Hypermedia environments and adaptive instruction

Hypermedia environments and adaptiveinstruction$

Pat-Anthony Federico*

Institute for Defense Education and Analysis, Naval Postgraduate School, Criscuolo Hall,

101 North Street, Monterey, CA 93943-5227, USA

Abstract

The relevant professional literature is reviewed and discussed regarding the following sali-

ent issues: (1) hypermedia environments enable learner control; (2) this is re¯ected in individ-uals' navigational paths through network-based subject matter; (3) these student tracks can becaptured in log ®les; and (4) this information can be used by arti®cially intelligent tutors to

implement adaptive instruction. Assuming (1) most schools, colleges, universities, and cor-porations will eventually o�er distributed students network-based instruction for particularrefresher preparation and certain core courses, and (2) adaptive intelligent tutors are crucialcomponents of course management systems, recommendations for research, development,

and evaluation, extracted from the discussion, are made to appropriate sponsors, academicadministrators, faculty members, training managers, and instructional developers, interestedin realizing on-line learning. Published by Elsevier Science Ltd.

Keywords: Hypermedia environments; Adaptive instruction

1. Campus context

Many academic institutions and private corporations recognize the need forlearning at a distance and continuing education and training on the job or in theschoolhouse. Traditional residential classroom instruction is expensive and timeconsuming, requiring people to travel to courses' locations. Fortunately, we are inthe midst of a paradigm shift in education and training from `classroom centric' to`network centric'. Internet-based information and communication technologies arechanging how instruction and assessment are being conducted in innovative schools,

Computers in Human Behavior 15 (1999) 653±692

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0747-5632/99/$ - see front matter Published by Elsevier Science Ltd.

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* Tel.: +1-831-656-5719; fax: +1-831-656-3547.

E-mail address: [email protected]$ Opinions or assertions contained herein are those of the author and are not to be construed as o�-

cial or re¯ecting the views of the Department of the Navy.

colleges, universities, and corporations throughout the world (Federico, 1997). Edu-cation and training are experiencing a noticeable transition ``. . .from the traditionalcentralized, local, classroom-teacher focused approach, to a de-centralized, global,network-based, student-focused one. . .'' (http://www.altgrp.com/Vision.html).The recent digital fusion, the merger of computer, communication, and informa-

tion technologies, enables a multimedia capability on the Internet or intranets. Thisconsolidated technology can be used to complement customary instruction, or toprovide entire courses over networks, which are becoming more capable of e�-ciently delivering the complete multimedia spectrum. In pursuit of such e�ciencies,many academic and corporate institutions intend to transition, or already transi-tioned, prerequisite refresher preparation and a signi®cant segment of scholasticcore courses to network-based interactive multimedia collaborative instruction.The expected payo�s for organizations planning to implement these innovative

systems include the following: education and training do not have to be con®ned toclassrooms and the campus, and students and teachers do not have to be present atthe same place and time for instruction. Consequently, costs associated with resi-dential education and training can be lowered by: (1) eliminating some travelexpenses to the campus; (2) shortening amounts of time students must remain on sitefor instruction; (3) achieving traditional educational goals more rapidly by refresh-ing prerequisite skills beforehand at a distance; (4) delivering pedagogy via networksto remote individuals or classes ashore, a¯oat, or a¯ight; (5) sharing faculty exper-tise throughout a distributed virtual school, college, or university; (6) expandingcourse o�erings without correspondingly increasing on-board teachers; and (7)incorporating education and training into work schedules thereby broadeningproductivity.By leveraging network-based technology, these institutions would be able to: (1)

o�er some courses more frequently; (2) reach a more geographically dispersedaudience; (3) enhance learning through curricula o�ered anytime and anyplace; (4)enable continual access to instruction and information; and (5) ensure outdatededucational materials are readily replaced by the most recent versions.Therefore, the general goals of proposed and actual analysis, development,

implementation, and evaluation e�orts are to: (1) assess the best uses of state-of-the-art technology for network-based education and training, as viable instructionalsystems to supplement residential and nonresidential courses; (2) explore the pos-sibilities of successfully instructing via networks to complement curricula, and capi-talizing upon this emerging technology to sustain traditional and nontraditionaleducation, while demonstrating the learning and cost e�ectiveness of these innova-tive systems; (3) ascertain and establish the necessary infrastructure required forimplementing network-based instruction, in terms of hardware, software, and tech-nical assistance; (4) identify other salient institutional support issues, such as requi-site faculty development and optimal organizational structure, essential to employe�ectively and e�ciently this creative educational technology, from innovation dif-fusion and sociotechnical systems perspectives; (5) determine the requisite infra-structure for conceivable virtual institutions, consisting of distributed schools,colleges, universities, and corporations; (6) lead the creation of these cybercampuses,

654 P-A. Federico /Computers in Human Behavior 15 (1999) 653±692

sustaining not only traditional and nontraditional education, but also on-the-jobdecision aiding and electronic performance support, therefore improving schools'positions as instructional resources for their own organizations and external entities;(7) investigate the feasibility and viability of these virtual universities, as well asassess the pedagogic and cost e�ectiveness of these cybercampuses, if they are to beestablished as expected; and (8) estimate the likelihood of students completingcourses remotely via network-based technology, thereby virtually enlarging and ®ll-ing classes, as means of increasing class sizes and surviving as academic or trainingorganizations, because of anticipated or actual budgetary constraints.Within this campus context, the technical objectives of this reported work were to:

(1) review and discuss the relevant professional literature regarding the followingsalient issues: (a) hypermedia educational environments enable learner control, (b)this is re¯ected in individuals' navigational paths through network-based subjectmatter, (c) these student tracks can be captured in log ®les, and (d) this informationcan be used by arti®cially intelligent tutors to implement adaptive instruction; and(2) make recommendations extracted from the discussion to appropriate sponsors,academic administrators, faculty members, training managers, and instructionaldevelopers, interested in realizing on-line learning.

2. Adaptive instruction

2.1. Historical Perspective

Several psychologists (e.g. Bracht, 1970; Cronbach, 1957, 1967; Cronbach &Gleser, 1965; Cronbach & Snow, 1969; Gagne, 1967; Glaser, 1967, 1972, 1977;Jensen, 1967, 1968) asserted no single teaching method is best for all students. If thisis true, then students will be able to reach educational goals more e�ciently wheninstructional procedures are adapted to individual di�erences. This would be possi-ble if instructional treatments were accommodated to premeasured student apti-tudes. According to Cronbach (1957, p. 681), it is best to ``. . .design treatments notto ®t the average person, but to ®t groups of students with particular aptitude pat-terns,'' or conversely, to ``. . .seek out aptitudes which correspond to (interact with)modi®able aspects of the treatment.'' In this context, aptitude is ``. . .any character-istic of the individual that increases (or impairs) his probability of success in a giventreatment. . .''; and treatment, ``. . .variations in the pace or style of instruction. . .''(Cronbach & Snow, 1969, p. 7). Aptitude includes any index of individual di�erencedistinguishing among students and treatments with respect to learning outcomes. Itdoes not refer to general and mental ability (Snow & Solomon, 1968). As used in theliterature, though, aptitude does indicate a rather enduring trait from which extra-polations are made concerning appropriate teaching treatments (Cronbach & Snow,1969). However unintentional, this trait aspect of aptitude connotes a tendency thatis relatively stable over short intervals (Tobias, 1976).Cronbach (1967) discussed three models for accommodating instruction to speci®c

students. The ®rst involved simply manipulating the pace of teaching; the second,

P-A. Federico /Computers in Human Behavior 15 (1999) 653±692 655

tracking homogeneous types of students who were given general treatments derivedfrom instructional macro-theories (i.e. those entailing decision rules that prescribefeedback, prompting, reinforcement, etc.); and the third, designing instructionaltreatments as a function of how students normally acquire and manipulate material.The latter is much more accommodating in that it permits the modi®cation of notonly teaching treatments, but also student cognitive aptitudes. For the most part,Cronbach's models stressed pretask instructional adaptation (Tennyson, 1975), i.e.they presumed instructional treatments can be determined from empirically estab-lished aptitude measurements taken before the actual learning situation, andregression equations can be derived for assigning certain types of students to speci®cinstructional treatments.It was asserted that aptitude measurements can be used for adapting instructional

treatments to student characteristics only if aptitudes and treatments interact(Cronbach, 1967; Cronbach & Gleser, 1965; Cronbach & Snow, 1969, 1977), i.e.aptitude measures must be developed to predict which individuals will learn bestfrom speci®c instructional treatments. If such measures are available, then teachingtreatments can be prescribed for types of students having speci®c aptitude pro®les.This can be facilitated by the capability to discriminate among instructional treat-ments to maximize their interactions with aptitude measures. Cronbach (1967) pro-posed a comprehensive program of research to identify aptitudes that interactbest with treatments. This area of research has been labeled aptitude±treatment±interaction (ATI). The emphasis of ATI research was on identifying aptitude meas-ures that are useful for selecting instructional treatments to maximize individualattainment of speci®c educational objectives (Glaser, 1972).Supporting evidence was obtained when signi®cant interactions were established

between alternative instructional treatments and individual di�erences, or persono-logical variables. In ATI research, the personological variable was de®ned as anymeasure of individual characteristics (e.g. IQ, scienti®c interest, aptitude, anxiety;Bracht, 1970). ATIs were usually sought in educational research by employing two-by-two factorial analysis of variance experimental designs. It was hoped one perso-nological variable correlated signi®cantly with learning performance under oneinstructional treatment, and the other personological variable correlated sig-ni®cantly with learning performance under the other instructional treatment.Very few empirical data have been obtained to support the ATI idea consistently

and conclusively (Berliner & Cahen, 1973; Boutwell & Barton, 1974; Bracht,1969,1970; Bracht & Glass, 1968; Cronbach & Snow, 1969, 1977; Gage & Unrah,1967; Roberts, 1968±69; Tobias, 1976). Bracht (1969, 1970) surveyed and analyzednumerous ATI studies that compared two or more alternative instructional treat-ments designed to attain the same educational objectives, and included one or moreindividual di�erence variables for evaluating distinct treatments at speci®c values ofthese variables. Ninety investigations were assessed for signi®cant disordinal inter-actions, which exist when the regression lines for instructional treatments intersectwithin the range of the aptitude measures or other personological variables underinvestigation. In these studies, 108 ATIs were scrutinized, but only ®ve werefound to have signi®cant disordinal interactions. Of the ®ve, just one included an


educationally related individual di�erence variable, namely, under- or over-achievement. Bracht (1969, 1970) drew two general conclusions from his review: noavailable data demonstrate conclusively measures of general ability and achievementare useful for discriminating among alternative instructional treatments for studentswithin the age range, and no analyses seem to have been conducted before studyingATI e�ects of the di�erent kinds of information processing elicited in the studentsby the teaching treatments themselves. Consequently, these experiments typicallyassessed ATI e�ects as an afterthought, and individual di�erence variables were notconsidered in an information-processing frame of reference.Cronbach and Snow (1969) reported an extensive, systematic analysis of many of

ATI's rami®cations. They concluded, as Bracht (1969, 1970) did, that ATI e�ects areseldom established empirically. Only infrequently have signi®cant disordinal inter-actions been found and reported. They suggested these negative results could be dueto the psychometric development of the aptitude measures for selection purposesrather than for learning-performance purposes. Possibly, the instructional treat-ments were too poorly conceived and implemented for them to interact with learningand performance processes. Roberts (1968±69) reviewed the literature for ATIresults and inferred that the consequences of ATI are indeed complex, and theincorporation of practice e�ects makes the phenomenon even more complex. Themajority of the reported ATI studies have been conducted in the laboratory usingarti®cial tasks, thus precluding valid generalization to the real classroom. Beforesuch a generalization can be made, more ATI investigations must be conductedusing more appropriate learning materials.In most of the ATI studies surveyed, an extremely large battery of aptitude tests

has been administered. Although these psychometric measures may have had mod-erate reliabilities and signi®cant correlations with performance measures, the prac-tical use of the battery is precluded. Therefore, it seemed that individualizedinstruction based upon aptitude tests has been restrained because most ATI investi-gations have not had important impact upon the classroom. The promises of theATI idea have not been ful®lled. It has been almost impossible to extrapolateresearch results into useful adaptive instructional systems (Boutwell & Barton, 1974;Gage & Unruh, 1967). Apparently, the usefulness of the ATI construct was still tobe demonstrated (Tobias, 1976).Cronbach and Snow (1977) re-examined the ATI literature to gather additional

evidence concerning the evidence of ATIs, and to identify ATI hypotheses worthy offurther study. The major impetus driving investigations of ATIs has been the ideathat policy decisions, regarding student placement and adaptive instruction, couldbe validly derived from established ATI generalizations. However, the ATI literatureis plagued by inconsistencies that preclude appropriate extrapolations. If there is atrend in the literature, it would seem to be that many of the ®ndings of ATI studiesare incompatible. Consequently, it is di�cult to make sound recommendationsregarding adaptive instructional procedures. No ATIs have been substantiated tothe extent they can be used unequivocally as prescriptions for accommodatinginstruction to student characteristics. Also, the majority of ATI studies su�er fromlack of replication or generalization. Although some ATIs have been empirically


established, they have not been corroborated. In other investigations, either ATIshave not been demonstrated at all, or the results have not been interpretable, thusemphasizing the elusiveness of ATIs. Because customary methods employed forexperimenting with ATIs have not been very successful in producing a set of pre-scriptive procedures for adapting teaching treatments, it seemed reasonable to seekalternative approaches for accommodating instructional strategies to individual dif-ferences that exist among students (Federico, 1980).

2.2. Cognitive processes

Cognitive process perspectives of learning and performance, as opposed to tradi-tional behavioristic theories, stressed the use of cognitive operations or mechanismsin the acquisition and retention of knowledge. Within this framework, students wereperceived as processors of information input, manipulators of intellectual through-put, and producers of performance output. Some of the operations that learnersperformed during these intervening stages of cognition include selecting, encoding,organizing, storing, retrieving, decoding, and generating information. All of thesemediating activities were largely under the voluntary and conscious control of thelearner (Boutwell & Barton, 1974; Glaser, 1972; Glaser & Resnick, 1972; Melton,1967; Rigney & Towne, 1970; Rohwer, 1970a, b, 1971; Seidel, 1971; Tobias, 1976).It was speculated that cognitive processes should be considered in the design and

development of adaptive instructional systems. Customary measures of abilities,aptitudes, and other attributes have been produced primarily for predictive pur-poses. These instruments were not created as tests of cognitive processes that med-iate distinct types of learning and performance. Therefore, traditional psychometricmeasures are not indices that suggest how to support and facilitate the process ofacquiring knowledge or evoking performance (Federico, 1978). It appeared that ifinstruction is to be successfully accommodated to individual di�erences amonglearners, then mediation mechanisms or their correlates must be measured andemployed to prescribe particular teaching treatments. Intervening processes used bydistinct students to learn, retain, and retrieve a speci®c subject matter must be ana-lyzed before the most appropriate instructional technique can be selected. It wastheorized that ascertaining the nature of this mediating cognitive activity allowed theselection of alternative teaching strategies and tactics that increased the e�ectivenessand e�ciency of instruction.Within this conceptual structure it was not necessary or su�cient to speculate or

determine which abilities or aptitudes might be related to learning and performance.In the traditional ATI orientation (Cronbach & Snow, 1969, 1977), it had beencustomary to examine variations in abilities and aptitudes among students to selectinstructional treatments, and to neglect di�erences in intervening cognitive activitiesamong these same students. However, it was conjectured that the very processesintrinsic to learning should be paramount considerations in adapting instructionaltechniques to individual di�erences. Accommodative instructional systems shouldbe designed around relevant cognitive processes, not irrelevant mental abilitiesand aptitudes. In this context, the psychological processes employed by students in


taking these ability and aptitude tests are actually more important than the psycho-metric results themselves (DiVesta, 1973; French, 1965; Glaser, 1972; Rigney &Towne, 1970).Research results (e.g. Coop & Sigel, 1971) suggested that there is a wide range of

variability among individuals, regarding the psychological processes they used tomediate acquisition, organization, retention, and generation of knowledge. Thesedi�erences may be attributed to students adopting di�erent learning sets that theyperceived to be pertinent to the task at hand. Therefore, it was contemplated thatthe disparity among students in acquiring, retaining, and retrieving information maynot be due to dissimilarities in general abilities and aptitudes, but rather to di�er-ences in learning sets, competencies, schemata, knowledges, and rules the studentsbring into the instructional environment (Glaser, 1976a, b; Rumelhart & Ortony,1977; Scandura, 1973, 1977). This implied that to master a primary task, the studentshould learn the supporting subordinate skills and the proper integration of thesesecondary competencies. These sustaining learning sets, schemata, skills, andknowledges were considered cognitive mediators themselves, facilitating the transferof lower-level competencies to higher-level competencies in the learning hierarchy.The supporting internal processes, or mental mechanisms, employed in the initialphases of learning were likely to be quite distinct from those used in the ®nal phasesof learning. This shift in importance pertaining to intervening cognitive processesused during task mastery was thought advantageous for adapting instruction toindividual di�erences (Boutwell & Barton, 1974; Briggs, 1968; Fleishman & Bartlett,1969; Gagne & Paradise, 1961; Snow, 1976).Traditional psychometric theory, ironically, had not su�ciently considered the

variability among individuals. Correlations between psychometric measures ofabilities, aptitudes, and other attributes and performance indices did not provideinsight into the nature of the mental mechanisms accounting for these behavioraldi�erences. Instead of normatively based, psychometric measures of abilities andaptitudes with their static, trait-like properties, what was needed were individuallybased, idiosyncratic indices of cognitive processes with their dynamic, state-likeproperties. It was asserted that with them, instruction could be optimized by pre-scribing treatments to support mediation activity or modify detrimental, interfer-ing mediation activity (Glaser & Resnick, 1972; Hunt & Lansman, 1975; Seidel,1971).Su�cient empirical evidence existed to support the thesis that intervening pro-

cesses are inherently involved in learning and performance (e.g. Estes, 1975±76;Melton & Martin, 1972; Paivio, 1971; Solso, 1973; Tulving & Donaldson, 1972). Itappeared very likely that individual variability in acquiring, retaining, and retrievingknowledge could be analyzed in terms of the processes intrinsic to this cognition.Within this context, cognitive processes themselves were considered individual dif-ference variables that were potentially useful for adaptive instructional purposes.Seldom had variations in mediation mechanisms or psychological processes beenemployed to accommodate pedagogical procedures to di�erences among pupils. Notto examine the likelihood of using these mediational processes for adaptive instruc-tion was to negate the very essence of the individual di�erences in learning and


performance (Boutwell & Barton, 1974; Coop & Sigel, 1971; Glaser, 1972, 1976b,1977; Hunt, 1976; Labouvie-Vief, Levin & Urberg, 1975; Melton, 1967).It was thought that this information might be used either to adapt instructional

treatments to maintain mediation mechanisms, or to modify the mental elaborationitself so that it is more conducive to task mastery. In appropriate cases, individualscould be taught the mediating processes or elaborating techniques contributing tolearning or performing a particular task. Many di�erent instructional treatmentsspeci®c to cognitive processes were possible (Coop & Sigel, 1971; Glaser, 1972, 1976b;McKeachie, 1974; Rigney, 1976; Rohwer, 1970a, b; Schroder, Driver & Streufert,1967; Snow & Solomon, 1968). It appeared highly probable that the new aptitudesor cognitive processes could be modi®ed by appropriate training to produce apotentially powerful procedure for adaptive instructional purposes. Research wasadvocated to resolve this still salient issue: is it better to assign instructional treat-ments to capitalize on potent cognitive processes, or to assign instructional treatmentsto improve impotent cognitive processes (Berliner & Cahen, 1973)?

2.3. Within-task cognitive mechanisms and dynamic acquisition measures

Some researchers (Leherissey, O'Neil, Heinrich & Hansen, 1973; O'Neil,Spielberger & Hansen, 1969; Tennyson, 1975; Tennyson & Boutwell, 1973) attemp-ted to establish ATIs using within-task measures rather than pretask measures. Ithad been customary to employ pretask measures of abilities, aptitudes, and otherattributes to predict learner's behavior during instruction. This was done beforeprescribing speci®c teaching treatments to individuals as a function of their incom-ing characteristics. It had been suggested that within-task measures of studentbehavior and performance while actually in the instructional situation itselfÐsuchas number of errors, responsive latencies, and emotive statesÐcould be used foradaptive purposes. It was speculated that such measures taken during the verycourse of learning may provide for the manipulation and optimization of instruc-tional treatments and sequences on a much more re®ned scale, such as varying theamount of prompting, feedback, incentives, and examples (Atkinson, 1976). Thismicro-treatment approach to adaptive instruction was an alternative to the macro-treatment approach proposed by the traditional ATI formulation, which employedpremeasures for selecting teaching treatments (Cronbach, 1967; Cronbach & Gleser,1965; Cronbach & Snow, 1969, 1977).The use of micro-treatments based upon within-task measures did not preclude

the use of macro-treatments based upon pretask measures. It was theorized thatthese distinct instructional strategies should be utilized to complement one another,i.e. once the optimal macro-instructional treatment had been selected for an indivi-dual as a function of pretask measures, micro-instructional treatments could beselected for the same individual as a function of within-task measures. If coursecontent was complex, then it was thought possible to design an instructional systemwith multiple modules and entry points. Under such circumstances, pretask meas-ures may be employed to determine the appropriate level of di�culty for commen-cing instruction for an individual, and within-task measures may be employed to


manipulate treatments for a student as a function of his or her continuously mon-itored learning behavior. The advocated criterion for accommodating instruction,then, was the correct classi®cation of the student's successes and failures manifestedover the course of learning. This was the suggested sine qua non for optimally pre-scribing instructional treatments. In addition, it was speculated that the increasedreliability of a sequence of within-task state measures, as opposed to a single pretasktrait measure, should signi®cantly improve the validity of adaptive instructionaldecisions.The identi®cation of ATIs may be inadequate and unnecessary for individualizing

instruction. Merrill (1975) systematically examined some of the assumptions implicitto the ATI approach for adapting teaching techniques to individual di�erencesadvocated by Cronbach and Snow (1977). In contrast to what was inherent in theATI formulation pertaining to the permanence and pervasiveness of di�erentialindividual attributes, Merrill (1975) emphasized that it is the momentary mutabilityof these characteristics that determines the optimal instructional treatment for thelearner, i.e. student performance is not a�ected by stable attributes but by theirdynamic characteristics. Likewise, it is not ®xed, preset instructional strategies thathave utility for ATIs but transient teaching tactics. For adapting instruction toindividual di�erences, Merrill (1975) maintained it may be better to assume thatdynamic, state, idiosyncratic variables are more useful for predicting pupil perfor-mance than stable, trait, aptitude measures.It was speculated that adapting instruction based upon traditional ATI investiga-

tions would probably produce pupils who were instructional system dependent.Rather than having teaching techniques selected for them, it was proposed thatpassive students should be given the opportunity to choose actively instructionaltreatments. Learners could become system independent by enabling them tomanipulate and accommodate treatments to their own momentary cognitiverequirements. This could be accomplished by designing a dynamically adaptableinstructional system, in which students actively and continuously select instructionaltreatments most appropriate to their idiosyncratic states. The measurement of stabletrait-like aptitudes was no longer a prerequisite for the implementation of thisactively accommodating individualized instruction. Merrill's (1975) learner controlapproach to adaptive pedagogy was an important departure that went noticeablybeyond the ATI formulation supported by Cronbach and Snow (1977).Learner control was considered an alternative procedure for accommodating

instruction to the dynamic characteristics of students. However, its e�ectivenessdepends to a large extent on how well each individual student can decide whichlearning strategy is optimal for him or her at any one moment. Some students maynot be as adept as others at selecting appropriate learning strategies for themselvesor at managing their own instruction. Also, some students may not even care tocontrol their own learning, or may feel they are being shortchanged because theteacher is not there constantly to guide them. What little evidence there wasregarding learner control (Steinberg, 1977) underscored the fact that much remainedto be discovered regarding this pedagogical procedure. This was especially soregarding this still salient question: which individual characteristics of students are


indicators of success in this dynamic instructional environment? Not all learners arecapable of, or inclined toward, exercising any control over their learning strategies(Beard, Lorton, Searle & Atkinson, 1973). Some may believe that this is anothercase of the blind leading the blind. What was urgently needed then, and still isrequired now, was research that identi®ed: (1) which cognitive characteristics ofstudents are salient for learner control; and (2) which students can su�ciently func-tion and bene®t in this dynamic instructional environment. Tests measuring mutu-able and particular properties of students may be more amenable to ATIs(Goldberg, 1972).In summary, a primary principle of adaptive instruction is that no single instruc-

tional strategy is best for all students. Consequently, students will be able to achievelearning goals more e�ciently, when pedagogical procedures are adapted oraccommodated to their individual di�erences (Federico, 1991). Some student attri-butes or characteristics historically considered or studied as individual di�erenceindices for adaptive instruction have alternatively included: pretask cognitive apti-tudes and abilities as well as within-task cognitive mechanisms and dynamic acqui-sition measures (Federico, 1980, 1987). Theoretical or conceptual problems, inaddition to methodological di�culties, have limited the practical payo� fromresearch in adaptive instruction, using these mentioned indices of individual di�er-ences (Federico, 1991).

3. Hypermedia environments

3.1. Essential features

Hypermedia refer to on-line settings where networks of multimedia nodes con-nected by links are used to present information and manage retrieval. Nodes,containing texts, graphics, videos, audios, animations, models, simulations, visuali-zations, are accessed and viewed by interactive browsers, such as Netscape's Navi-gator or Microsoft's Internet Explorer. Hypermedia can be considered an umbrellaterm, referring to any sort of computer-stored information, which is related andretrieved via links. Although connectivity among nodes is constrained by the designof the speci®c network-based environment, navigational paths through the nodes areultimately determined by the user, who freely controls the movement among nodes,according to intrinsic interests and present goals.According to Large (1996), hypermedia educational environments attracted the

attention of instructional designers, chie¯y because of the adaptability or ¯exibilitythey a�ord individual learners. Students are able to follow links or paths throughon-line content within the context created by the developer, or chart their ownroutes according to individually perceived requirements, changing dynamically dur-ing the process of acquisition. Learners are no longer locked-in to linear lessonsequences, but can actively control their own instructional strategies within hyper-media-based scholastic settings: ``. . .choosing what to view, when to view, for howlong to view and how many times to view. . .'' (Large, 1996, p. 104). Hypermedia


allows learner control: the creation of links and connection of di�erent pieces ofinformation. Therefore, a student is actively involved in constructing the learningenvironment. This interactivity enables timely control over the pedagogical process,making possible adaptation of instruction by learners themselves to their own per-ceived individual needs.In hypermedia educational settings,

. . .the individual student. . .[is] paramount in mediating learning. Instructionalsequence decisions and options are intended to adapt the sequences to individualstudent di�erences. The student becomes the focus of the learner-instructiontransaction rather than the instructional materials per se. [Learner] [c]ontrolgives individuals the possibility to make choices and to a�ect outcomes. . .[S]tudents need to be given control of learning because they can learn betterhow to learn. . . (Large, 1996, p. 97).

Individuals vary in their amount of involvement in learner control, and mayexplore nodes not intended by the instructional developer.

[N]avigational performance is a relational property among parts of a system(i.e., the individual, the task, the hypermedia, and the context in which learningoccurs). . . Hypermedia is conceived as an evolving environment that changesbased on the skills and intentions of each individual user with respect to thea�ordances of the medium. (Barab, Bowdish & Lawless, 1997, p. 24).

``[T]he learner is viewed as part of an instructional ecosystem, simultaneously shap-ing and being shaped by the instruction encountered. . .'' (Misanchuk & Schwier,1992, p. 361).Learning can be considered as acquisition and reorganization of knowledge

structures: semantic or neural networks, or associative architectures or schemata.Hypermedia environments enable and mimic association of knowledge structures,since new nodes can be created and linked to extant nodes. When students areengaged in hypermedia contexts, controlling acquisition by selecting certain links,they are intrinsically modeling the learning process, by making their own knowledgeassemblies or associations. However, some researchers (Locatis, Letourneau &Banvard, 1989) are skeptical of this position and question the educational relevanceof hypermedia environments.``[L]inking information may be a necessary condition for learning, but it is not

su�cient. Links can be made in many ways, including totally arbitrary ones withlittle semblance to how people associate ideas. . .'' (Large, 1996, p. 98). Requiringlearners to link nodes in these settings, and keep track of these connections ornavigational paths can easily increase their cognitive loads, which, ironically, canreadily interfere with the acquisition of knowledge. Also, enabling learner controlassumes students have su�cient knowledge to select and make optimal links inhypermedia environments, which is not always true, especially with novices. Theseissues suggest the need for an arti®cially intelligent tutor that can guide learning


strategies, or adapt navigational paths, through the hypermedia-based content, inorder to facilitate students' control of the knowledge acquisition process.In addition to hypermedia, network-based instruction uses hypertext: nonlinear

text which is broken down into pieces or chunks named nodes, consisting usually ofa single concept or idea. Nodes are limited to the amount of information that can besqueezed into a computer display or single screen, and are linked together logically,not necessarily sequentially. By selecting and clicking on `hot links' with a mouse, alearner chooses which link to follow. Nodes represent facts, concepts, procedures, orprinciples, and links represent relationships among this instructional content.

Hypertext is a network of nodes connected by links. A node is the basic unitused to store information. . . A link is the relationship between two nodes. . . [A]path is. . .sequence of nodes visited during a search. . . [P]ath length is de®ned asthe number of nodes in the path. . . (Qiu, 1994, p. 131).

Nodes can be connected or associated with other nodes containing superordinate orsubordinate information, i.e. linked to additional nodes hierarchically or horizon-tally. Distinct cognitive models underlie di�erent nodal patterns: structural theorieswhich represent knowledge as interconnected hierarchical architectures, or connec-tionist theories which represent knowledge as associated neural networks (Large,1996). Evidently, distinct hypertext environments: network and hierarchical struc-tures, do not result in signi®cantly di�erent acquisition outcomes for individualswith dissimilar learning styles (Melara, 1996).Hypertext speci®es ``computer-based texts that are read in a non-linear fashion

and that are organized in multiple dimensions'' (Landow, 1992, p. 166). Interactingwith hypertext, students are deciding individually when, and what order, certainfacts, concepts, principles, and rules will be learned, i.e. they are in control of theirown acquisition processes (Jonassen, 1986). Students' choices in hypertext environ-ments are re¯ected in their mouse clicks on icons, buttons, objects, or hot words,permitting them to navigate through the knowledge base and control the sequenceof learning on-line content. Merrill (1975) maintained that allowing learner controlenables students to assess the consequences of directing acquisition processes anddeveloping monitoring abilities, i.e. metacognitive skills. However, without clearlyspeci®ed instructional goals, students can become readily disoriented or `lost incyberspace'. This seems to be especially so in complex hypertext environments, per-mitting many navigational choices, producing alleged advantages (enhancing cogni-tive engagements) and disadvantages (losing learners in cyberspace) (Barab,Bowdish, Young & Owen, 1996).

[T]he growth of hypertext, hypermedia, and multimedia provided capabilitiesnecessary for developing complex, content-oriented learning environments. . .Recent advances in learning theory have fueled more rapid and extensive revo-lution in computer-supported learning systems. Rather than using the computeras a delivery vehicle for displaying and purveying information, generativelearning systems and knowledge construction environments are designed to


form partnerships with learners/users, to distribute the cognitive load andresponsibility to the part of the learning system that performs the best. They areno longer passive recipients of information, controlled by programmed com-puter algorithms. They are actively involved in knowledge construction andmeaning making. The computer's computational functionality is being used tosupport these processes rather than to present information. Currently, hyper-media and multimedia are the chosen platforms for implementing these know-ledge construction environments, largely because of their open architecture. . .These systems are more constructivistic, that is, they assume that learning is aprocess of knowledge construction rather reproduction. They believe that learn-ers should be in control of their learning and doing rather than the computer,that the control of the system and the processes a�orded by it should be inter-nal to the learner. . . (Jonassen, 1993, pp. 332±333).

Lawless and Brown (1997) considered learner control and navigation in multi-media environments from a schema-theory perspective, regarding acquisition as anactive constructive process. They examined how distinct student characteristics, e.g.prior knowledge, self e�cacy, and present interest, and di�erent external con-straints, e.g. learner control, instructional design, and control extent, a�ect the pro-cess of learning. According to these researchers, individuals must develop andemploy necessary cognitive skills, in addition to those typically applied in ordinaryinstructional settings, for properly exercising learner control in multimedia envir-onments.

[T]he development of these skills is centered around one's ability to makemindful navigational selections. While the ability to control one's instructionalsequence can enhance learning and heighten attitudes and self-e�cacy, unrest-ricted control and lack of learning goals can dampen the power of learning insuch an environment. . . (Lawless & Brown, 1997, p. 127).

These investigators asserted that decisions concerning learner control and naviga-tion must consider current learning theories, cognitive models, and instructionalparadigms.

We must be cautious not to make the instructional system ®t the technology butmake the technology ®t the instructional systems and formats that have beendemonstrated to be e�ective. Technology is not e�ective learning in and ofitself, but merely provides a forum for e�ective learning. . . (Lawless & Brown,1997, p. 127).

In ordinary learning environments, students must follow the sequential orderimposed by employed educational media, e.g. texts, audios, videos, under programcontrol, in order to acquire and understand the information. Whereas in multimediainstructional settings, information is presented randomly or nonlinearly to students,who decide for themselves what content to access, as well as the speci®c sequence to


engage certain facts, concepts, principles, or rules, i.e. individuals direct their ownunique sequences of tailored instruction under learner control by navigating themultimedia landscape (Lawless & Brown, 1997).However, for students to thrive in these instructional settings they must possess

su�cient domain knowledge, experience, and ability (Shyu & Brown, 1992, 1995).Without these attributes, students may not be able to capitalize adequately onlearner control, ``. . .[which] cannot be expected to overcome the persistent fact thatindividual characteristics not under the control of the individual will determine to asigni®cant extent what and how much that individual will learn in a given instruc-tional setting. . .'' (Snow, 1980, pp. 152±153). Consequently, students who do notpossess required cognitive and metacognitive characteristics will likely need gui-dance or coaching in order to exercise e�ective and e�cient learner control fornavigating multimedia subject matter. This suggests a requirement for adaptiveintelligent tutoring systems that can advise students when they learn in multi-media environments, considering their individually relevant and current cognitiveattributes.Advisement can apparently aid learner control (Shin, Schallert & Savenye,

1994). According to Large (1996, pp. 102±103), this guidance can assume manyforms:

. . .advice to complete practice examples in the included lesson, advice to repeata lesson if the examples are not correctly done, advice to ®nish the current topicrather than breaking o� to choose a new topic, advice on the sequence in whichto follow lessons, or advice on the navigational path to follow. With suchadvice, the learner still remains in control of the instructional sequencing but isgiven guidance on how to make the necessary navigational decisions. . . [A]critical issue for the design of e�ective instructional programs is not necessarilywhether to choose one or the other, but how to establish a balance betweenprogram and learner control.

Hypermedia permit students themselves to determine which, and in what order,subject-matter content will be engaged, thus controlling the process of learning.Consequently, on-line learners can dynamically adapt the educational experience totheir own momentary needs, by directly interacting with hypermedia (Barab et al.,1997). This implies that the on-line learning environment was planned and imple-mented to produce `playfulness' or `¯ow' in student interactions with hypermedia-based subject matter.Csikszentmihalyi (1990, p. 3) maintained that ¯ow experiences are enjoyable states

in which we feel `ìn control of our actions, masters of our own fate. . . we feel a senseof exhilaration, a deep sense of enjoyment.'' When experiencing ¯ow, individuals areabsorbed in their endeavors, focused in their attention, and riveted controlling theirenvironments. According to Webster, Trevino and Ryan (1993, p. 413), within acomputer-based setting, ¯ow consists of four aspects and incorporates the degree towhich ``(a) the user perceives a sense of control over the computer interaction, (b)the user perceives that his or her attention is focused on the interaction, (c) the user's


curiosity is aroused during the interaction, and (d) the user ®nds the interactionintrinsically interesting'', i.e. these researchers proposed that ¯ow is a multi-dimensional construct described by four dimensions: control, focus, curiosity, andinterest.In order to enhance individuals' cognitive engagement with hypermedia consistent

with this perspective, these four facets should be considered and incorporated intothe design and development of network-based educational environments (Federico,1998). ``[S]ystems that are designed to provide more user control, focus the user'sattention, and incite their cognitive enjoyment may result in more positive attitudes,more system use, and more positive outcomes. . .'' (Webster et al., 1993, p. 420). Theimplication here is that more e�ective student learning will likely occur in on-linesettings where creating and maintaining ¯ow is paramount. Feeding back individ-uals' navigational paths through course content should focus attention on theiruncovered learning styles and consequences. Using this recorded information toguide students' engagement of the subject matter should support their control of theacquisition process and accompanying expected enjoyment.

[T]he structure of hypertext environments parallels and can facilitate the waysin which we often learn: nonsequentially, dynamically, and interactively,through associations and by exploration. Hypertext can allow the user thefreedom to navigate courses through the material [control ] a manner deter-mined by her own interest, curiosity, and experience, or by the nature of thetask at hand, rather than following a course predetermined by the author. . .(Burbules & Callister, 1996, p. 31).

This suggests that hypermedia learning environments intrinsically facilitate ¯ow andenable `seductive details'. These details are:

. . .highly intriguing but of little importance to mastering the domain of interest(e.g., movie clips. . .). Designers should capitalize on the saliency of these typesof details, using them as ``hooks'' to facilitate more active involvement, notdetails that seduce the user away from learning base content. These hooks. . .`èngaging details,'' can help learners appreciate why the content isimportant. . .'' (Barab et al., 1997, p. 38).

Learner control of instruction is intuitively appealing, since it is assumed thatindividuals will be more highly motivated if allowed to control their ownlearning. Unfortunately, research ®ndings regarding the e�ects of learner con-trol have been inconclusive, and, what is more, they have been more frequentlynegative than positive. . . [M]uch of the actual research in the ®eld contradictsthe theory of unrestricted learner control. These negative ®ndings may occurbecause many students, especially low-achievers, lack the knowledge and moti-vation to make appropriate decisions regarding such conditions as pacing,sequencing of content, use of learning aids, and amount of practice. . . (Chung& Reigeluth, 1992, p. 14).


Chung and Reigeluth (1992) provided prescriptions for learner control, concern-ing content, sequence, pace, display, and internal processing, and advisor strategies,as well as learner control in hypermedia environments. In order to arrange addi-tional direction or instructional objectives in these on-line settings, for low abilitylearners to acquire metacognitive skills for monitoring and controlling their perfor-mance, probabilistic retrieval models may be employed to provide a default path orguided tour through the hypermedia subject matter, when individuals are new to thisenvironment or lack con®dence in their navigational skills.Audit trails, ``histories of the nodes and links that the user has accessed in a linear

order'' (Chung & Reigeluth, 1992, p. 18), can aid learners to make better informeddecisions concerning navigation through hypermedia environments, thus improvingtheir control over the process of acquiring knowledge (Jonassen, 1989; Wilson &Jonassen, 1989). Audit trails or navigational paths taken by individuals can providesome insight into cognitive and metacognitive processes intrinsic to learner controlin hypermedia environments, therefore furnishing the bases for implementing adap-tive instructional strategies. Theoretically, if done properly, these strategies can bet-ter learner control by improving: (1) motivation, relevance, and expectancy; (2)attribution, anticipation, and success; and (3) information processing, encoding, andschemata (Milheim & Martin, 1991).Furthermore, it may be possible to extrapolate a hybrid model, combining browser-

based hypermedia and query-based free text retrieval systems, and using contextualinformation to furnish access to content (Dunlop & Rijsbergen, 1993), to provide adirect path to a speci®c topic, when particular navigational patterns are identi®edamong apparently disillusioned learners who seem disoriented in hyperspace or dis-engaged from essential subject matter. This hybrid model suggests adaptinginstruction to students in hypermedia environments, employing an intelligent tutorthat recognizes individuals' navigational paths, and prescribes, using this informa-tion, speci®c coaching to accommodate each of them.

3.2. Navigational paths

Horney (1993a) established that individuals having di�erent skills and goals willnavigate hypertext employing markedly di�erent patterns, and these routes bear lit-tle resemblance to structures intrinsic to the subject matter. Trail records indicatedthat a few people interacted with the content in a linear pattern, even within largeconnected networks, and other users engaging the subject matter nonlinearly inmeager documents. Metrics employed to compute linearity functions for individualswere based upon ``. . .the number of visits to each node from each of its parents. Theratio of parent±child traversals to the total number of node visits thus gave an esti-mate of the probability of a parent±child traversal. These ratios were then used tocalculate path probabilities and then to generate linearity functions. . .'' (Horney,1993a, p. 75).Even though linearity functions are coarse estimates, Horney asserted that these

indices demonstrated indubitably nonlinearity is more appropriately an attribute ofindividuals using hypertext, not the documents themselves. Users can either exploit


or ignore the richness of the hypertext structures. Consequently, hypertext itselfshould not be de®ned as ``. . .nonlinear or sequential text. Instead hypertext shouldbe seen as a characteristic of how that text is used. . .'' (Horney, 1993a, p. 82). These®ndings suggest that nonlinearity of hypertext can be utilized as an individual dif-ference indicator, and that students' navigational patterns can be employed foradapting instruction to these attributes.According to Lucarella and Zanzi (1993, p. 301),

Hypertext is. . .an extremely ¯exible knowledge representation tool analogous inmany ways to semantic networks. Like semantic nets, hypertext also consists ofnodes and links. Di�erent types of knowledge formalism can be implemented inhypertext by structuring and de®ning the basic types of nodes and links in dif-ferent ways. The e�ect is that the highly connected structure of hypertext can beexploited as a knowledge base and can be used to build intelligent retrievalsystems. . .

If hypertext can be considered a knowledge base, then students' navigational pathsthrough this semantic network-like structure can indicate what nodes they linked,thereby uncovering what concepts they associated. This, in turn, can reveal semanticstructures constructed by individuals, as they attempt to learn the entire knowledgebase, or acquire the course contents. Students' navigational paths can provideunobtrusive and unobstructed views into the processing of learning in hypertextsettings. This diagnostic information can reveal lack of critical associations amongnodes, which can be used by an adaptive intelligent tutor to guide students to makethe salient links or connections among important concepts in order to complete theirsemantic networks or acquisition of the knowledge base.Because a basic assumption of hypermedia educational environments is that indi-

viduals are in control of their learning, students are free to engage subject matter insequences they perceive to be suitable to their present states of knowledge. However,a number of students do not have su�cient metacognitive experience in monitoringand controlling their learning in these network-based instructional settings. There-fore, these individuals have limited situation-assessment skills and decision-makingschemata that allow them to exercise e�ciently and e�ectively learner control inthese hypermedia settings. Consequently, what is needed is a practical means oftracking students' navigation paths through this content. Then, based upon thispattern, information and their learning performance feeding back to them while on-line suggested routes for engaging the subject matter to aid them in acquiring thenecessary learner control and knowledge. A network-based system planned, pro-duced, and implemented to accomplish these objectives would likely inculcateimproved metacognitive and cognitive skills.Horney (1993b) observed ®ve navigational patterns among users of hypertext: (1)

linear traversal, moving from node to node in physical order or linear sequence;(2) side trip, moving from linear traversal to visit other nodes not on the main path;(3) star pattern, moving from a central node to another then returning to the centralnode; (4) extended star pattern, moving beyond ®rst nodes visited in a star pattern to


others then returning to the central or intermediary nodes; and (5) chaotic, movingseemingly randomly from node to node apparently a mixture of other patterns.Also, Horney (1993b, p. 268) mentioned a study by Canter, Rivers and Stores

(1985), who researched navigational paths taken by individuals through hypertextand devised a series of selected track indicators: ``. . .pathiness, ringiness, loopiness,spikiness, number of nodes traversed and the ratio of the number of di�erent nodesvisited to the number of visits to nodes.'' Employing these derived indices, they dis-cerned ®ve search strategies:

[1] Scanning: A mixture of deep spikes and short loops as users seek to cover alarge area but without great depth. [2] Browsing: Many long loops and a fewlarge rings, where users are happy to go wherever the data takes them until theirinterest is caught. [3] Searching: Ever-increasing spikes with a few loops forusers motivated to ®nd a particular target. [4] Exploring: Many di�erent paths,suggesting users who are seeking the extent and nature of the ®eld. [5] Wan-dering: Many medium-sized rings as the user ambles along and inevitably revi-sits nodes in an unstructured journey. . . (Horney, 1993b, p. 268).

Barab et al. (1997) discovered an interaction between di�erent individual naviga-tional pro®les and type of information-retrieval task, and advocated ``. . .the use oflog ®les as a window into the process of hypermedia navigation. . .'' (p. 23), whichcontributed to the identi®cation of four hypermedia-users' pro®les: model users,disenchanted volunteers, feature explorers, and cyber cartographers.These three distinct sets of patterns, identi®ed by Horney (1993b), Canter et al.

(1985), and Barab et al. (1997), underscore that di�erent people navigate di�erentlythrough a hypertext environment. Consequently, navigational patterns can be con-sidered as measures or indices of individual di�erences in information processing,including learning strategies re¯ecting particular orders students use to engagehypertext content.

The advent of the hypermedia application as a major means of deliveringinstruction in both educational and training environments has providedinstructional designers with the ability to produce instructional materials thatcan be accessed in many di�erent ways. This implies learners with the samelearning objectives may navigate through the same hypermedia application inmany di�erent manners. This is due to the fact learners typically bring to agiven learning session di�erent levels of interest in the topic, di�erent worldexperiences, di�erent informational needs, di�erent levels of motivation, di�er-ent problem-solving strategies. . . thus in¯uencing how they navigate through ahypermedia environment. . . (Beasley & Waugh, 1997, p. 156).

In a highly learner controlled hypermedia environment, learners navigatethrough the information creating a personal interpretative representation ofthat information. Each individual can take a di�erent path, encountering dif-ferent amounts of and types of information in di�erent sequences. With this


system the learner must use an exploration strategy to extract the implicitstructure of the content that, in most cases, is not as explicit as in traditionalprint materials. . . (Schroeder & Grabowski, 1995, p. 313).

Beasley and Waugh (1997) analyzed prevailing navigational patterns that learnersin a hypermedia environment employed while studying a lesson structured as acompletely constrained hierarchy. These researchers established individuals used``. . .a systematic, top-down, left-to-right (depth-®rst) navigation strategy to ensurefull coverage of the lesson material initially and then covered the material in a muchmore spotty and less systematic manner during review. . .'' (Beasley & Waugh, 1997,p. 155). In addition to learners' attributes, these results suggested structure of thesubject matter and individuals' goals interacted to a�ect acquisition strategiesadopted by participants in the hypermedia setting.Insight into how individuals interact with hypermedia-based subject matter during

acquisition can be provided by ``. . .automated data collection scripts running in thebackground. . . [to] capture and store a learner's navigational choices accuratelyand unobtrusively. . .'' (Beasley & Waugh, 1997, p. 156). According to Beasley andWaugh, their data collection scripts recorded in a ®le: ``. . .the code of the nodemoved to. . ., its title. . ., and the amount of time spent on the node. . .'' (p. 161). Also,these routines make possible some understanding, concerning: (1) how individualsacquire knowledge in certain domains; (2) how to improve planning and producinginstruction; and (3) why learners take speci®c navigational paths and not others.This information can be used in the design and development of adaptive instruc-tional systems for hypermedia environments.According to Winne, Gupta and Nesbit, (1994, p. 192), ``[A]nalyzing ®ne-grained

traces of cognition. . . sharpen pictures of learners express intra- and interindividualdi�erences, and provide more penetrating grounds for examining how individ-ual di�erences a�ect the course of learning and its ultimate achievements.'' Theseresearchers asked a still salient question: ``How can trace data be analyzed to revealcognitive acts and patterns that constitute individual di�erences?'' (p. 192). Theyasserted that graph theoretic statistics can be used to analyze individual di�erencesin studying strategies in order to describe elements of mental processing and pat-terns of cognitive engagement, as well as to assess the extent of similarity amongspeci®c traces of this cerebral activity.These investigators developed a methodology grounded on directed graphs for

inspecting log ®le data. In their recommended approach, ``. . .a sequence of studyactions is reduced to a set of nodes representing action types and a set of linksrepresenting a temporal relation. . .'' (Winne et al., 1994, p. 177). If traces are inten-ded to mirror theoretically salient cognitive events, then these indices can char-acterize individual di�erences in schema-driven actions, or compiled productions.Winne et al. (1994, p. 192) mentioned important limitations, when employing graphsto study individual di�erences in complex cognition: `À sequence of traces re¯ectsnot only knowledge and competence a learner brings to the task, but also the inter-action between these and the information studied. . .''. Consequently, graph theo-retic structures, as individual di�erence indices of students' cognitive processing


during learning in hypermedia settings, may have some utility for planning andproducing adaptive intelligent instructional systems.Graphic browsers visually represent the structure of the knowledge base, or

subject-matter content, on a domain map, to explicitly provide individuals withnavigational orientation, in order to link chosen conceptual nodes. This depictionpermits direct access to any node, rapid retrieval of information, and personaldirection in hypermedia environments. Semantic networks themselves can be used asmaps of domain knowledge, allowing timely exploration of topics of interest(Schroeder & Grabowski, 1995).Recker (1994) utilized statistical clustering algorithms to uncover common usage

patterns, theorized to represent individuals' navigation and learning strategies amongstudents in a hypertext environment. She established: (1) the existence of a separateglobal navigation cluster, apparently re¯ecting a speci®c discernible cognitive skill,implying the importance of providing aids for orientation in hypermedia; (2) thatnovice individuals seemed to like forward serial browsing and dislike backtracking,which indicated a default navigation strategy for these users, suggesting sca�oldingprocedures to support the development of other browsing approaches; (3) that stu-dents seemed to choose screens in this on-line environment containing instructionalexamples; this appeared to be especially so with novices in the early stages of acqui-sition; and (4) that the hypertext instructional setting did not seem to possess su�-cient features for students to track accurately their navigation through the content;consequently, individuals had to note their orientation increasing cognitive loadand possible interference with learning. These results seemed to underscore therequirement for an adaptive intelligent tutoring system that can provide necessaryorientation aids, acquisition sca�olding, and instructional examples in hypermediaenvironments as a function of individual students' uncovered navigational patterns.It is di�cult to design access systems employing navigational links because of

having to: (1) choose a number of salient links from a large number of potential links;(2) represent links in the user interface when dealing with temporal aspects of mediasuch as video clips; (3) handle repetitive selections of links if this previous problem isnot addressed properly; (4) extract from a link more than what is obviously apparentregarding potential paths from one node to another; and (5) localize the users withinthe space of these connected graphs (Aigrain & Longueville, 1992).One aspect to consider in the design and development of e�cient information

retrieval systems for hypertext or hypermedia is the modi®cation of the status of cer-tain links: attaching levels of importance or weights on connections, in order to furnishdi�erential relevance for links as functions of chosen navigational paths (Agosti,1993). This could be used to suggest to a learner to follow a speci®c path, dependingupon the previously navigated route through the knowledge base, which has directimplications for implementing adaptive instructional strategies in hypermedia settings.

3.3. Log ®les

Learner control is integral to navigating hypermedia, where the individual decidesthe order to access informational nodes. Log ®les, ``. . .time-stamped records of


students' navigational choices while using a computerized environment. . .'' (Barabet al., 1997, p. 25), can be employed for capturing the learning process as it happenswithin an instructional context. Customary means of assessment, summative meas-ures taken after learning has occurred, do not capture dynamic processes intrinsic toknowledge acquisition, indicated by navigational performance in hypermedia envir-onments. Records can be created of individuals' navigational selections in theseinstructional spaces, which shed some light on their changing intentions and learn-ing strategies, when interacting with hypermedia environments. These log ®les pro-vide a picture of individuals' acquisition performance, and externalize their on-linelearning processes (Barab et al., 1997; Young & McNesse, 1995). ``[T]he use of audittrails forms an unobtrusive way of e�ectively peering over the shoulder of groups ofusers to determine how they are traversing the interactive media package. . .''(Misanchuk & Schwier, 1992, p. 362).One method of estimating e�ectiveness and e�ciency of learner control is to

monitor and document a student's movement through a multimedia instructionalsetting, or navigational path.

``By collecting information regarding what types of selections an individualmakes (i.e., text-based screens, digitized movies), the sequencing of these dif-ferent screens and the time spent processing the various components of theenvironment, researchers are a�orded a non-intrusive window into knowledgeacquisition strategy, information search and problem solving. . .'' (Lawless &Brown, 1997, p. 124).

Information concerning individuals' navigational paths can be easily collected byemploying a program transparent to students, to record order of content chosen,nature of subject matter selected, and time topics processed. This tracking informa-tion is saved in computerized data banks usually alluded to as `dribble ®les' (Lawless& Brown, 1997; Lawless & Kulikowich, 1996; Young, Kulikowich & Barab, 1997).Barab, Bowdish et al. (1996, p. 382) believed log ®les ``. . .(i.e., computerized

records of number of screens visited that are stamped with the amount of time spenton each screen) can provide a seamless, non-intrusive means for capturing processesrelated to search. . .''. They studied possible uses of log-®le data, and demonstratedbene®ts of a scoring rubric to code search patterns. Barab, Bowdish, et al. (1996)employed a scoring procedure to capture navigational paths for kiosk searches thatassessed the frequency of ®ve variables: ``. . .a) total time in the kiosk; b) number ofscreens visited; c) number of index buttons selected; d) number of directory buttonsselected; and e) level of depth when user quit searching. . .'' (p. 385). These investi-gators established ``. . .[l]og ®les contain information. . .that provides insights aboutthe intentions of the learner; that is, they provide a unique opportunity to capturenavigational choices, from which the researcher may infer intentions, withoutintruding upon the search process itself. . .'' (Barab, Bowdish et al., 1996, p. 386).Log ®les furnish unobtrusive windows for evaluating individuals' on-line learning,without encroaching upon their cognitive and metacogntive processes employed inhypermedia environments.


Barab et al. (1997, p. 38) di�erentiated four types of navigational performance.

Central to these distinctions were the issues of goals and motivation: modelusers appeared to be motivated to solve the problem (performance goals); cybercartographers appeared to be motivated to explore the space (learning goals);feature explorers appeared to be motivated to ®nd and watch movies. . .; anddisenchanted volunteers seemed to be motivated to stop using the hypermedia.

A limitation of the Barab et al. (1997) study, as indicated, is that they only ana-lyzed static frequency counts of navigational scores, not dynamic patterns of per-formance. Consequently, these investigators recommended research capitalizing onindividuals' navigational paths recorded in dribble ®les. This procedure can rep-resent ``. . .temporally unfolding cognitive engagements that constitute a learn-er's expression of knowledge, motivation, cognition, metacognition, and self-regulation. . .'' (Winne et al., 1994, p. 178). Studying sequences of learners' actions ispossible by associating choices of speci®c nodes to certain actions and activatinglinks to temporal relationships among selections of particular nodes. By employingstudents' actions patterns, represented as temporally ordered strings of symbols,while they interact with on-line instruction, their studying strategies or learningstyles can be captured and explained, as well as their cognitions and metacognitionspartially inferred and understood (Winne et al., 1994).Young et al. (1997, p. 136) stated,

. . .there is a growing opportunity to exploit computer interactions for assess-ment. Such computer-based environments can a�ord familiar text-based read-ing, multimedia reading, or goal-directed tasks such as information retrievaland active problem solving. Many such environments are inherently nonlinear,open-ended and a�ord multiple paths to solution. Understanding their use haspresented a challenge to contemporary assessments.

To grasp the use of nonlinear text, these researchers have analyzed ``. . .dribble ®les,continuous time-stamped logs of users' actions including screens visited, dwell time,typed text, calculations made, buttons pressed and other possible interactionsa�orded by the user-system interface. . .'' (Young et al., 1997, p. 137). Initially, theyqualitatively studied dribble ®les employing retrospective verbal protocols. As mostknowledgeable individuals realize, retrospective protocols may not be valid becauseof forgetting and fabricating on the part of research participants (Ericsson & Simon,1984). Then, they quantitatively analyzed transitions among screens using Path-®nder techniques (Schvaneveldt, 1990), but this procedure lacked information nor-mally contained in dribble ®les, and did not adequately indicate dynamicinteractions. Consequently, Young et al. (1997) thought it was important to analyzescreen transitions in order to understand interaction within situated contexts anddynamics, i.e. from ecological psychology perspectives.Kornbrot and Macleod (1990) described an interaction monitoring tool that can

be employed to observe and analyze individuals' use of a hypermedia system to learn


course content. Watching students' use of hypermedia yields information concerninghow they explore the on-line content, and navigate through the knowledge base. Thisprovides useful information for instructional designers and developers as well ascognitive scientists because it sheds some light on processes intrinsic to learner con-trol in hypermedia environments. These settings enable multiple navigational struc-tures to form complementary mental models. Students can explore the knowledgebase ``. . .in di�erent ways at di�erent stages of learning, to have multiple means ofrecovering from getting lost, and to have information available about unvisitednodes. . .'' (Kornbrot & Macleod, 1990, p. 401). This suggests that properly designedintelligent systems can e�ectively capitalize upon this navigational structure infor-mation to implement adaptive instruction and learner control in hypermedia settings.The monitoring tool employed by Kornbrot and Macleod (1990) was called

AutoMonitor:

. . .a software device which can capture a time-stamped record of interactionbetween a user and HyperCard stack. It is unobtrusive, both in its physicalpresence, and its e�ects upon interaction,. . .[and] records interaction at the levelof user actions upon discrete interface objects, such as buttons, ®elds and menuitems. This avoids the common problem of being overwhelmed by informationat the lower pixel and mouse co-ordinate level. . . (p. 403).

AutoMonitor allows classi®cation of learners' interactions for assessing hypermedia-based instructional systems into navigating, browsing, selecting, and displayingsubject-matter content.The monitoring system measured students' navigational activities in terms of,

. . .[1] overall activity rate, user actions per minute, [2] median time betweenmouse-up actions [in] seconds, [3] [count of] mouse-up user actions, [4] [countof] command. . .actions, [5] [total count] of cards visited, [6] [count of] numberof visits per card, and [7] mean of card visit time [in] seconds. . .'' (Kornbrot &Macleod, 1990, p. 404).

These researchers established that in this instructional environment students weredi�erentiated according to: (1) relatively more or less interactions performed perminute; (2) their employment of navigational structures; and (3) the subject-mattercontent they selected.Gay and Mazur (1993) examined the evolution of tracking systems, and described

®ve types of these tools that have utility for user-centered design (Norman, 1987)and development, as well as formative and summative evaluation of human±computer interaction in hypermedia-based instructional settings. According to theseresearchers, ``[t]racking systems record meticulously the number of keystrokes,content items seen by the user, navigation strategies, and paths constructed throughthe program. Tracking tools, along with the powerful records keeping capabilityof the computer, can provide valuable information for designers and users. . .'' (Gay& Mazur, 1993, p. 45).


They maintained hypermedia educational environments can be problematic fordesigners, developers, and evaluators, because: (1) di�erent students can traverse thesubject matter di�erently; (2) multiple navigational paths are feasible for every on-line learner; and (3) varied tracks through the content are conceivable for the sameindividual at distinct instructional sessions. This is due to the nonsequential struc-ture for presenting subject matter, which is inherent to hypermedia. From the per-spective of user-centered system design, a salient issue is how to e�ectively ande�ciently explore learners' interactions with, and very individualistic uses of,hypermedia environments. From the perspective of adaptive instructional systemdesign, this information is important; without this capability, it would be practicallyimpossible to analyze individuals' navigational paths for planning and producingintelligent systems for supporting adaptive instruction in hypermedia-based settings.According to Gay and Mazur (1993, p. 46),

. . .[c]omputer tracking tools and recordkeeping capabilities of the computer canprovide valuable information for designers and users: (1) the user can view hisor her previous steps in the program, plan future moves in the program, andre¯ect upon work in progress if tracking data are made visible, as in someinteractive systems; and (2) designers and evaluators can use the data to helpdetermine the e�ectiveness, usability, and comprehensibility of a multimediaprogram. The raw data can be transformed and interpreted to address a varietyof research and design questions.

These researchers stated that computer tracking tools have progressed from essen-tially text-based lists to graphical and video representations of users' interactions inon-line settings. Also, these tracking systems are currently and readily accessible toindividuals, permitting improved learner control and displaying re®ned feedback tostudents, which conceivably enhances their on-line performance and knowledgeacquisition.Gay and Mazur (1993, p. 47) described ®ve types of tracking tools: [1] customized

tracking tools:

. . .chronological printouts of options used and/or chronological lists of eventsviewed/used, [producing:] text lists of program options selected, chronologicalrecord of time-stamped use of options, [value:] codeable patterns of data,inductive analysis of patterns of use, system responsiveness, usability ofoptions; [2] records of written products: word processed texts from programwriting spaces, [producing:] written notes or documents, [value:] discourseanalysis, learning patterns, narrative reconstructions of student use, [3] interac-tion histories: interaction history as diagram or list, [producing:] diagrams orlists of user moves within program, graphic representations of nodes in websof information, [value:] e�ectiveness of tool use, examine navigational strat-egies, evidence of knowledge construction and relational webs of information,[4] online recorder: digital sound recording, [producing:] digital voice or soundrecording, [value:] evidence of user attitudes, [5] player piano: realtime


playback, screen actions taken by users, [producing:] realtime records of useraction in program, available for user playback, voice annotation of screenrecorder tape, [value:] shows stages of problem solving, version control/feed-back on usability of program options, shows individual use pattern where manyare possible in hypermedia, prompt for dialogue between user and researcher,prompt for think-ahead reporting.

Schroeder and Grabowski (1995) investigated navigational study strategies usingtwo distinct graphic browsers: ``. . .links, graphic browser with links visible butnot described, and detailed links: graphic browser with relationships between linksdescribed, and with no explicit graphic browser, hotwords: highlighted terms linkedto a related screen. . .'' (p. 316). ``The computer recorded each screen selected, theorder in which the screens were selected, and length of time spent on each screen. . .''(p. 318). These raw audit trail data were converted into analyzable navigational pathdata by categorizing and coding to ascertain:

A. Number of screens viewed and for how long:1. how many. . .screens were viewed,2. how many screens were seen more than once,3. how many screens were viewed in total,4. how long was spent on the treatment,5. how many of the practice screens were viewed,6. how long the user spent on the practice,7. how many screens a user moved to before returning to the objectives screen,

and8. how many screens. . .they linked together;

B. Movement onward or returning to the previous screen;

C. Hierarchical vs. heterarchical vs. process movement, i.e., if the user went1. up the hierarchy of concepts,2. down the hierarchy of concepts,3. to a concept across the hierarchy at the same level in the same section,4. to a related concept in another section,5. to a concept before or after the current one. . .,6. back to the objectives screen, and7. in what order to the new sections chosen, and how often;

D. What position in the map was clicked or, where the clicked hotword waslocated on the screen. . . (Schroeder & Grabowski, 1995, p. 319).

These researchers used the above indices to investigate: (1) the time spent on dis-tinct segments of the subject matter; (2) the nature of individuals' navigational pathsthrough the on-line content; and (3) the relationship of chosen tracks to study topicsand screen positions. They considered study strategies as techniques for selecting


screens when students were learning in hypermedia settings. Schroeder andGrabowski (1995) grouped path data into active and passive exploration patterns,conceptual navigation through the knowledge domain, forward or backward move-ment, and progress by clicking locations on displays. These investigators established:(1) that students learning hypermedia content relied primarily on a passive approachto exploring the knowledge base; (2) that individuals demonstrated little con-ceptually linked progression through the subject matter despite the presence of gra-phic browsers; (3) that learners evidently began at the top and moved linearlythrough the on-line content; and (4) that novices manifested only slight under-standing regarding proper use of hypermedia for achieving instructional objectives.Schroeder and Grabowski (1995) suggested that students, with little knowledge or

experience using hypermedia for learning, likely need assistance when initiallyinteracting with these instructional systems. They thought presenting a ``guidedtour'', or overview of salient concepts to be acquired on-line would be more bene-®cial for novices than experts. This insinuated providing di�erential degrees of gui-dance as a function of hypermedia experience, i.e. an adaptive instructional system.According to Barab, Fajen, Kulikowish and Young (1996), present procedures

for quantitatively analyzing log ®les are chie¯y based on static frequency countsof accessing certain hypermedia or time percentages viewing speci®c pages, notdynamic transitions between them. Because these techniques fall short of captur-ing dynamic processes, they cannot be used to distinguish individuals' navigationalpaths through hypermedia environments. Also, customary measurement methods,obtained at the completion of acquisition and based upon static models of compre-hension, are limited in their assessments on account of not accurately representinginteractive and dynamic mechanisms inherent to learning.However, Rowe, Cooke, Hall and Halgren (1996) demonstrated that log ®les can

potentially capture dynamic cognitive operations, and classify action patterns with-out intruding upon learning processes. These investigators established: (1) that dis-tinct aspects of dynamic performance can be assessed on-line using an intelligenttutoring system; (2) that Path®nder procedures (Schvaneveldt, 1990) for explainingcomplicated sequences and representing knowledge structures can be used to iden-tify and interpret di�erent action patterns among individuals; and (3) that Path-®nder networks can be employed to discriminate between high and low performersfor prescribing certain intervention strategies. These ®ndings seem to suggest on-lineintelligent tutoring, together with Path®nder techniques, may be used for planning,producing, and implementing adaptive instructional systems for hypermedia envir-onments.Barab, Fajen et al. (1996, p. 185) ``. . .evaluate[d] the validity of Path®nder for

representing and comparing individuals' navigation through a computer-basedhypermedia environment. . . [and] for generating an empirically-derived path thatrepresents a set of navigational paths. . .''. These researchers established that Path-®nder was able to distinguish groups expected to di�er, and produce an empirically-derived network accurately re¯ecting individuals' paths. Nevertheless, they main-tained Path®nder procedures have several serious limitations: (1) salient transitionsmay be excluded during the production of a person's network; (2) identical passages


happening at di�erent occasions will likely be scored the same; (3) students' dwelltimes for speci®c pages may not be included in the derived network; and (4) empiri-cally produced paths may not precisely represent individually generated paths. Thesestipulations appear to restrict the utility of Path®nder procedures for designing anddeveloping adaptive intelligent tutoring systems for hypermedia settings.Lawless and Kulikowich (1996) demonstrated that appropriate domain knowl-

edge seemed to distinguish among three groups of participants whose navigationalpro®les through hypertext were established by cluster analysis: knowledge seekers,feature explorers, and apathetic users. These researchers used a scoring rubric foranalyzing students' dribble ®le information grounded upon the following naviga-tional variables: ``. . .1) total time in hypertext, 2) number of base cards. . . 3) numberof deviations from the base cards, 4) number of resources, 5) number of specialfeatures visited, and 6) number of people-related cards visited. . .'' (Lawless &Kulikowich, 1996, p. 380). They computed proportions or frequency counts forthese navigational variables based upon the number of cards accessible per category,and then subjected these dribble ®le data to a clustering algorithm. Lawless andKulikowich (1996) stated that their investigation emphasized the utility of naviga-tional paths for studying students' cognitive processing associated with hypertext,and cluster analysis for examining individual di�erences in dribble ®le information.Their ®nding regarding the importance of requisite domain knowledge for per-forming well in a hypertext setting is consistent with the results obtained by Shyuand Brown (1992, 1995) for a multimedia environment.Individuals' navigational choices have been analyzed (Barab et al., 1997, p. 32)

according to a scoring rubric, consisting of 10 scores designed to assess path vari-ables:

1. Choices. . .the number of di�erent navigational choices students made whileexploring. . . 2. Deviations. . .the total number of point-to-point navigationaldeviations participants made. . .while solving the task. . . 3. Directories. . .thenumber of screen displays selected that contained directory information. . . 4.Help. . .the number of help buttons selected. . . 5. Index. . .the number of indexscreens selected. . . 6. Level of Choice. . .(level of depth). . .where participantsreported that they found. . .the best solution to the task. . . 7. Level of Depth. . .the deepest informational level participants accessed as they made navigationalchoices. . . 8. Movies. . .the number of movies watched. . . 9. Shifts. . .the numberof times participants shifted their search patterns. . . 10. Total Time. . .the timefrom when students selected the problem to the time when. . .[the solution]button was selected. Frequency counts for each of these variables were derivedby inspecting participants' log ®les.

Also, assessments were made regarding the quality of the task solution, usinginformation retrieval scores re¯ecting the e�ciency and correctness of the resolu-tion. Lastly, a panel of experts rated participants' paths to presented problems.Indices similar to these can be used by subject-matter experts to evaluate learners'navigational paths through network-based content.


3.4. Intelligent tutors

Young et al. (1997, p. 137) proposed and emphasized process over product forsituated assessments:

As students interact with computer-based environments to solve problems, [orlearn facts, concepts, rules, or principles] their navigational decisions. . .producea rich stream of data that can provide information to be fed back to the pro-blem solver or to a more experienced partner working along with the learner(i.e., a guide, coach or instructor). In this sense situated assessments haveprocess authenticity, referring to their use to identify particular aspects of theproblem that a�ord action. . .

Within this framework, information concerning learners' navigational patternscan be fed back to an arti®cially intelligent tutor which can use this authenti-cally assessed path data to coach or guide individuals in order to implementadaptive instructional strategies for speci®c situational contexts. Also, withinhypermedia educational environments, from a constructivist learning perspective,this information can be fed back to students as a means of providing requiredelaboration during acquisition by establishing in them necessary neural orsemantic networks.Because each student's interactions with hypermedia can be saved automatically

in a dribble ®le, established patterns of engagement with on-line content can beemployed to project an individual's path through the learning space. This projectedtrack can then be compared to paths established by successful students, theoreticalmodels, or subject-matter experts (Young et al., 1997). The di�erence betweennovice and expert paths can be used by: (1) intelligent tutors to guide adaptivelyindividuals' instruction in hypermedia settings; and (2) students as situated assess-ment directly fed back to them during acquisition for monitoring and controlling theprocess of learning.

Hypertexts that allow a degree of unstructured and idiosyncratic explorationcan be an indispensable support to. . .learning; and hypertexts with a degree ofstructure built in, but also the options of customized design, may serve ase�ective bridges or sca�olds to bring. . .[students] to the point where they cancreate more personal and distinctive organizations of the. . .material available. . .(Burbules & Callister, 1996, p. 42).

The suggested degree of structure can be provided by an intelligent tutoringsystem that: (1) monitors learners' routes through the hypertext content; (2)feeds back to individuals their navigational paths and consequences for acquisi-tion; and (3) guides students' interactions with the subject matter taking intoaccount attended to trails. Such an adaptive instructional system should incul-cate learner control, build bridges and sca�olds, and induce cognitive andmetacognitive skills.


Hypertext researchers. . .have emphasized ¯exible organizations of multimedia`nodes' through connections made with user-speci®ed links, and interfaces thatfacilitate browsing in this network of links. . .Network structures are, however,not new to IR [Information Retrieval], and retrieval models have been pro-posed that use automatically and manually generated links between documentsand between the concepts or terms used to represent their content. . . (Croft &Turtle, 1993, p. 313).

Several probabilistic retrieval models for hypertext have been advanced, where linksindicate signi®cant dependencies among nodes, having meanings determined bytheir contents as well as the topics of nodes joined to them. The goal of these modelsis to enhance the e�ectiveness of retrieval and suggest better beginning points forbrowsing (Croft & Turtle, 1993). It may be possible to employ probabilistic retrievalmodels, or extensions of them, to guide students' interactions with network-basedcontent. In order to improve their learning e�ectiveness, automatically link to rele-vant nodes, or have them manually link to speci®ed subject matter, based uponuncovered navigational paths. Adapt instruction to individual interactive sequenceswith the content by coaching students to connect to certain nodes, possiblyemploying probabilistic retrieval models thereby maintaining some semblance oflearner control.El-Tigi and Branch (1997) presented a Web-based instructional design model,

using frames (dividing displays into two or more partitions), image maps (presentingvisual cues allowing individuals to keep track of their positions in hyperspace), andtables (®nding information via two dimensional matrices) that can be employed forplanning and implementing learner control, student interaction, and feedbackfor hypermedia environments. This pedagogical paradigm may suggest schemes forimproving network-based instruction, so that it better adapts to uncovered indivi-duals' navigational paths or learning styles. Ebersole (1997) contemplated design ofhypermedia settings from users' cognitive processing perspectives of browsabledatabases: overload, orientation, consistency, symbol systems, and response time.These mental aspects should be considered when attempting to improve Web-basedarchitecture for instructional delivery, especially capitalizing upon exposed naviga-tional patterns as unobtrusive windows into individuals' learning processes. Also,Poncelet and Proctor (1993) presented guidelines derived from cognitive and con-structivist contexts as well as instructional design frameworks for e�ectively fabri-cating hypermedia pedagogical environments that may be extrapolated to planningand implementing improved adaptive network-based instructional settings. Fur-thermore, Starr (1997) presented an overview of basic design principles for Web-based instruction that may be useful for constructing adaptive learning environ-ments for these on-line contexts.Feeding back to individual students their navigational paths through previously

encountered Web-based educational content, discerned by an on-line intelligentmonitor, can help anchor or conditionalize (Bransford, Sherwood, Hasselbring,Kinzer & Williams, 1990) their future interactions with hypermedia environments.These recognizable patterns can provide individuals with their generic strategies or


interactive schemata used in similar instructional situations. Feedback interactionpatterns, together with their instructional consequences, can be used by students toadapt and improve their control of current and future on-line learning.Cockburn and Jones (1996, p. 120) summarized usability problems associated with

Web-based browsers: ``(1) Failure of the users' mental models of the navigationsupport provided by the browsers. (2) Lack of context. There is a paucity of supportfor the users' awareness of where they are within a WWW [World Wide Web] sub-space. (3) Memory overload problems.'' Implications of their analysis of browsernavigation for Web-based educational environments are that interface designersneed to: (1) understand entirely navigational features of the hypermedia context inorder to better support student on-line learning; (2) consider the mapping betweentheir model of this instructional setting and the learners' image of the network-basedsystem; and (3) realize navigational problems can be ameliorated through graphicalbrowsers that dynamically adapt to learners' actions. Being able to detect andrecognize individuals' navigational trails or patterns through hypermedia, will allowinterface and instructional designers to better understand students': (1) pathsthrough the knowledge base; (2) models of the instructional setting; and (3) inter-actions with the on-line content. These anticipated consequences will likely result inimproved adaptive instruction in network-based pedagogical environments.``[M]any potential users of hypermedia systems lack the cognitive skills, the moti-

vation or the attitude toward learning required to take full advantage of thesecomplex systems. . .'' (Trumbull & Gay, 1992, p. 315). These researchers recom-mended, to overcome this problem, the creation of interfaces that adapt to users'information-processing styles, which allow highly intuitive and interactive searchstrategies. Notable features of navigational tools that Trumbull and Gay (1992)studied were: (1) a tracking system that recorded users' paths through the hyper-media environment; (2) a guide that suggested other nodes also in the category inwhich users demonstrated the most interest; and (3) a pattern recognition systemthat the guide used to advise individuals on other topics for investigation. Their on-line student tracking produced a permanent record of system use, including the totaltime interacted, the search modes used, the nodes accessed, the sequence followed,and the individuals' activity at each site visited.Trumbull and Gay (1992) found that the amount of user control over interaction

with the hypermedia system varied, with some individuals' navigational paths orsearch modes lacking complete e�ectiveness. Because di�erent users employed dif-ferent ways of searching hypermedia, they suggested developing di�erent interfacesfor di�erent information-seeking strategies. It must be emphasized that the key todesigning these adaptive interfaces is the capability to recognize individuals' navi-gational patterns, interactive strategies, or search modes. This is especially crucialwhen intending to plan, produce, and implement adaptive instructional models forhypermedia environments.It may be feasible to use multidimensional scaling and Path®nder procedures

(Schvaneveldt, 1990) which have been evaluated for assessing alterations in thestructural representation of knowledge as a function of acquisition (Gonzalvo,Canas & Bajo, 1994) in conjunction with individuals' uncovered navigational


patterns in hypermedia settings, to suggest to students di�erent paths or instruc-tional strategies through on-line course content as a means of advising or adaptinglearner control over interacting with the subject matter. Diversi®ed frequency dis-tribution models of hypertext patterns, employing path length and frequency ofnode visiting (Qiu, 1994), may provide the basis for simulating information retrievalor knowledge acquisition in these environments, culminating in improved instruc-tional designs for on-line learner control.According to Rowe et al. (1996, p. 31), `Òne goal of intelligent tutoring systems is

to incorporate individualized instruction based on a detailed assessment of studentknowledge that can be used to diagnose cognitive strengths and weaknesses.Instructional intervention can then be directed at these strengths and weaknesses.''Assessment approaches for intelligent tutoring systems have attempted to comparenovice and expert models, or student and instructor conceptualizations, of aknowledge domain. These researchers mentioned that di�erent procedures havebeen employed for eliciting and examining these mental models: (1) accuracy andtime measures obtained during performance only provide an indirect look into thenature of the cognitive processing itself; (2) interviews or process traces obtainedduring or after performance producing reams of verbal data are usually di�cult tointerpret; and (3) structural analytical techniques employing proximity measures andmultivariate statistical procedures do not provide insight into heuristics or strategiesfor executing cognitive tasks.Rowe and colleagues recommended uncovering and analyzing meaningful pat-

terns of students' actions, as well as measuring and evaluating their states ofknowledge during acquisition in a reliable and valid manner. They suggested on-lineassessment schemes where students' observed action patterns are indirectly mappedto their present states of knowledge. One way of accomplishing this in a network-based instructional environment is to analyze individuals' navigational pathsthrough the multimedia subject matter and measure their mastery of speci®c contentduring the process of acquisition. Then, attempt to relate various path pro®les takenby students to their states of knowledge during the process of learning on-line. Anintelligent system designed to use this diagnostic information could consequentlyprescribe to students speci®c paths through the content as a means of implementingadaptive instructional strategies tailored to the particular needs of individuals.Recommended means of accomplishing this goal are comparing the navigationalpaths of less successful students to their more successful counterparts, or contrastingthe track pro®les of individual novices and subject-matter experts.Cognitive ¯exibility theory is a case-based conceptualization of learning advo-

cated to assist advanced individuals acquire complex knowledge (Feltovich, Spiro &Coulson, 1989; Spiro, Coulson, Feltovich & Anderson, 1988; Spiro, Vispoel,Schmitz, Samarapungavan & Boerger, 1987). A major tenet of this theory proposesthat advanced acquisition consists of producing ¯exible representations of knowl-edge to promote deep conceptual understanding and adaptation of information tonovel situations. According to Jacobson (1994), cognitive ¯exibility theory has alsobeen put forth as a conceptual context for planning and producing hypermediapedagogical environments. Jacobson (1994, p. 146) discussed seven important


elements of this theory with respect to the design and development of these on-lineinstructional settings:

. . .(a) employ rich cases and examples; (b) use multiple forms of knowledgerepresentation; (c) link abstract concepts to case examples; (d) demonstrateconceptual complexities and irregularities; (e) stress the interrelated and web-like nature of knowledge; (f) encourage knowledge assembly from di�erentconceptual and case sources; and (g) promote active learning. . .

These elements of cognitive ¯exibility theory, together with the abilities to trackstudents' navigational patterns and associated performances through hypermedia-based content, suggest adaptive instructional systems can be planned and imple-mented to `criss-cross conceptual landscapes', as a means of facilitating advancedlearning and transfer to new contexts. If students' navigational patterns are mon-itored and corresponding performances recorded, then they can be guided oradvised on-line to take particular paths through the hypermedia subject matter as afunction of previous routes and achievements, in order to implement design ele-ments discussed by Jacobson (1994) to enhance advanced learning and knowledgetransfer. Also, for hypertext learning environments, research results establishedde®nitely demonstrating crucial relationships between hypothetical and case-basedknowledge elements in multiple contexts will facilitate transfer to new situations,congruent with the concepts of cognitive ¯exibility theory (Jacobson & Spiro, 1995).Consequently, by monitoring individuals' navigational paths and correspondingperformances this information can be employed to maximize transfer by guidingstudents to, or prescribing for them, salient links between abstract and case-basedknowledge components.Tergan (1997) critically reviewed research on the consequences of multiple exter-

nal representations: many views, contexts, and symbol systems, on learning fromhypermedia. This was done to evaluate the theory that acquisition performance andknowledge transfer are enhanced when content is presented employing multipleperspectives, contexts, and codes. Tergan determined that a number of perspectivesdo not automatically improve learning performance and knowledge transfer. How-ever, multiple representations may enhance acquisition when su�cient instructionalsca�olding is supplied. This is especially critical for novice individuals because of theanticipated increase in cognitive load, demanded for interpreting several repre-sentations and their interrelationships. An intelligent tutoring system, enablingadaptive instruction in hypermedia environments by employing learners' naviga-tional paths to guide their engagement with the subject matter, can provide thenecessary sca�olding that may be su�cient for multiple representations to improveacquisition performance and knowledge transfer.It may be possible to construct an adaptive intelligent tutor that monitors and

guides students' navigational paths through hypermedia-based subject matter byusing hybrid cognitive modeling: ®nite state automata (FSA) together with aknowledge-based system to evaluate state transition rules. These are speci®ed as a setof productions, conditional statements having the form: if in state i, then transition


to state j. An FSA consists of a number of cognitive or action states connected orgoverned by state transition rules. A student can be in any cognitive state corre-sponding to the FSA for a particular pedagogical goal. The student's present statea�ects an individual's learner control: what navigational decisions will be made toaccess certain instructional nodes, or hypermedia subject matter.When students are interacting with speci®c instructional nodes they have asso-

ciated cognitive states that strongly in¯uence their subsequent navigational decisionsand learning behavior. Within an FSA framework, students' acquisition behavior ata hypermedia node is de®ned by the current state, and their navigation betweennodes is instigated by transitions between cognitive states. The representation ofthese state transitions necessitates the construction of a production-rule sub-structure, suggesting hybrid cognitive modeling combining a knowledge-based sys-tem and FSAs (Raeth, 1990).This conceivable hybrid cognitive modeling approach to an adaptive intelligent

tutor for hypermedia environments would require a knowledge database specifyingstudents' FSAs, which include their cognitive states and corresponding productionrules which execute state transitions. Students' cognitions or actions associated witha certain instructional goal can be simulated by its FSA, the transition rules used bythe FSA, and achieved learning outcomes for a speci®c hypermedia node.Assuming a mastery learning paradigm implementing criterion-referenced assess-

ment, a student's current cognitive state is determined by previous behavioral states.These are de®ned by successfully completed learning objectives presented at speci®cinstructional nodes, or navigational paths taken through hypermedia subject matterduring the process of acquisition. Recognizing a student is currently interacting withan instructional node, and has mastered other hypermedia content nodes in a spe-ci®c sequence or navigational path, an individual's present cognitive or knowledgestate can be easily estimated, employing objectives-referenced assessment. This mustbe tied to mastering distinct subject matter, or completing certain instructionalobjectives contained within hypermedia educational nodes.Consequently, a student's current cognitive state re¯ects mastery of particular

learning objectives, i.e. present knowledge state. Then, this information can be usedby the hybrid cognitive model to compute the next most appropriate cognitive statefor a student based upon recognition of an individual's current knowledge state. Theprescribed state, tailored or adapted to a speci®c student at a certain time in theacquisition process, can be fedback to an individual, in order to guide or advise astudent concerning learner control in the hypermedia environment. This is one pos-sible approach to developing an adaptive intelligent tutor, to monitor and guidestudents' navigational paths through hypermedia-based content using hybrid cog-nitive modeling which implements FSAs and state transition rules.Arti®cial neural systems (ANSs) are mathematical models of conjectured cognitive

and cerebral activity. ANSs are also labeled: `neural networks, connectionism,adaptive systems, adaptive networks, arti®cial neural networks, neurocomputers,and parallel distributed processors'. ANSs capitalize on largely parallel local pro-cessing and widely distributed cerebral representation believed to exist in the humanbrain. The primary purpose of ANSs is to explore and reproduce information-


processing tasks, or cognitive functions. ANS theory is derived from a number ofdistinct academic disciplines: neuroscience, cognitive science, computer science,psychology, biology, mathematics, physics, engineering, philosophy, and linguistics.However, a universal goal of these diverse knowledge domains is the construction ofarti®cially intelligent systems (Simpson, 1990).

[A]n ANS is a nonlinear directed graph with weighted edges that is able to storepatterns by changing the edge weights and is able to recall patterns fromincomplete and unknown inputs. The key elements of most ANS descriptionsare the distributed representation, the local operations, and nonlinear proces-sing. These attributes emphasize two of the primary applications of ANSsÐsituations where only a few decisions are required from a massive amount of dataand situations where a complex nonlinear mapping must be learned. . . (Simpson,1990, p. 4).

The abilities of ANSs to store, recognize, classify, and match spatial and spatio-temporal patterns suggest that they may be useful for designing, developing, andimplementing arti®cially intelligent systems for adapting instruction in hypermediaenvironments. Theoretically, an ANS-based intelligent tutor may be able to recog-nize a student's navigational pattern through a network of multimedia nodes theindividual connected by links. This information may be used by the smart system toprescribe learning strategies, or suggest navigational paths through hypermediasubject matter in order to adapt instruction to the individual, or enhance a stu-dent's control of the knowledge acquisition process. An adaptive intelligent tutorutilizing ANSs may be able to advise students when they engage network-basedcontent to accommodate coaching speci®c to each of them, considering theirestablished tracks through a hypermedia environment. In addition to guiding astudent to take a particular learning path, an ANS-based intelligent tutor may becapable of providing necessary orientation aids and acquisition sca�olding, as afunction of an individual's recognized navigational pattern through the multimediasubject matter. Also, a smart system using ANSs may be able to compare naviga-tional patterns of more and less successful students, or contrast trail pro®les ofnovices and subject-matter experts, and use this diagnostic data to prescribe speci®cpaths through the content, to implement adaptive instructional strategies tailored toindividual needs.

4. Recommendations

Assuming: (1) most schools, colleges, universities, and corporations will eventuallyo�er distributed students network-based instruction for particular refresher pre-paration and certain core courses; and (2) adaptive intelligent tutors are importantcomponents of course management systems, recommendations for research, devel-opment, and evaluation are made, extracting from the preceding topical discussionto determine empirically the feasibilities of the following:


1. utilizing graph theoretic structures, as individual di�erence indices of students'cognitive processing during learning in hypermedia settings, for planning andproducing adaptive intelligent instructional systems;

2. employing probabilistic retrieval models, or extensions of them, to guide indi-viduals' interactions with network-based subject matter;

3. feeding back students' navigational paths through previously encounteredsimilar hypermedia content recognized by an intelligent monitor, to provideindividuals with their generic learning strategies so they can accommodate andimprove control of current and future on-line learning;

4. designing adaptive interfaces based upon recognized students' navigationalpatterns, interactive tactics, or search modes, to implement tailored instruc-tional strategies for hypermedia environments;

5. developing adaptive instructional systems grounded on cognitive ¯exibilitytheory to present multiple perspectives to facilitate advanced learning andknowledge transfer, considering individuals' navigational paths and corre-sponding performances through hypermedia content to prescribe for studentssalient links between abstract and case-based components;

6. constructing an adaptive intelligent tutor that monitors and guides students'navigational paths through hypermedia subject matter, by using hybrid cogni-tive modeling which applies FSAs and state transition rules; and

7. using ANSs for planning, producing, and implementing arti®cially intelligentsystems for adapting instruction in hypermedia environments.

References

Agosti, M. (1993). Hypertext and information retrieval. Information Processing and Management, 29,

283±285.

Aigrain, P., & Longueville, V. (1992). Evaluation of navigational links between images. Information Pro-

cessing and Management, 28, 517±528.

Atkinson, R. (1976). Adaptive instructional systems: some attempts to optimize the learning process. In

D. Klahr, Cognition and instruction. Hillsdale, NJ: Erlbaum, (pp. 81±108).

Barab, S., Bowdish, B., & Lawless, K. (1997). Hypermedia navigation: pro®les of hypermedia users.

Educational Technology Research and Development, 45, 23±41.

Barab, S., Bowdish, B., Young, M., & Owen, S. (1996). Understanding kiosk navigation: using log ®les to

capture hypermedia searches. Instructional Science, 24, 377±395.

Barab, S., Fajen, B., Kulikowish, J., & Young, M. (1996). Assessing hypermedia navigation through

path®nder: prospects and limitations. Journal of Educational Computing Research, 15, 185±205.

Beard, M., Lorton, P., Searle, B., & Atkinson, R. (1973). Comparison of student performance and attitude

under three lesson-selection strategies in computer-assisted instruction (Technical Report No. 222).

Stanford, CA: Institute for Mathematical Studies in the Social Sciences, Stanford University.

Beasley, R., & Waugh, M. (1997). Predominant initial and review patterns of navigation in a fully con-

strained hypermedia hierarchy: an empirical study. Journal of Educational Multimedia and Hypermedia,

6, 155±172.

Berliner, D., & Cahen, L. (1973). Trait-treatment interaction and learning. In F. Kerlinger, Review of

Research in Education. Itascha, IL: Peacock, (pp.58±94).

Boutwell, R., & Barton, G. (1974). Toward an adaptive learner-controlled model of instruction: a place

for the new cognitive aptitudes. Educational Technology, 14, 13±18.


Bracht, G. (1969). The relationship of treatment tasks, personological variables, and dependent variables to

aptitude-treatment interactions. Unpublished doctoral dissertation, University of Colorado, CO.

Bracht, G. (1970). Experimental factors related to aptitude-treatment interactions. Review of Educational

Research, 40, 627±645.

Bracht, G., & Glass, G. (1968). The external validity of experiments. American Educational Research

Journal, 5, 437±474.

Bransford, J., Sherwood, R., Hasselbring, T., Kinzer, C., & Williams, S. (1990). Anchored instruction:

why we need it and how technology can help. In D. Nix & R. Spiro, Cognition, education, and multi-

media: exploring ideas in high technology (pp. 115±141). Hillsdale, NJ: Erlbaum.

Briggs, L. (1968). Learner variables and educational media. Review of Educational Research, 38, 160±

176.

Burbules, N., & Callister, T. (1996). Knowledge at the crossroads: some alternative futures of hypertext

learning environments. Educational Theory, 46, 23±50.

Canter, D., Rivers, R., & Stores, G. (1985). Characterizing user navigation through complex data struc-

tures. Behaviour and Information Technology, 4, 17±41.

Chung, J., & Reigeluth, C. (1992). Instructional prescriptions for learner control. Educational Technology,

32, 14±20.

Cockburn, A., & Jones, S. (1996). Which way now? Analyzing and easing inadequacies in WWW navi-

gation. International Journal of Human±Computer Studies, 45, 105±129.

Coop, R., & Sigel, I. (1971). Cognitive style: implications for learning and instruction. Psychology in the

Schools, 8, 152±161.

Croft, W., & Turtle, H. (1993). Retrieval strategies for hypertext. Information Processing and Manage-

ment, 29, 313±324.

Cronbach, L. (1957). The two disciplines of scienti®c psychology. The American Psychologist, 12, 671±684.

Cronbach, L. (1967). How can instruction be adapted to individual di�erences. In R. Gagne, Learning and

individual di�erences. Columbus, OH: Merrill, (pp. 23±39).

Cronbach, L., & Gleser, C. (1965). Psychological tests and personnel decisions. Urbana, IL: University of

Illinois Press.

Cronbach, L., & Snow, R. (1969). Individual di�erences in learning ability as a function of instructional

variables (Final Report). Stanford, CA: School of Education, Stanford University.

Cronbach, L., & Snow, R. (1977). Aptitudes and instructional methods: a handbook for research on inter-

actions. New York: Irvington.

Csikszentmihalyi, M. (1990). The psychology of optimal experience. New York: Harper and Row.

DiVesta, F. (1973). Theories and measures of individual di�erences in studies of trait by treatment inter-

action. Educational Psychologist, 10, 67±75.

Dunlop, M., & Rijsbergen, C. (1993). Hypermedia and free text retrieval. Information Processing and

Management, 29, 287±298.

Ebersole, S. (1997). Cognitive issues in the design and development of interactive hypermedia: implica-

tions for authoring www sites. Interpersonal Computing and Technology: An Electronic Journal for the

21st Century, 5, 19±36.

El-Tigi, M., & Branch, R. (1997). Designing for interaction, learner control, and feedback during web-

based learning. Educational Technology, May±June, 23±29.

Ericsson, K., & Simon, H. (1984). Protocol analysis: verbal reports as data. Cambridge, MA: MIT Press.

Estes, W. (Ed.) (1975±76). Handbook of learning and cognitive processes (Vols. 1, 3 and 4). Hillsdale, NJ:

Erlbaum.

Federico, P-A. (1978). Accommodating instruction to student characteristics: trends and issues (NPRDC

TR 79-1). San Diego, CA: Navy Personnel Research and Development Center.

Federico, P-A. (1980). Adaptive instruction: trends and issues. In R. Snow, P.-A. Federico & W.

Montague, Aptitude, learning, and instruction: Vol. 1, Cognitive process analyses of aptitude (pp. 1±26).

Hillsdale, NJ: Erlbaum.

Federico, P-A. (1987). Cerebral, cognitive, and conative processes. In R. Snow & M. Farr, Aptitude,

learning, and instruction: Vol. 3, Conative and a�ective process analyses (pp. 99±130). Hillsdale, NJ:

Erlbaum.


Federico, P-A. (1991). Student cognitive attributes and performance in a computer-managed instructional

setting. In R. Dillon & J. Pellegrino, Instruction: theoretical and applied perspectives (pp. 16±46). New

York: Praeger.

Federico, P-A. (1997). Internet-based instruction to supplement traditional and nontraditional education

provided by the Naval Postgraduate School: technology, advantages and disadvantages, issues, and

recommendations (IDEA TM 97-1). Monterey, CA: Institute for Defense Education and Analysis,

Naval Postgraduate School.

Federico, P-A. (1998). Student attitudes towards network-based instruction: di�erent academic departments

and dissimilar learning styles (IDEA TM 98-1). Monterey, CA: Institute for Defense Education and

Analysis, Naval Postgraduate School.

Feltovich, P., Spiro, R., & Coulson, R. (1989). The nature of conceptual understanding in biomedicine:

the deep structure of complex ideas and the development of misconceptions. In D. Evans & V. Patel,

The cognitive sciences in medicine (pp. 113±172). Cambridge, MA: MIT Press.

Fleischman, E., & Bartlett, C. (1969). Human abilities. Annual Review of Psychology, 20, 349±380.

French, J. (1965). The relationship of problem-solving styles to the factor composition of tests. Educa-

tional and Psychological Measurement, 25, 9±28.

Gage, N., & Unrah, W. (1967). Theoretical formulations for research on teaching. Review of Educational

Research, 37, 358±370.

Gagne, R. (1967). Instruction and the conditions of learning. In L. Siegel, Instruction: some contemporary

viewpoints. San Francisco, CA: Chandler, (pp. 291±313).

Gagne, R., Paradise, N. (1961). Abilities and learning sets in knowledge acquisition. Psychological

Monographs, 75 (14, Whole No. 518).

Gay, G., & Mazur, J. (1993). The utility of computer tracking tools for user-centered design. Educational

Technology, 33, 45±59.

Glaser, R. (1967). Some implications of previous work on learning and individual di�erences. In R.

Gagne, Learning and individual di�erences. Columbus, OH: Merrill, (pp. 1±18).

Glaser, R. (1972). Individuals and learning: the new aptitudes. Educational Researcher, 1, 5±13.

Glaser, R. (1976a). Cognitive psychology and instructional design. In D. Klahr, Cognition and instruction.

Hillsdale, NJ: Erlbaum, (pp. 303±315).

Glaser, R. (1976b). Components of a psychology of instruction: toward a science of design. Review of

Educational Research, 46, 1±24.

Glaser, R. (1977). Adaptive instruction: individual diversity and learning. New York: Holt, Rinehart, and

Winston.

Glaser, R., & Resnick, L. (1972). Instructional psychology. In P. Mussen & M. Rosenweig, Annual review

of psychology. Palo Alto: Annual Reviews, (pp. 207±276).

Goldberg, L. (1972). Student personality characteristics and optimal college learning conditions: an

extensive search for trait-by-treatment interaction e�ects. Instructional Science, 1, 153±210.

Gonzalvo, P., Canas, J., & Bajo, M. (1994). Structural representations in knowledge acquisition. Journal

of Educational Psychology, 86, 601±616.

Horney, M. (1993a). A measure of hypertext linearity. Journal of Educational Multimedia and Hyperme-

dia, 2, 67±82.

Horney, M. (1993b). Case studies of navigational patterns in constructive hypertext. Computers and

Education, 20, 257±270.

Hunt, E. (1976). Varieties of cognitive power. In L. Resnick, The nature of intelligence. Hillsdale, NJ:

Erlbaum, (pp. 237±259).

Hunt, E., & Lansman, M. (1975). Cognitive theory applied to individual di�erences. In W. Estes, Hand-

book of learning and cognitive processes. Vol. 1: Introduction to concepts and issues. Hillsdale, NJ:

Erlbaum, (pp. 81±110).

Jacobson, M. (1994). Issues in hypertext and hypermedia research: toward a framework for linking theory-

to-design. Journal of Educational Multimedia and Hypermedia, 3, 141±154.

Jacobson, M., & Spiro, R. (1995). Hypertext learning environments, cognitive ¯exibility, and the transfer

of complex knowledge: an empirical investigation. Journal of Educational Computing Research, 12,

301±333.


Jensen, A. (1967). Varieties of individual di�erences in learning. In R. Gagne, Learning and individual

di�erences. Columbus, OH: Merrill, (pp. 45±57).

Jensen, A. (1968). Social class, race, genetics: implications for education. American Education and

Research Journal, 5, 1±42.

Jonassen, D. (1986). Hypertext principles for text and courseware design. Educational Psychologist, 21,

269±292.

Jonassen, D. (1989). Hypertext/hypermedia. Englewood Cli�s, NJ: Educational Technology Publications.

Jonassen, D. (1993). Conceptual frontiers in hypermedia environments for learning. Journal of Educa-

tional Multimedia and Hypermedia, 2, 331±335.

Kornbrot, D., & Macleod, M. (1990). Monitoring andanalysis of hypermedia navigation. Human±

Computer Interaction-Interact, 90, 401±406.

Labouvie-Vief, G., Levin, J., & Urberg, K. (1975). The relationship between selected cognitive abilities

and learning. A second look. Journal of Educational Psychology, 67, 558±569.

Landow, G. (1992). Hypertext: the convergence of contemporary theory and technology. Baltimore: Johns

Hopkins University Press.

Large, A. (1996). Hypertext instructional programs and learner control: a research review. Education for

Information, 4, 95±106.

Lawless, K., & Brown, S. (1997). Multimedia learning environments: issues of learner control and navi-

gation. Instructional Science, 25, 117±131.

Lawless, K., & Kulikowich, J. (1996). Understanding hypertext navigation through cluster analysis.

Journal of Educational Computing Research, 14, 385±399.

Leherissey, B., O'Neil, H., Heinrich, D., & Hansen, D. (1973). E�ect of anxiety, response mode, subject

matter familiarity and program length on achievement in computer-assisted learning. Journal of Edu-

cational Psychology, 64, 310±324.

Locatis, C., Letourneau, G., & Banvard, R. (1989). Hypermedia and instruction. Educational Technology

Research and Development, 37, 65±77.

Lucarella, D., & Zanzi, A. (1993). Information retrieval from hypertext: an approach using plausible

inference. Information Processing and Management, 29, 299±312.

McKeachie, W. (1974). Instructional psychology. Annual Review of Psychology, 25, 161±193.

Melara, G. (1996). Investigating learning styles on di�erent hypertext environments: hierarchical-like and

network-like structures. Journal of Educational Computing Research, 14, 313±328.

Melton, A. (1967). Individual di�erences and theoretical process variables: general comments on the

conference. In R. Gagne, Learning and individual di�erences. Columbus, OH: Merrill, (pp. 238±252).

Melton, A., & Martin, E. (1972). Coding processes in human memory. Washington, DC: Winston.

Merrill, M. (1975). Learner control: beyond aptitude-treatment interactions. AV Communications Review,

23, 217±226.

Milheim, W., & Martin, B. (1991). Theoretical bases for the use of learner control: three di�erent per-

spectives. Journal of Computer-Based Instruction, 18, 99±105.

Misanchuk, R., & Schwier, R. (1992). Representing interactive multimedia and hypermedia audit trials.

Journal of Educational Multimedia and Hypermedia, 1, 355±372.

Norman, D. (1987). User centered design. In D. Norman, The psychology of everyday things. New York:

Basic Books, (pp. 187±217).

O'Neil, H., Spielberger, C., & Hansen, D. (1969). E�ects of state-anxiety and task di�culty on computer-

assisted instruction. Journal of Educational Psychology, 60, 343±350.

Paivio, A. (1971). Imagery and verbal processes. New York: Holt, Rinehart, and Winston.

Poncelet, G., & Proctor, L. (1993). Design and development factors in the production of hypermedia-

based courseware. Canadian Journal of Educational Communication, 22, 91±111.

Qiu, L. (1994). Frequency distributions of hypertext path patterns: a pragmatic approach. Information

Processing and Management, 30, 131±140.

Raeth, P. (1990). Process states as decision criteria. AI Expert, 5, 40±45.

Recker, M. (1994). A methodology for analyzing students' interactions within educational hypertext. In

T. Ottmann & I. Tomek, Proceedings of Educational Multimedia and Hypermedia (pp. 474±479). Asso-

ciation for the Advancement of Computing in Education, Charlottesville, VA.


Rigney, J. (1976).On cognitive strategies for facilitating acquisition, retention, and retrieval in training (Tech-

nical Report No. 78). Los Angeles, CA: Department of Psychology, University of Southern California.

Rigney, J., & Towne, D. (1970). Taskteach: a method for computer-assisted performance training. Human

Factors, 12, 285±296.

Roberts, D. (1968±69). Abilities and learning: a brief review and discussion of empirical studies. Journal of

School Psychology, 7, 12±21.

Rohwer, W. (1970a). Images and pictures in children's learning. Psychological Bulletin, 73, 393±403.

Rohwer, W. (1970b). Mental elaboration and pro®cient learning. In J. Hill, Minnesota Symposia on Child

Psychology, 4. Minneapolis: University of Minnesota Press, (pp. 220±260).

Rohwer, W. (1971). Learning, race and school success. Review of Educational Research, 41, 191±210.

Rowe, A., Cooke, N., Hall, E., & Halgren, T. (1996). Toward an on-line knowledge assessment metho-

dology: building on the relationship between knowing and doing. Journal of Experimental Psychology:

Applied, 2, 31±47.

Rumelhart, D., & Ortony, A. (1977). The representation of knowledge in memory. In R. Anderson, R.

Spiro &W. Montague, Schooling and the acquisition of knowledge. Hillsdale, NJ: Erlbaum, (pp. 99±135).

Scandura, J. (1973). Structural learning I: theory and research. London: Gordan and Breach.

Scandura, J. (1977). Structural approach to instructional problems. American Psychologist, 32, 333±353.

Schroeder, E., & Grabowski, B. (1995). Patterns of exploration and learning with hypermedia. Journal of

Educational Computing Research, 13, 313±335.

Schroder, H., Driver, M., & Streufert, S. (1967). Human information processing: individuals and groups

functioning in complex social situation. New York: Holt, Rinehart, and Winston.

Schvaneveldt, R. (1990). Path®nder associative networks. Norwood, NJ: Ablex.

Seidel, R. (1971). Theories and strategies related to measurement in individualized instruction. Educa-

tional Technology, 11, 40±46.

Shin, E., Schallert, D., & Savenye, W. (1994). E�ects of learner control, advisement, and prior knowledge

on young students' learning in a hypertext environment. Educational Technology Research and Devel-

opment, 42, 33±46.

Shyu, H., & Brown, S. (1992). Learner control versus program control in interactive videodisc instruction:

what are the e�ects of procedural learning? International Journal of Instructional Media, 19, 85±96.

Shyu, H., & Brown, S. (1995). Learner-control: the e�ects on learning a procedural task during computer-

based videodisc instruction. International Journal of Instructional Media, 22, 217±231.

Simpson, P. (1990). Arti®cial neural systems: foundations, paradigms, applications, and implementations.

New York: Pergamon.

Snow, R. (1976). Theory and method for research on aptitude processes: a prospectus (Technical Report

No. 2). Stanford, CA: School of Education, Stanford University.

Snow, R. (1980). Aptitude, learner control, and adaptive instruction. Educational Psychologist, 15, 151±

158.

Snow, R., & Solomon, G. (1968). Aptitudes and instructional media. AV Communication Review, 16, 341±

357.

Solso, R. (1973). Contemporary issues in cognitive psychology: the Loyola symposium. Washington, DC:

Winston.

Spiro, R., Coulson, R., Feltovich, P., & Anderson, D. (1988). Cognitive ¯exibility theory: advanced

knowledge acquisition in ill-structured domains. In Tenth annual conference of the Cognitive Science

Society (pp. 375±383). Hillsdale, NJ: Erlbaum.

Spiro, R., Vispoel, W., Schmitz, J., Samarapungavan, A., & Boerger, A. (1987). Knowledge acquisition

for application: cognitive ¯exibility and transfer in complex content domains. In B. Britton & S. Glynn,

Executive control processes in reading (pp. 177±199). Hillsdale, NJ: Erlbaum.

Starr, R. (1997). Delivering instruction on the world wide web: overview and basic design principles.

Educational Technology, May±June, 7-15.

Steinberg, E. (1977). Review of student control in computer-assisted instruction. Journal of Computer-

Based Instruction, 3, 84±90.

Tennyson, R. (1975). Adaptive instructional models for concept acquisition. Educational Technology, 15,

7±15.


Tennyson, R., & Boutwell, R. (1973). Pre-task versus within-task anxiety measures in predicting perfor-

mance on a concept acquisition task. Journal of Educational Psychology, 65, 88±92.

Tergan, S. (1997) Multiple views, contexts, and symbol systems in learning with hypertext/hypermedia: a

critical review of research. Educational Technology, July±August, 5±18.

Tobias, S. (1976). Achievement treatment interactions. Review of Educational Research, 46, 61±74.

Trumbull, D., & Gay, G. (1992). Students' actual and perceived use of navigational and guidance tools in

a hypermedia program. Journal of Research on Computing in Education, 24, 315±328.

Tulving, E., & Donaldson, W. (1972). Organization of memory. New York: Academic Press.

Webster, J., Trevino, L., & Ryan, L. (1993). The dimensionality and correlates of ¯ow in human±

computer interactions. Computers in Human Behavior, 9, 411±426.

Wilson, F, & Jonassen, D. (1989). Hypertext and instructional design: some preliminary guidelines. Per-

formance Improvement Quarterly, 2, 34±49.

Winne, P., Gupta, L, & Nesbit, J. (1994). Exploring individual di�erences in studying strategies using

graph theoretic statistics. The Alberta Journal of Educational Research, 40, 177±193.

Young, M., Kulikowich, J., & Barab, S. (1997). The unit of analysis for situation assessment. Instructional

Science, 25, 133±150.

Young, M., & McNesse, M. (1995). A situated cognition approach to problem solving. In P. Hancock, J.

Flach, J. Caid & K. Vicente, Local applications of the ecological approach to human±machine systems:

(Vol. 2). Hillsdale, NJ: Erlbaum, (pp. 359±391).


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