local learnersgreater use of the index. However, in the first study (Ellis etal., 1993) these correlations were found using scores on theStudy Preference Questionnairemeasure of holist/serialistbiases and not with scores on Witkin’s GEFT measure offield dependence/independence. In the subsequent study ofhypertext navigation (Ford & Chen, in review), althoughfield dependence/independence as measured by Riding’sCognitive Styles Analysiscorrelated with scores on theStudy Preference Questionnaire,navigation behavior corre-lated with field dependence/independence and not with ho-list/serialist biases.

Utility of the Constructs

The global and analytic styles discussed here haveproved useful as explanatory variables exposing otherwisemasked effects in research studies. Theymayalso be usefulin helping us provide machine support to enhance humanperformance in applied system design. These areas of use-fulness—actual and potential—are discussed below.

Explanatory Variables Exposing Otherwise MaskedEffects

It has proved useful to include measures of cognitivestyles in a number of studies of “traditional” informationenvironments, in that they have revealed significant findingsthat would otherwise have been masked.

For example, in the study of on-line searching (Wood etal., 1992), the effects of postings information were investi-gated by comparing search results for searches conductedwith and without postings information being available to thesearchers. No significant differences were found until cog-nitive style test results were included in the analysis. Sig-nificant differences in behavior and performance in the twoexperimental conditions were found between individualswith comprehension, operation, and versatile stylistic bi-ases.

In the context of learning, it may also be useful, whenassessing user performance, to differentiate between typesand levels of understanding characteristic of different cog-nitive styles. An overall measure of understanding (e.g., asimple score of correct test responses) may mask differen-tial achievement relating to, for example, the broad under-standing of how topics interrelate to form an overall picture(the strength of the holist) as opposed to the detailed localunderstanding of chains of argument making up a subtopic(the strength of the serialist).

Providing Machine Support to Enhance HumanPerformance

Global description-building and analytic procedure-building views and support tools could be deployed withina virtual environment to provide support for individuals (a)


weak in either description- or procedure-building compe-tence, or (b) who require a holist or serialist approachdespite not lacking description- or procedure-building com-petence. As described in previous sections, there is evidencethat when information is presented in a way that matches ormismatches individuals’ preferred holist or serialist biases,learning is significantly enhanced or disrupted respectively(Ford, 1989, 1995; Pask and Scott, 1972). Such effects havebeen found even for individuals typed as “versatile”—i.e.,who are competent in the skills of both description andprocedure building (Ford, 1989). The implication here isthat even versatile individuals who engage in both descrip-tion and procedure building may need to do so with apredominantly holist or serialist sequence and approach.

A virtual environment could enable differential patternsand sequences of access to (a) global views of a developingtopic, with “enrichment” material suited to the holist; and(b) more analytic views presented with an emphasis onclose logical connections, and freedom from what is strictlylogically irrelevant material, as required by the serialist.Such access could be autonomous, prescribed or recom-mended.

Autonomousaccess would enable users to choose accesspatterns for themselves. However, the extent to which in-dividuals are likely to choose strategies that are optimal fortheir particular information styles is an open question. Forexample, in the study by Ford and Ford (1992), when givenextensive freedom during interactions with a computer-based training system, a proportion of postgraduate studentschose ineffective strategies, other choosing more effec-tively. Access patterns could alternatively beprescribedforusers by the system. However, such a system would require(a) a mechanism for accurately assessing each user’s stylis-tic competence, and (b) a robust model capable of generat-ing, on the basis of such an assessment, an effective accesspattern for each user. A variation on this approach would beto replace prescribed options withrecommendedoptions,which could take the form of default navigational “nextmoves” suggested by the system—but capable of beingoverridden by the user if desired.

It may also be possible to support individuals who areweak in description or procedure building. A user strongerin description building than in procedure building could behelped to achieve a more secure evidence-based understand-ing of an area via tools enabling the exploration, testing, andvalidation of procedural aspects of topics and subtopics.Support for such a learner weak in procedure-building com-petence could be provided in the form of components of avirtual environment that engage in, for example, the com-putation of argumentation. Such support could subject us-ers’ developing understanding to a level of logical verifica-tion sufficient to ensure that they avoid the characteristicpathology of the extreme comprehension learner—namely,an inability to build closely argued detailed evidence tosupport the broad conceptual overview of concept interre-lationships.

Ford (1986) reported an early system that used rule-based expert system techniques to support procedure build-ing in this way. Learners were required to express proce-dural aspects of their learning in the form of rules. Thestipulation that the rules should execute correctly in theinference engine was designed to ensure logically soundprocedural understanding. At present, within rather re-stricted domains, such a facility could also argue with theuser in an attempt to strengthen procedure building. Anumber of argumentation systems have been developed(e.g., Minors & Sillince, 1993; Vreeswijk, 1993, 1997),which might be employable for such purposes.

One might also envisage support within a virtual envi-ronment for the development of more global description-building aspects of understanding via tools facilitating theacquisition of a view of the subject domain in terms ofbroad interrelationships between concepts. Shortcomings inthe level of integration of developing concepts, symmetries,analogies, and points of difference between concepts (char-acteristically missed by the extreme operation learner) couldbecome more visible—indeed, could be pointed out—to thistype of learner.

Systems thatengagein a particular intellectual activ-ity can to some extent support learners who are weak inits competence by supplanting or facilitating that activity.Supplantationentails engaging in the activity on behalfof the user so that his or her need for the particularcompetence is reduced.Facilitation entails providingtools that enhance the user’s own engagement in theintellectual activity—for example, by providing encour-agement, structure, review, validation, and feedback.Such support is shown in Figure 2.

FIG. 2. Intellectual support facilities within a virtual environment.


Problems for System Development

The activity of individuals learning from virtual environ-ments can be easily logged. The resultant logs may be ableto help us develop more effective models of learning whichcan be used to “drive” intelligent adaptive systems. Suchsystems could: (a) identify particular learning strategiesbeing used by individual users; (b) classify them in terms ofa provisional model of learning, which makes explicit rela-tionships between strategies, individual differences, learn-ing tasks, and learning outcomes; (c) offer individualizedstrategic support (autonomous, recommended or pre-scribed), based on the model, relating to how the user mightmost productively go forward; and (d) use feedback dataprogressively to improve the model, and the system’s ownperformance based on the model, over time.

However, a problem with analysing such logs is thatlearning strategies characteristic of particular cognitivestyles are essentially “fuzzy” concepts. Each instance of astrategy, although displaying the essential characteristics ofa particular style, is unlikely to have been encounteredbefore in precisely the same form. A range of such strate-gies—all exemplars of the same cognitive style—may con-sist of totally different idiosyncratic individual routesthrough intellectual content. In other words, the same con-cept (a particular style) may be represented by a variety ofdifferent surface structures. We need to be able to identifyunderlying patterns defined by individually distinct compo-nents. What must be identified is some relationship betweenthe components—for it is this relationship, rather than theidentity of individual components that constitutes a givenstyle.

Ford, Lloyd–Williams, and Morris (1995) report the useof a self-organizing Kohonen self-organizing neural net-work (Kohonen, 1990) in analyzing such logs to perform afuzzy classification of learning strategies. Given data fromlogs of activity from 22 students learning from a systemdesigned to teach indexing, along with knowledge of thestudents’ test performance, the neural network developedwhat was essentially a flexible model linking learning ac-tivity with different levels of performance. The systemcould correctly classify previously unseen patterns of learn-ing activity in terms of different levels of learning out-comes. After 2,000 iterations, the system correctly identi-fied 83.3% of successful strategies, and 78.2% of unsuc-cessful strategies (“success” being defined simply as abovethe mean test score).

Mullier (Mullier, 1995; Mullier & Moore, 1998) reportswork using neural networks to identify navigational strate-gies and drive adaptive hypermedia systems. Based onperformance data derived from assessment tasks encoun-tered during learning, a neural network is trained to identifymore and less successful navigational strategies. The sub-ject domain is represented by a semantic network of con-cepts. The links between concepts are weighted and, whenthe weight of a link rises above a certain threshold, the link

appears as a hyperlink available to the student in the learn-ing program.

The weightings are controlled by a user model thatapplies at a relatively global level to differentlevels ofstudent assessed in terms of their ongoing performance.Thus, when a high-performing student revisits a given node,s/he will be “promoted” in that s/he will see new nodeslinked to it, not available on the first visit. The user model,and dynamic hyperlink generation based on it also applies atthe level of theindividual student. Link creation in this caseis based on each student’s navigation history (nodes vis-ited), performance on assessment nodes in the learningprogram, and classification of navigation strategy by theneural network. It must be noted that the author is unawareof any empirical evidence of the effectiveness of this systemin terms of learning.

An advantage of using neural network approaches tomodel building is that as well as being able to handle thesort of fuzzy data processing required to classify learningstrategies to drive an adaptive system, they can also learn toimprove their own performance over time. Such a capabilitywould be useful in the development of intelligent adaptivevirtual environments, because as previously discussed, themodels of learning with which they can at present beequipped are limited, and can at best serve as provisionalstarting points. Such a system is shown in Figure 3.

Conclusions

Our models of information seeking, accessing and use—many based on experimental conditions far removed fromreal-world contexts—are arguably not sufficiently advancedto drive adaptive information systems. They cannot offerhigh levels of probability that the performance of individ-uals using them in real-world environments will be en-hanced. Having said this, they certainly offer thispossibil-ity.

However, further research is needed to establish morerobust and ecologically valid models of the interactionbetween individuals and information systems. In the field of

FIG. 3. Architecture of an adaptive virtual learning environment.


learning, research and real-course delivery may take place atthe same time. By integrating the two more closely, one cancomplement the other. Real-course delivery may benefit bybeing informed by research findings and research needs tobe conducted in ecologically valid contexts to inform ap-plied system design.

It is increasingly possible to build systems that can act atthe same time as mechanisms for real-course delivery tolarge numbers of students and research gathering instru-ments capable of enhancing our knowledge of learningprocesses and how they may (and may not) be enhanced bycomputer technology. Such tools are capable of deliveringcontent while collecting, and to some extent analyzing, dataon students’ learning behavior and linked outcomes.

Such systems may help us learn more about the extent towhich, in real-world contexts: (a) people tend consistentlyto adopt different information processing strategies; (b)such tendencies have significant effects on the performanceof the individuals using information systems; and (c) mod-els of users incorporating such differences can be used todevelop virtual environments that can enhance individuals’performance.

We have the prospect of being able to deliver trainingand education via systems that are at the same time learningto improve their own performance and providing us withresearch data in the form of logs of user and system activ-ities and performance. Such data can help us understandmore clearly the processes of effective learning and howthey may be supported and enhanced. The notion of cogni-tive styles would seem to offer a potentially importantbuilding block for such models. The capability of virtualenvironments to integrate, and allow the explicit manipula-tion of global and analytic aspects of a given body ofinformation would seem to map well onto learning require-ments suggested by research into cognitive styles. However,much further research is required before we can develop—indeed, before we can know the extent to which we arelikely to be able to develop—adaptive virtual environmentscapable of offering realistic improvements in the effective-ness of their “real-world” information processing activities.

References

Biggs, J. (1978). Individual and group differences in study processes.British Journal of Educational Psychology, 48, 266–279.

Brumby, M.N. (1982). Consistent differences in cognitive styles shown forqualitative biological problem-solving. British Journal of EducationalPsychology, 52, 244–257.

Clarke, J.A. (1993). Cognitive style and computer-assisted learning: Prob-lems and a possible solution. ALT-J, 1, 47–59.

Coan, R. (1974). The optimal personality: An empirical and theoreticalanalysis. New York: Columbia University Press.

Coombs, M.J., Gibson, R., & Alty, J.L. (1982). Acquiring a first computerlanguage: A study of individual differences. In M.J. Coombs & J.L. Alty(Eds.), Computer skills and the user interface (pp. 289–313). London:Academic Press.

Ellis, D., Ford, N., & Wood, F. (1992). Hypertext and learning styles. FinalReport of a project funded by the Learning Technology Unit. Sheffield:Employment Department.

Ellis, D., Ford, N., & Wood, F. (1993). Hypertext and learning styles. TheElectronic Library 11, 13–18.

Entwistle, N.J. (1981). Styles of learning and teaching. London: Wiley.Entwistle, N.J., Hanley, M., & Hounsell, D.J. (1979). Identifying distinc-

tive approaches to studying. Higher Education, 8, 365–380.Ford, N. (1995). Levels and types of mediation in instructional systems: An

individual differences approach. International Journal of Human-Com-puter Studies, 43, 241–259.

Ford, N. (1986). Novel uses of simple expert system technology in im-proving training and education. In Proceedings of the Second Interna-tional Expert Systems Conference, London, 30 September–2 October1986 (pp. 193–204). Oxford: Learned Information.

Ford, N. (1989). Learning styles and strategies of postgraduate students.British Journal of Educational Technology, 16(1), 65–79.

Ford, N., & Chen, Y. (in review). Individual differences, hypermedianavigation and learning: An empirical study. International Journal ofHuman–Computer Studies.

Ford, N., & Ford, R. (1992). Towards a cognitive theory of informationaccessing: An empirical study. Information Processing and Manage-ment, 29(5), 569–585.

Ford, Lloyd–Williams, M., & Morris, C. (1995). Intelligent adaptive sys-tems for information delivery in the electronic library. In: M. Collier &K. Arnold (Eds.), Electronic library and visual information research(ELVIRA 2) (pp. 137–143). London: Aslib.

Ford, N., Wood, F., & Walsh, C. (1994). Cognitive styles and searching.Online and CDROM Review, 18(2), 79–86.

Fowler, W. (1980). Cognitive differentiation and developmental learning.In: H. Rees & L. Lipsitt (Eds.), Advances in child development andbehaviour (vol. 15, pp. 163–206). New York: Academic Press.

Gazzaniga, M.S. (1998). The split brain revisited. Scientific American,279(1), 34–39.

Hofstadter, D. (1995). Concepts and creative analogies: Computer modelsof the fundamental mechanisms of thought. New York: Basic Books.

Holyoak, K.J., & Thagard, P. (1996). Mental leaps: Analogy in creativethought. Cambridge, MA: MIT Press.

Hudson, L. (1968). Contrary imaginations. Harmondsworth: Penguin.Jonassen, D.H., & Grabowski, B. (1993). Individual differences and in-

struction. New York: Allen & Bacon.Keane, M.T. (1988). Analogical problem solving. New York: Ellis Hor-

wood.Kennet, K.F., & Cropley, A.J. (1975). Uric acid and divergent thinking: A

possible relationship. British Journal of Psychology, 66, 175–180.Kohonen, T. The self-organising map. (1990). Proceedings of the IEEE,

78, 1464–1480.Landauer, T.K. (1987). Relations between cognitive psychology and com-

puter system design. In: J.M. Carroll (Ed.), Interfacing thought (pp.1–25). Cambridge, MA: MIT Press.

Landauer, T.K. (1991). Let’s get real: A position paper on the role ofcognitive psychology in the design of humanly useful and usable sys-tems. In: J.M. Carroll (Ed.), Designing interaction: Psychology at thehuman-computer interface (pp. 60–73). Cambridge: Cambridge Univer-sity Press.

Marton, F., & Saljo, R. (1976). On qualitative differences in learning.I—Outcome and process. British Journal of Educational Psychology, 46,4–11.

Mayer, R.E. (1992). Thinking, problem solving, cognition (2nd ed.). NewYork: W.H. Freeman.

Miller, A. (1987). Cognitive styles: An integrated model. EducationalPsychology, 7, 251–268.

Minors, B., & Sillince, J. (1993). Extending hypertext for CAI by usingargumentation. In Proceedings of the Workshop on Collaborative Prob-lem Solving, Conference on Artificial Intelligence in Education (AI-ED93) (pp. 56–58). Edinburgh. Edinburgh University.

Minsky, M. (1986). The society of mind. New York: Simon & Schuster.Mitchell, M. (1993). Analogy-making as perception: A computer model.

Cambridge, MA: MIT Press.Mullier, D.J. (1995). Using a neural network to model hypermedia brows-

ing. In A.R. Johnson, I.W. Eames, & R.E. Flori (Eds.), Proceedings of


the International Conference Hypermedia in Sheffield ’95, 3–5 July 1995(pp. 115–122). Sheffield: Sheffield Academic Press.

Mullier, D.J., & Moore, D.J. (1998). A Web based intelligent tutoringsystem [On-line]. Available: http://www.lmu.ac.uk/ies/conference/mul-lier.html.

Pask, G. (1976a). Conversational techniques in the study and practice ofeducation. British Journal of Educational Psychology, 46, 12–25.

Pask, G. (1976b). Styles and strategies of learning. British Journal ofEducational Psychology 46, 128–148.

Pask, G. (1976c). Conversation theory: Applications in education andepistemology. Amsterdam: Elsevier 1976.

Pask, G. (1979). Final report of S.S.R.C. Research Programme HR 2708.Richmond, Surrey: System Research Ltd.

Pask, G. (1988). Learning strategies, teaching strategies, and conceptual orlearning style. In R.R. Schmeck (Ed.), Learning strategies and learningstyles (pp. 83–99). New York: Plenum Press.

Pask, G., & Scott, B.C.E. (1972). Learning strategies and individualcompetence. International Journal of Man–Machine Studies, 4, 217–253.

Pask, G., & Scott, B.C.E. (1973). CASTE: A system for exhibiting learningstrategies and regulating uncertainty. International Journal of Man–Machine Studies, 5, 17–52.

Riding, R.J. (1991). Cognitive styles analysis. Birmingham: Learning andTraining Technology.

Riding, R.J., & Cheema, I. (1991). Cognitive styles—An overview andintegration, Educational Psychology, 11, 193–215.

Riding, R.J., & Sadler–Smith, E. (1992). Type of instructional material,cognitive style and learning performance. Educational Studies, 18, 323–340.

Robertson, I.T. (1977). An investigation of some relationships betweenlearning and personality. Unpublished Ph.D. Milton Keynes: Open Uni-versity.

Vreeswijk, G.A.W. (1993). Defeasible dialectics: A controversy-orientedapproach towards defeasible argumentation. Journal of Logic and Com-putation, 3, 3–27.

Vreeswijk, G.A.W. (1997). IACAS: An interactive argumentation system[On-line]. Available: http://www.cs.rulimburg.nl/;vreeswyk/iacas.htm.

Witkin, H.A., Moore, C.A., Goodenough, D.R., & Cox, P.W. (1977).Field-dependent and field-independent cognitive styles and their educa-tional implications. Review of Educational Research, 47, 1–64.

Wood, F., Ford, N., & Walsh, C. (1992). Online searching and cognitivestyles. Final report to the British Library. London: British Library.

Wood, F., Ford, N., Miller, D., Sobczyk, G., & Duffin, R. (1996). Infor-mation skills, searching behaviour and cognitive styles for student cen-tred learning: A computer-assisted learning approach. Journal of Infor-mation Science, 22(2), 79–92.


cognitive styles and virtual environments

Documents