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Irina Yevseyeva Niilo Mäki Instituutti University of Jyväskylä

Information technology in education & management Computerized Estimation of Children’s Learning Abilities. Irina Yevseyeva Niilo Mäki Instituutti University of Jyväskylä Kharkiv National University of Radioelectronics iyevsev @cc.jyu.fi. Objectives.

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Irina Yevseyeva Niilo Mäki Instituutti University of Jyväskylä

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  1. Information technology in education & managementComputerized Estimation of Children’s Learning Abilities Irina Yevseyeva Niilo Mäki Instituutti University of Jyväskylä Kharkiv National University of Radioelectronics iyevsev@cc.jyu.fi

  2. Objectives • Expert systems for the learning processes • Application of MCDM methods for the assistance in the learning process • MCDM methods & ES

  3. Survey of the situation in theleaning abilities • The problem of testing and estimation of pupil’s knowledge • The problem is unique for every “teacher-pupil” pair

  4. Expert System • ES as a tool of Artificial Intelligence • Knowledge accumulation • IF-THEN rules

  5. Basic properties of ES • Accumulation and organization of knowledge • High-quality experience utilization • Knowledge representation in natural notation • Ability to train and learn • Ability to explain the decision

  6. Structure of Expert System User Knowledge Base Explanation Subsystem User Interface Knowledge Acquisition Subsystem Inference Engine Knowledge Engineer Workplace Expert

  7. Features of MCDM methods • Multi Criteria Decision Making – is techniques that used when there is a number of competitive factors that influence the final choice of the decision • The variety of the MCDM methods (MAUT, AHP, ELECTRE, VBA)

  8. Stages of the decision making process • Parametrical Identification: Define the possible decisions for the problem in question • Structural identification: Define the the number and content of the criteria

  9. Weighted Product Method • The following function has to be calculated:

  10. MCDM methods & ES • ES has following advantages compare to MCDM methods: pure expert’s thoughts; structure no needs to be well-formalized; no need to get to know complex algorithms. • Disadvantages of ES compare to MCDM methods: possible contradictions and uncertainties. • Combination of MCDM and EC overcome limitations of both

  11. Aggregation by the rule • Let us look on the decision making process as reasoning process • Example IF A1 & A2 THEN C1 • Now A1 and A2 accosiated with C1 • A rule is agregation process

  12. MCDM&ES Application • One possible combination of MCDM and ES is following: the ES works until the contradictions appear, the MCDM methods is applied to solve these contradictions.

  13. Examples of the tasks of the test • two ? 2 • 2 ? * * • two ? * *

  14. ES in context of NEURE project • NEURE computerized tool for diagnostics and theoretical understanding of cognitive functions under the desired perception, thought or behaviour • NEUREassess testing and evaluation knowledge and skills through computerized tasks

  15. Structure of NEURE project Expert UI Expert System DB Server Application Server Subject UI Teacher UI Task Editor Subject Management Task Explorer

  16. Current Results • The expert system is developed with Java platform • Psychological background for learning process is analyzed • Preliminary survey of MCDM methods is done • The structure of the MCDM+ES is developed

  17. Future direction of the work • Analysis of MCDM methods for neuropsychological diagnostics • Implementation of MCDM methods in the ES • Comparative study of the applied MCDM methods EUROOPAN UNIONI

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