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more semantics in contrast to concept maps. Theontology has value constraints, where values of theconcept properties are restricted with data types, valuerange or allowable value list. It also contains axiomsand restrictions about concepts linkage. It has enoughdetailed relations between concepts, e.g. inverserelations and disjoint classes.We suppose that the usage of ontology in evaluationof learner-created concept maps have some advantages.In cases, when learner’s concept maps do not fullymatch with the predefined ontology, semanticcomparison may be performed. This includes searchingfor the synonyms of concepts and links and checking ofconcept linking. In cases if concepts are relatedimplicitly, ontology helps to verify if alternativelinking is logical. In addition, missing concepts that thelearner failed to master in learning activities areidentified. It allows providing learners with necessaryadditional materials that enable to prevent determinedweaknesses in concepts mastery.The system’s deployment with ontologies requiresalso changes in its architecture (see Figure 9). Thesystem may be extended in two directions: theadditional database provides the learner with advicesand necessary learning materials to improve his/herknowledge level, and the remedial agent finds theappropriate material. Mentioned system’s extensionsobligate teachers not only to create course ontology butalso to prepare materials.The system’s operation is provided by interactionsbetween two intelligent agents: the assessment agentand the remedial agent. Each of these agents consists ofseveral agents. The assessment agent has the samestructure as its predecessor in the previous system’sversion (see Figure 1). The remedial agent is requiredfor analyzing learner’s concept maps to determinewhich concepts are incorrectly mastered. The remedialagent includes the analysis agent and the search agent(see Figure 10), which determine non-masteredconcepts and appropriate additional learning material,respectively. The remedial agent also finds whichconcepts are causes of non-mastery of other concepts.The agent backtracks in predefined course ontology todetermine which predecessor concept is incorrectlymastered.A rather similar approach is proposed in (Jong et. al.2004) where in concept maps missed concepts aresearched using Remedial – Instruction Decisive path.After determination of missed concepts, the agentsearches appropriate additional learning materials inthedatabase.LEARNERTEACHERCourseontologyFeedbackCourseontologyAdditionalmaterialStandardontologyLearners’ conceptmaps and scoreStructureof theconceptmapLearner’s concept mapof the previous stageLearnercompletedconceptmapAssessment agentLearner’sconcept mapand scoreFeedback,scoreNon-masteredconcepts andstandardontologyLearningmaterialsAdditionalmaterialAdditionalmaterialRemedialagentINTELLIGENT ONTOLOGY-BASED KNOWLEDGE ASSESSMENT SYSTEMFigure 9. Typical interaction between users and the intelligent ontology-based knowledge assessment system.Annual Proceedings of Vidzeme University College “ICTE in Regional Development”, 20066
LEARNERStructure of theconcept mapLearnercompletedconcept mapFeedback,scoreAdditionalmaterialCommunication agentStructureof theconceptmapLearnercompletedconceptmapAssessment agentFeedback,scoreNon-masteredconcepts andstandardontologyMasteryanalyzingagentNon-masteredconcepts andtheir causesINTELLIGENT REMEDIAL AGENTAdditionalmaterialSearchagentLearningmaterialsAdditionalmaterialFigure 10. The architecture of the remedial agent.CONCLUSIONSEmerging of a knowledge-based society andgrowing demand for the highly skilled and educatedworkforce lead to emphasizing of qualitative aspects oflearning. It, in turn, affects not only the content ofcurriculum and the organization of educational process,but also knowledge assessment.Acquiring of qualitative knowledge is achievablethrough the management of this process both from theteacher side through knowledge assessment at eachstage of a learning course, and from the learner side bymeans of knowledge self-assessment. In both cases,assessment results can be used to determine and toremedy knowledge misconceptions andmisunderstandings timely. As both assessmentsdemand additional time and cost, they are usuallyignored by educational institutions.Given paper describes a computerized tool- anintelligent knowledge assessment system based onconcept maps, which can assist in the mentionedproblem solving.The developed system uses concept maps as a toolof knowledge assessment. Concept maps visuallydisplay concepts and relationships between themwithin problem domain. Thus, the teacher offers suchmaps as a task for knowledge self-assessment or usesthose for knowledge assessment at each stage of alearning course.The teacher's concept map serves as the standardwith which the learner-completed concept map iscompared. The system is sensitive to variations offilling the concept maps using for this purpose theintelligent comparison algorithm.Positive results of system testing in four learningcourses that are described in detail in this paper haveprovided a basis for further development of the system.One of directions of future work is related with theuse of ontologies for deepening knowledge assessmentand for generating the recommendation concerninglearning material, which a learner should be masteredfor filling blanks in his/her knowledge.Annual Proceedings of Vidzeme University College “ICTE in Regional Development”, 20067
Page 1 and 2: ISBN 9984-633-03-9Annual Proceeding
Page 3 and 4: “Development of Creative Human -
Page 5 and 6: TABLE OF CONTENTSINTELLIGENT SYSTEM
Page 7 and 8: INTELLIGENT SYSTEM FOR LEARNERS’
Page 9 and 10: LEARNER 1GROUP OF HUMAN AGENTSLEARN
Page 11: QuantityQuantityFigure 6. Distribut
Page 15 and 16: WEB-BASED INTELLIGENT TUTORING SYST
Page 17 and 18: materials to be presented and which
Page 19 and 20: INFORMATION TECHNOLOGIES AND E-LEAR
Page 21 and 22: correspondence with the course aim
Page 23 and 24: projects and through IT. Hence, it
Page 25 and 26: APPLICATION OF MODELING METHODS IN
Page 27 and 28: can support configuration managemen
Page 29 and 30: The EKD is one of the Enterprise mo
Page 31 and 32: CHANGES TO TRAINING AND PERSPECTIVE
Page 33 and 34: or an end, yet none of these attitu
Page 35 and 36: make decisions. It cannot be volunt
Page 37 and 38: logs), data and video conferencing
Page 39 and 40: Ability to follow user’s multi-ta
Page 41 and 42: CONCLUSIONSEDUSA method gives us a
Page 43 and 44: in successful SD. Given this situat
Page 45 and 46: SPATIAL INFORMATIONFor the visualis
Page 47 and 48: MOBILE TECHNOLOGIES USE IN SERVICES
Page 49 and 50: learning environment (Learning Mana
Page 51 and 52: ago only some curricula on Logistic
Page 53 and 54: The Web-based version can be access
Page 55 and 56: Web-portal, which incorporates diff
Page 57 and 58: DO INTELLIGENT OBJECTS AUTOMATICALL
Page 59 and 60: Table 1. Examples for introducing R
Page 61 and 62: workable influencing of the process
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are handed over to the objects and
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• Basic processes, such as wareho
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THE ECR E-COACH: A VIRTUAL COACHING
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participating in the workshops and
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• Assessment modules enable indiv
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with pictures and illustrated graph
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ECR Question Banknumber category su
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educational programme that follows
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DEVELOPMENT OF WEB BASED GRAVITY MO
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These results of a model require a
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CONCLUSIONSThe main goal of work ha
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dimension and included within any o
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• Resources sharing by providing
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Pursuant to the guidelines of elect
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tariffs of regulated services have
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INFORMATION TECHNOLOGY FOR MOTIVATI
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difficult to predict when and for w
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Listeners' workon the WebListenersS
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PERSPECTIVES OF WEB PAGE AND E-MAIL
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INCREASE IN THE NUMBER OF INTERNETU
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tourism accommodations (home pages
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interactive relationships with clie
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• The data obtained by the resear
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Central Statistical Bureau of Latvi
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departures for 1995 are taken from
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120100maximumworldminimum806040200-
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140120maximumworldminimum1008060402
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would be a promising extension. Cur
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AN OVERVIEW OF THE AGENT − BASED
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Suitability for social system simul
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6. MASONDescription:MASON is a fast
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Suitability for social system simul
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could be bad particularly when over
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(for 10 repeat &| CCar[]->runfor);P
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• Streaming audio• Collaboratio
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NECESSITY OF NEW LAYERED APPROACH T
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Up to now, there has only been limi
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aaaaa6= −aa2,1 = − a0,3226= −
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∂ u∂x∂ u∂y2 2+ b = 02 2wher
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a6,3= −2030a4,5−130a4,3- - - -
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0,10,20,30,4( )Mag x y y Ge wx2, =
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Example 1. To understand better the
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Therefore, further the following co
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SOLUTION OF THE THREE-DIMENSIONALEQ
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Mag1, m , m , m1 2 3= mm1 m2m32 2 2
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MagMag0, m , m , m1 2 31, m , m , m
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CONCLUSIONSThe basic content of thi
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