A conceptual model of personalized virtual learning environments

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izeg Wof QHongwingify leLEselingcaptf knoone of the most significant recent developments in theInformation Systems (IS) field.innovation. Those opportunities develop online learnersExpert Systems with Applicatedu.hk (H. Wang), iswmh@cityu.edu.hk (M. Wang).models, models of VLEs in general pedagogical bases, and particularly, the definition of the ontology of PVLEs on the constructivistpedagogical principle. Based on those comprehensive conceptual models, a prototyped multiagent-based PVLE has been implemented. Afield experiment was conducted to investigate the learning achievements by comparing personalized and non-personalized systems. Theresult indicates that the PVLE we developed under our comprehensive ontology successfully provides significant learning achievements.These comprehensive models also provide a solid knowledge representation framework for PVLEs development practice, guiding theanalysis, design, and development of PVLEs.q 2005 Elsevier Ltd. All rights reserved.Keywords: Personalized learning; Agent oriented modeling; Conceptual model; Virtual learning environment1. IntroductionAt the dawn of the 21st Century, the education landscapeis changing, in large part due to the Internet. Many of thetraditional institutions of higher education, universities andcolleges, are now beginning to develop and deliver Web-based courses via Virtual Learning Environments (VLEs)(McCormick, 2000). Therefore, in this technology-mediatedlearning area, the research and development of VLEs havebeen growing quickly. So-called VLEs can be defined ascomputer-based environments that are relatively opensystems, allowing interactions and encounters with otherparticipants (Wilson, 1996). A Virtual Learning Environ-ment is an environment in which students and educators canperform education-related tasks asynchronously, which isVLEs are best at achieving learning effectiveness whenthey adapt to the needs of individual learners (Park &Hannafin, 1993). VLEs should be able to identify learningneeds and customize solutions that foster successful learningand performance, with or without an instructor to supplementinstruction. These are called Adaptive Computer AssistedInstructions (ACAIs) (Davidovic, Warren, & Trichina, 2003)or Personalized VLEs (PVLEs) (Lassey, 1998; Martinez &Bunderson, 2000). The key issue of such approaches is thecustomization of learning environments for diverse studentcommunities, which has been attracting more and moreattention by educational professionals and researchers (Alavi,2004; Castro, Kolp, & Mylopoulos, 2002; Roach, Blackmore,& Dempster, 2001). PVLEs tend to engage high-levelpersonalized eLearning and to provide opportunities forA conceptual model of personalDongming Xua,b, HuaiqinaBIS, UQ Business School, The UniversitybDepartment of Information Systems, City University ofAbstractThe Virtual Learning Environment (VLE) is one of the fastest groto achieve learning effectiveness, ideal VLEs should be able to identto supplement instruction. They are called Personalized VLEs (PVmodels corresponding to PVLEs are essential. Such conceptual modcorrection of system development errors. Therefore, in order todevelopment of conceptual models for PVLEs, including models od virtual learning environmentsangb,*, Minhong Wangbueensland, St Lucia, Qld 4072, AustraliaKong, Tat Chee Avenue, Kowloon, Hong Kong, Chinaareas in educational technology research and development. In orderarning needs and customize solutions, with or without an instructor). In order to achieve PVLEs success, comprehensive conceptualdevelopment is important because it facilitates early detection andure the PVLEs knowledge explicitly, this paper focuses on thewledge primitives in terms of learner, curriculum, and situationalions 29 (2005) 525534www.elsevier.com/locate/eswasituations and is inherently a creative, generative andreflective process (Alavi & Leidner, 2001). Individual onlinelearners can be uniquely identified, with content specificallypresented and progress individually monitored, supportedand assessed (Akhras & Self, 2000). The learning process is0957-4174/$ - see front matter q 2005 Elsevier Ltd. All rights reserved.doi:10.1016/j.eswa.2005.04.028* Corresponding author. Tel.: C852 2788 8491; fax: C852 2788 8535.E-mail addresses: dxu@business.uq.edu.au (D. Xu), iswang@cityu.higher cognitive abilities and foster creativity. Such person-alized eLearning requires higher-level thinking in more openbetween traditional hypermedia systems and personalizedsystems (Raad & Causse, 2002). In this research, Raad andith Apa reflection of inherently active, generative and reflectivehigh-level thinking in more open situations (Pea, 1985).Therefore, PVLEs are becoming more promising towardslearning effectiveness (Lassey, 1998). PVLEs provideopportunities for online learners to amplify and extendcognitive capabilities as well as to organize the learningprocess by altering the tasks available to them. PVLEsemphasize the importance of scaffolding learner self-regulation and strategic process to help online learnersmanaging the complexity of the learning situation (Scarda-malia, Bereiter, McLean, Swallow, & Woodruff, 1989).In order for the PVLEs to be succeeded, the compre-hensive conceptual models corresponding to PVLEs areessential. A conceptual model is an explicit specification ofa conceptualization, which defines the terminology of adomain in terms of the concepts that constitute the domainand the relationships between them (Gruber, 1993).Conceptual models are these diagrams used by systemsanalysts to represent specific requirements for new appli-cations. These diagrams are designed to support communi-cation between developers and users, to help analystsunderstand a domain, and to provide an input to systemsdesign. Within the IS field, the task of conceptual modelingtypically involves building a representation of selectedphenomena in some domain consisting of a hierarchicaldescription of the important concepts in a domain, as well asthe properties of each concept (Wand & Weber, 2002).High-quality conceptual modeling work is importantbecause it facilitates early detection and correction ofsystem development errors. The development of conceptualmodeling has drawn on results from knowledge represen-tation and conceptual modeling (Mylopoulos, 1990; Wand,Monarchi, Parsons, & Woo, 1995).The basic problem of conceptual modeling involves thedevelopment of an expressive presentation notation withwhich to represent knowledge (Wand & Weber, 2002).Therefore, the conceptual models of PVLEs are based on theknowledge of instructional design that must be rooted instrong pedagogical principles.Most existing VLEs are based on the objectivistlearning model. The objectivist learning model is basedon stimulus-response theory. Learning is a change in thebehavioral disposition of an organism that can be shapedby selective reinforcement. There is an objective realityand the goal of learning is to understand this reality andmodify behavior accordingly. The goal of teaching is tofacilitate the transfer of knowledge from the instructor tothe learner. The instructor should be in control of thematerial and pace of learning. Via exercises, the instructorassesses whether knowledge transfer has occurred. To theobjectivist learning model, the presentation of informationis critical of the successful knowledge transferring (Leidner& Jarvenpaa, 1995).By way of contrast, the constructivist learning modeldenies the existence of knowledge transfer. From aD. Xu et al. / Expert Systems w526constructivist point of view, knowledge is created orCausse (2002) classify and model personalized methods intotwo categories: personalized presentation (includingadditional explanation, prerequisite explanation, compara-tive explanation, and sorting explanation); and personalizednavigation (including direct guidance, sorting links, hiddenlinks and annotation links).In this paper, a conceptual model of PVLEs, which canbe regarded as an application of the constructivist learningmodel, is described. In order to represent such models, anumber of knowledge primitives of VLEs is modeled inSection 2, such as curriculum, learners, instructors, etc.Section 3 describes the conceptual models of VLEs ingeneral based on two pedagogical principles: objectivistlearning and constructivist learning model. Based on thediscussion in the above sections, the ontology of PVLE isdeveloped in Section 4, while the experimental evolution ofPVLE is presented in Section 5. Section 6 is the conclusions.2. Conceptual models of knowledge primitivesIn order to ensure that a VLE can work, it should have alarge knowledge base. Such knowledge can be modeled intotwo levels: the domain level that is related to the real worlddomain, and the meta level that is about the knowledge ofthe domain level knowledge.2.1. Domain level modelThe domain level knowledge in VLEs contains thecurriculum of domain knowledge, learners, instructors andthe relationship among them. The curriculum model is thestructure of the curriculum. A portion of the Curriculumclass diagram of such a model is shown in Fig. 1. TheCurriculum class in Fig. 1 has a number of importantattributes, such as keywords, difficulty level, description,constructed by each individual learner (Jonassen & Wilson,1993). Learners are assumed to learn better when they arerequired to discover things by themselves rather than simplythrough the process of being instructed. Learners mustcontrol the learning plans. The instructors serve as thecreative mediators of the process. They provide tools forhelping learners construct their own views of reality. Veryrecently, a few constructivist learning model-based VLEshave been developed (Akhras & Self, 2000). Such VLEsneed to be attuned to special features of the learner, thelearning environment, and the interaction between learnerand learning environment.The research and development of PVLEs are still at anearly stage. Currently, the most existing research onlyprovides the conceptual models of PVLEs withoutimplementation and validation. For instance, the Personal-ized Hypermedia Systems aim mainly to bridge the gapplications 29 (2005) 525534etc. Content is a sub-class of Curriculum. There areegies are stored in the class Instruction, including : Strilevel : Strrriculu+dis+aduestioolutioExhapttiongramith Apa number of attributes in the class Content, e.g. pre_requisite, co_requisite, exercise, example, etc.Similar to Fig. 1, Fig. 2 shows a portion of learners andinstructors models. In VLEs, learners are engaged in goal-orientated learning processes. Based on pedagogical prin-ciples, learning goal is either defined by instructors under theobjectivist learning model or learners under the constructivistlearning model. Dweck identified two major classes of goalorientations: learning goal orientation, that is to developcompetence through expanding ones abilities by mastering-keywords-different_-discriptionCu+display()-pre_requisite : Content-co_requisite : Content-exercise : Exercise-sample : Exercise-part_of : Content-contain : ContentContent-q-sChapter-part_of : CSec1 0 ..*containFig. 1. Partial class diaD. Xu et al. / Expert Systems wchallenging situations; and a performance goal orientationthat involves demonstrating and validating ones competenceby seeking favorable judgments and avoiding negativejudgments (Dweck, 1986). Learning and performance goalorientations are associated with different personal beliefsabout ability and effort (VandeWalle, Cron, & Slocum, 2001).VLEs emphasize the nature of knowledge, learning andteaching, which have led to architecture that focuses onrepresenting the knowledge to be learned (curriculummodel), inferring the learners knowledge (learner model),and planning instructional steps to the learner (instructionmodel) (Akhras & Self, 2000). The construction of dynamiclearner profiles is based on the learners behavioral patternsand their learning activities. The profiles can be utilized tosupport collaborative learning and to enable personalizedinstruction to different learners. Interaction among learnersin VLEs plays an important role in fostering effectivelearning process (Piccoli, Ahmad, & Ives, 2001). Knowl-edge sharing or building is the process by which aninstructor and learners achieve, through discussions, ashared understanding of a particular concept. Hooper alsoindicates that persisting interactions are correlated posi-tively with learners achievement (Hooper, 2003).teaching_attitude, motor_skill, intellectual_skill, andproblem_solving_skill.2.2. Meta level modelFrom the discussion in Section 1, VLEs achieve greaterThe Learners model is related to Learning_Goal,Learning_Plan and Learner_Profile. It has an associatedaction, self_assessment. A number of instructional strat-ng: Integeringmplay()vise()n : Stringn : Stringerciseer -part_of : SectionConcept..*......1 0containfor course curriculum.plications 29 (2005) 525534 527learning effectiveness when they need to be attuned tofeatures of the learner (i.e. the situation), the interactionbetween learner and learning environment, and the learningprocess. The meta level entities do not focus on basicknowledge structure, but on the nature of the learnerslearning contexts through which learners can construct theirown knowledge about a domain by experiencing the domainand interpreting their own experiences (Akhras & Self,2002). A portion of these meta level entities is presentedin Fig. 3.As shown in Fig. 3, the Situation model characterizes anopen context in which learners interactions make explicit inthe VLEs the information about the context in which theinteractions occur and the nature of these interactions. Anumber of important attributes are defined in the Situationentity (Akhras & Self, 2000). The event represents thecurrent situation; the pre-condition represents an event thatmust occur before the current situation, and the post-condition represents an event that will occur after thecurrent situation.The Interaction model is related to the occurrence ofevents or the entities that hold situations and to the cognitivestates, activities, and contexts. An interaction object is-learning_goal : Learning_goal-learning_plan : Learning_plan-learner_profile : Learner_profile-classmate : LearnerLearnerLearning_goalmepath TimestrateLearning_plan Learner_profileD. Xu et al. / Expert Systems with Applications 29 (2005) 525534528-course :Curriculum-completion : Date-expected_grade : String-long_term_goal : Learning_goal-when : Ti-learning_-duration :-learning_+assessment()+gain_attention()-teaching_attitude : Pedagogical_Model-motor_skill : Pedagogical_Model-intellectoral_skill : Pedagogical_Model-problem_solving_skill : Pedagogical_ModelPedagogical_Modelusually in a particular situation and shares some situation(s).The learners actions are to capture the various ways, such asutilizing, generating, or accessing objects. The learnerscognitive states are intended to capture the various waysin which entities of a situation are related to learnerspreviously formed cognitive structure (Akhras & Self, 2002).The Process model presents how patterns of interactionin one situation are connected to patterns of interaction inanother situation. The cumulativeness represents how theknowledge learned in one situation can be used in anothersituation later. The constructiveness represents the inte-gration of a learners previously constructed knowledgewith aspects of new learning experiences. The self-regulatedness represents the meta-cognitive processes in+processing()+feedback()Fig. 2. Partial class diagram for the lea-event : Event-precondition : Event-postcondition : Event-effect : EventSituation-learner_action : Learner-learner_cognitive_state-capture_relation : Situat-share : Situation-in : SituationInteractionFig. 3. Partial class diagram for situ: Contentgy : String+effort()+achievement()-name : String-learner_ID : String-time_spent : Double-grade : String-learning_path : Content-exercise_taking : Exercisewhich aspects of previous learning experience are revised ina later situation (Akhras & Self, 2002).3. Conceptual models of virtual learning environmentsIn Section 1, we noted that most existing VLEs are basedon the objectivist learning model. Such VLEs are teacher-centered learning environments that are able to provideonline learners with pre-defined course content according tolearning plan, which specifies the sequence of these contentunits. Therefore, online learners are passive in learningactivities and lack the opportunities to interact with theenvironments appropriately. An ideal VLE should be builtrner and the pedagogical model._profile : Situationion-cumulativeness : Interaction-constructiveness : Knowledge-self_regulatedness : Process-reflectiveness : ProcessProcess11..* containation, interaction and process.with reference to the constructivist learning model whereonline learners are able to control their learning process,receive adaptive instruction, experience personal relevantsubject matters, and communicate with VLEs.3.1. VLEs under the objectivist learning modelAccording to the objectivist learning model viewpoint,the pedagogical role of the system is to present to the learnerthe content of instructional units according to a plangenerated by an instructional planner. These units ofcurriculum can be lessons, problems, explanations,examples, exercises, tests, etc. related to the knowledge ofthe domain, and the plan describes how these units are to besequenced. It is usually divided into two phases, carried outby the instructional planner: content planning, whichdefines, according to the instructional goals and character-istics of the learner, the concepts to be learned andsequenced; and delivery planning, in which, for eachconcept to be learned the planner generates a sequence ofD. Xu et al. / Expert Systems with ApLearnerinstructional actions adapted to the individual learner. Theobjectivist-based model presented in Fig. 4 is based on anumber of existing eLearning systems (Wang, 1997a,b; Xu& Wang, 2002). The model demonstrates the relationshipsbetween teaching model, domain model and learner model.The domain is modeled in terms of the knowledge to belearned into the Curriculum model that stores all thecurriculum information, such as contents, exercises,examples, and the relations among them. It also specifiesthe relationships between these components, such as logicaldependencies and hierarchies (Akhras & Self, 2002).A learners knowledge is modeled in terms of thelearners correct or incorrect knowledge concerning thedomain. When a learner is studying, his/her learningCurriculumContent ExercisesExamplesLearning PlanLearningLearningGoalInstructor. . .Teaching ModelDomain ModelLearner ModelFig. 4. Conceptual model of objectivist VLEs.activities (such as his/her learning path, learning time,learning pace, and learning achievements) are monitored andanalyzed and the profiling data and stored in the learnermodel. Learners receive the content of instructional unitsaccording to a learning plan generated by the instructor.These units can be lessons, explanations, examples,exercises, tests, etc. related to the knowledge of the domainand the plan describes how these units are to be sequenced(Akhras & Self, 2002). A teaching model represents theknowledge to select teaching strategies for instructionalactivities, present them to learner, and handle the learnersresponse. Under the objectivist pedagogical principle, VLEsemphasize the structure of domain knowledge, the waylearners learn, and the way learning can be promoted.Based on the above perspective, the philosophy of mostexisting Intelligent Tutoring Systems (ITSs), a kind of VLEs,is based on a more objectivist learning model of the nature ofknowledge and of what it means to acquire knowledge. Insuch ITSs, the instructors, i.e. the instructional agents in theVLEs, play the major role for the learning plans, based on thedetermination of the cognitive state of the learner in terms oftheir knowledge and misconceptions.3.2. VLEs under the constructivist learning modelThe proponents of the constructivist model believe thatadaptiveness is mainly based on features of the learner,learning situation and the learning process. However,constructivist-based VLEs are very new. The constructivistlearning model presented in Fig. 5 is based on the INCENSEsystem, developed by Akhras and Self (2000).Under the constructivist pedagogical principle, VLEs areattuned to features of the learner, the environment, and theinteraction between learner and environment. The mainimplications for the design of constructivist VLEs empha-size that the domain is modeled in terms of situations ratherthan in terms of knowledge structure, that learningevaluation focuses on the learning process rather than onthe achievement itself, and that the opportunities for learningarise from afforded situations rather than being provided onthe basis of teaching strategies (Akhras & Self, 2002).In the constructivist learning model, the knowledge isindividually constructed from what learners do in theirexperiential worlds and cannot be objectively defined (vonGlasersfeld, 1989). Knowledge cannot be pre-specified beforelearning. Rather than concentrating on logical analysis ofdomain structure and dependency relationships between thecontents, the concern of the constructivist learning modelfocuses on the learning processes through which theperspectives and interpretations that are relevant to learningcan be constructed. Therefore, the domain is modeled in termsof the learning situation rather than its structured knowledge.Learning is an interactive process between learning andenvironment, i.e. learning by doing. This process is a time-extended process of interacting in situations that involvesplications 29 (2005) 525534 529aspects of learners actions, learners cognitive structures,environmentsCuExIntLdelSdel ofith Apand contexts of interaction between learner and environ-ment that span time (Akhras & Self, 2002). It focuses notonly on the knowledge to be acquired, but also on theknowledge to be constructed. Therefore, the process modelis broader than the learner model in objectivist VLEs withthe cognitive states and properties of interactions and ofsequences of interactions that can denote how learners careconstructing knowledge.The learning sequence, in constructivist VLEs, emergesin the interaction between the learner and the environmentfrom a combination of factors that depend on theopportunities available for the learner in the interactioncontexts and on the learners previously constructedknowledge (Akhras & Self, 2002). These opportunitiescharacterize affordability of learning situations to learnerswhose learning process is at a certain state. The utility of asituation for a learner at a certain time is determined byaffording that situation with respect to features of singleContentInstructorAffordanceSituation MoAffordance ModelProcess ModelFig. 5. Conceptual moD. Xu et al. / Expert Systems w530interactions and with respect to features of time-extendedprocesses of interaction (Gibson, 1977). Therefore, a modelof affordances indicate the possibilities in situations for thedevelopment of relevant learning activity, for a learnerwhose learning process is in a certain state, and is the basisfor creating spaces of interaction for the learners.From the discussion above, it is summarized that theconstructivist model of VLEs is broader in perspective thanthe objectivist model of VLEs. In constructivist VLEs, thelearning process at a certain time is modeled by the set ofpatterns of interaction developed up to that time (theinteraction model) and by the set of properties of the coursesof interaction that hold as a consequence of these patterns(the process model). Modeling affordance of learningsituations allows constructivist VLEs to adapt the inter-action that occurred in the VLEs and the process of suchinteractions of learning.In short, the learners learning process in a constructivistVLE plays the major role for the adaptiveness ofIn order to demonstrate the usefulness of the conceptualmodels developed in the previous sections, an applicationusing such models to develop the formal representation ofPVLEs is described in this section. PVLEs are based on theconstructivist learning pedagogical where they are attunedto features of the learner, the environment, and thethe learning situation. The role of the pedagogical strategyis not to determine instructional events but to provideprofitable spaces of interaction to the learners, which aredetermined on the basis of the interaction model and theprocess model. PVLEs can be viewed a kind of constructi-vist VLE in terms of learners learning process.4. Ontology of personalized virtual learningrriculumExercisesampleseractionearnerLearningGoal. . .ituationconstructivist VLEs.plications 29 (2005) 525534interaction between learner and environment. To developa prototype PVLE, intelligent agents supported personalizedeLearning system, for introductory Information Systems,ontology for representing the conceptual model is describedin Fig. 7 based on the modeling method, Tropos (Bresciani,Anna perini, Giorgini, Giunchiglia, & Mylopoulos, 2004;Castro et al., 2002).Tropos proposes a software development methodology anda development framework, which is founded on concepts usedto model early requirements by utilizing the notions of actor,goal and (actor) dependency (Bresciani et al., 2004; Castroet al., 2002; Mylopoulos, Kolp, & Castro, 2001). The Troposapproach is a requirement- and goal-oriented software-modeling method, which is particularly appropriate forgeneric, component software systems (Jonassen & Wilson,1993). Comparing with UML, a popular modeling tool,Tropos could be used to present agents, their goals, and thedependencies among them. Using the Tropos methodology,we are able to model the world from the following veryimportant perspectives: (1) Social entities, such as relevantagents/actors (e.g. learners, instructors, planers, etc.), theirobligations and capabilities; (2) Agent intentions; (3)Communications and dialogs among agents; (4) Learningprocesses and their relationships.time, learning pace, and learning achievements) aremonitored and analyzed by the Activity agent and theprofiling data will be stored in the Learner_Profile.Based on the situation and the Learner Profile, theInteraction agent models such interaction and buildsthe Interaction model. The Process agent will evaluate theinteraction model and build the new process model. By theemergence of learning situations, the interaction model andthe process model, the Interface agent will providepersonalized interaction, personalized contents, and person-alized exercises to the learner.Based on this approach, a prototype PersonalizedeLearning System Architecture was designed and thecorresponding Intelligent eLearning System (IeLS) wasimplemented (Xu & Wang, 2002). The IeLS architecture isshown in Fig. 8.There are three layers in the IeLS. The Repository layerXDepender DependeeDependencyDecomposition LinkActor/AgentRessourceGoalTaskLegendSoft GoalMeans-EndslinkFig. 6. The stereotypes.AdaInteraD. Xu et al. / Expert Systems with Applications 29 (2005) 525534 531Using such methods, each category of knowledge in aPVLE is treated as a class (with its instances being itsinstantiation). All classes of information can be organizedwithin a hierarchy. Such a knowledge hierarchy can bedivided into two levels: the domain level and the meta level(Wang, 1997a,b). The domain level knowledge is therepresentation of domain entities, while the meta levelknowledge is the knowledge about domain level knowledge.In our conceptual model, all the knowledge about thecurriculum, about the learners, about the instructors, isdomain level knowledge; while the knowledge aboutsituation, interaction, and the processes is meta levelknowledge. The stereotypes of the Tropos figures areshown in Fig. 6 (Mylopoulos et al., 2001).In the ontology of the PVLEs (shown in Fig. 7), theCurriculum stores the curriculum information, such ascontents, exercises, examples, and the relations amongthem; the Learner Profile stores the learning history of eachindividual learner. When a learner is studying, his/herlearning activities (such as his/her learning path, learningLearning_GoalModelingInteractionModelingProcessProcessAgentActivityAgentSituationProfilingInteractionAgentInteractionModel InterfaAgenFig. 7. Ontologycontains a number of resources, such as the Content Model(Curriculum), the Student Profile, the Student Model andthe Learning Plan. Similar to Fig. 1, all the contents, i.e. thechapters, the sections, the concepts, and the exercises,are represented by the Curriculum model and stored inthe system repository. The structured information, e.g. therelations among these entities, is also stored in the repository.Other than these static models, the dynamic models shown inFig. 4, such as the student model, the student profile, and thelearning plan, are also implemented and stored in therepository initially.Based on the constructivist learning model in PVLEsdescribed in Fig. 8, a number of software agents have beendeveloped in the IeLS: the Activity Agent, the ModelingAgent, the Planning Agent and the User Interface Agent.Combining the meta level model of the constructivistlearning model, the learning process can be described asfollows: the activity agent records the student interactionactivities in the learning process and generates the studentprofile. Based on such student profile, the modeling agentLearnerLearningCurricalumProcessModelAdaptivenessAdaptiveExercisesAdaptiveContentsptivectioncetof PVLEs.revtheinvser ding GeneactingodelinstractielAdaInteearninith Apthe IeLS can be described as follows:(1) Learning. Different types and levels of learningmaterials are provided to the learner by the IeLS, basedon the learners model, the learning plan, and theinstruction model. Therefore, individual learners areable to construct the domain knowledge based on theirindof5.peises the student model at a certain time sequence withinprocess model. Such modification of the student modelokes the planning agent to revise the learning plan, whichicates the possibilities in situations for the developmentrelevant learning activity. The personalization features ofFig. 8. Personalized eLActivity AgentRecording Activities;MAbStudent ModStudent ProfileRepository LayerAgent LayerUSeninterUser LayerD. Xu et al. / Expert Systems w532own learning pace and interaction with the learningenvironment.(2) Self-evaluation. After learning a section, the learner isadvised to take exercises. The questions are generateddynamically based on the content model, the currentlearners model as well as the instruction model. Theself-evaluation is not only that the examination ofthe domain knowledge will be successful, but also thecognitive learning process. It indicates the affordance ofthe relevant interaction of sequenced learning byindividual learner.(3) System adjustment. Based on these analyses, the IeLSwill perform the following tasks: (a) modify the learnersprofile; (b) modify the current learning plan based on theinstruction model; and (c) start a new that takes newmodifications in to account.Experimental evaluation of the PVLEsIn order to evaluate our prototype system in the areas ofrsonalized learning facilities and learning effectiveness,the prototype system, IeLS, was developed and a fieldexperiment was conducted with a 4-day online course toundergraduate students with free registration in April 2002in our university. In this experiment, participating studentsperformance and perception were collected.The field experiment was designed to adopt two parallellearning groups repeated measure to vary the learningenvironments, which are non-personalized regular eLearn-ing System (eLS) and personalized Intelligent eLearningSystem (IeLS). The personalization facilities were devel-oped in the IeLS, and retain the eLS as a control eLearningenvironment. Both systems deliver the instruction of theAgentActivities;rating; materialsg Agentng Models;Planning AgentMaking Plans;Content ModelLearning PlaniMacptiverfaceg system architecture.plications 29 (2005) 525534same course, Introduction to the Oracle Database, which isa four-chapter online course. In total, 228 studentsparticipated. They were assigned randomly to two systems,and completed the course work during the experiment,which lasted 4 days. Hundred and seventeen of them wereusing IeLS and 111 students used eLS. In the beginning ofthe experiment, students were required to take a pre-test,and then move on to the learning procedure. They receivedthe instruction directly from the systems, took quizzes aftereach chapter, and then took the final exam. The mainobjective of our experiment was to evaluate the studentslearning achievements when using our prototype IeLS.Through an Independent Samples Test, we derived thelearning performance comparison of the two groups ofstudents. Details are presented in Tables 1 and 2 andillustrated in Fig. 9.Because the students were assigned to two systemsrandomly, their pre-test scores are comparable, indicated inTable 1 and Fig. 9. There is no difference in terms ofstudents learning achievements in the Chapter 1 quiz,which might be due to the fact that students were not yetfamiliar with the system facilities, and the learning time wastwo groups differed significantly, with students who usedIeLS achieving higher scores than the students who usedtheitheithrothethisChapter 3 Chapter 4 Total38 44 18054 57 2552.292 (0.024) 2.057 (0.34) 3.331 (0.001)r 2 quiz Chapter 3 quiz Chapter 4 quiz Final exam76.8 74.4 83.370.2 65.1 72.30.027) 2.080 (0.039) 2.586 (0.011) 3.316 (0.001)ith Applications 29 (2005) 525534 533eLS. This indicates that the IeLS, our prototype of PVLE,can provide a better VLE, which can help students toachieve greater learning effectiveness.Table 2 depicts the time spent on learning the contentsin each chapter and shows the significant efficiency oflearning in the IeLS. Fig. 9 also demonstrates thedifferences between the two study groups in terms of thetime spent to perform each quizzes and the final exam. Itwas predicted that students who study in the IeLS would(1) spend less time to (2) achieve better exam scorescomparing with those studying in eLearning System(shown in Fig. 9). MANOVA analysis reports that studentsin IeLS achieved significantly higher learning performancecompared with those in eLS and manifesting a F value of3.745 (pZ0.002).6. Conclusionstoo short to achieve a learning difference. From CourseChapter 2 on, we found that the learning performances in theTable 2Learning time spent comparison (mean of minute)Chapter 1 Chapter 2IeLS 33 59ELS 42 83t (p-value) 3.088 (0.02) 2.957 (0.03)Table 1Learning performance comparison (mean of score)Pre-test Chapter 1 quiz ChapteIeLS (117) 55.7 66.1 83.0ELS (111) 54.9 71.3 75.6t (p-value) 0.246 (0.806) K1.583 (0.115) 2.228 (D. Xu et al. / Expert Systems wThis study focuses on the development of the conceptualmodels for the personalized VLEs, including the modelsof knowledge primitives in terms of learner, curriculum,pedagogical, and situational models, models of VLEs ingeneral, and particularly, the definition of the ontology ofPVLEs based on constructivist pedagogical principle. Itconcludes that the domain knowledge is modeled in terms ofthe learning process situation rather than the knowledgeitself, and that learning is afforded as a result of the learningsituation rather than particular teaching strategies.Based on those comprehensive conceptual models, aprototyped multiagent-based educational system has beenimplemented. A field experiment was conducted toinvestigate the learning achievements of personalizedVLEs by comparing personalized and non-personalizedeLearning systems. The experimental results reveal thatonline learners using the personalized VLE achievedperspective;(b) a conceptual model of PVLE, which is based on theconstructivist learning model represented by a power-ful modeling method, Tropos;(c) the agent-oriented ontology of PVLEs, where agents(or actors) in the model are able to carry out actions toachieve goals or perform tasks with intentions; and(d) the separation of the domain level and the meta levelthat reveals the meta level model provides deepunderstanding of the learning.(a)0102030405060708090100study include:a formal representation for VLEs in terms of knowl-edge primitives perspective and pedagogical principlecomugh individualized instruction. It also indicates thatPVLE system was developed successfully under ourprehensive ontology. In summary, the contributions ofthinortunities for online learners to amplify and extendr cognitive capabilities as well as to organize theking processes by altering the tasks available to themSystoppificant greater learning achievements as compared tor counterparts using the non-personalized eLearningem. Thus, it indicates that personalized VLEs providesign4 37 84 381577665683748470715565 727611 1210 13Pretest Quiz1 Quiz 2 Quiz 3 Quiz4 FinalExamIeLS (Score) eLS (Score)IeLS (Time Spent, minute) eLS (Time Spent,minute)Fig. 9. Learning achievements.The application of our models can lead to unambiguousunderstanding of the concepts and pinpoint the likely causes oflearning failure. Furthermore, such conceptual models providea uniform framework with which different approaches can beintegrated together to provide more sophisticated functionsand facilities. Therefore, by creating a rich conceptual model,J. (2004). Tropos: An agent-oriented software development method-ology. 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Intelligent agent assisted decision support systems:Integration of knowledge discovery, knowledge analysis, and groupdecision support. Expert Systems With Applications, 12(3), 323335.Wilson, B. G. (1996). Constructivist learning environments: Case studies ininstructional design. Englewood Cliffs, NJ: Educational TechnologyPublications.Xu, D., & Wang, H. (2002). Intelligent student profiling with fuzzy models.Proceedings of the Hawaii international conference on systems science,Hawaii, USA.A conceptual model of personalized virtual learning environmentsIntroductionConceptual models of knowledge primitivesDomain level modelMeta level modelConceptual models of virtual learning environmentsVLEs under the objectivist learning modelVLEs under the constructivist learning modelOntology of personalized virtual learning environmentsExperimental evaluation of the PVLEsConclusionsAcknowledgementsReferences


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