1. Finnish-Russian Doctoral Seminar on Multicriteria Decision Aid and Optimization Iryna Yevseyeva Niilo Mäki Institute University of Jyväskylä, Finland iyevsev@cc.jyu.fi Expert Classification of Children’s Learning Abilities Utilizing Multicriteria Analysis

2. Main topics of presentation Introduction to the problem in question NEURE project Possible tools for solving problem Expert System (ES) and multicriteria decision making (MCDM) methods Conclusions and future research

3. Teaching process Teaching theoretical and practical parts of the problem area Testing of the knowledge on the control tasks Evaluation of the knowledge and skills Depict the results of testing and propose the future way of the teaching process Identification of the reasons of errors Yes No Errors

4. NEURE Project NEURE: computerized tool for diagnostics and theoretical understanding of cognitive functions through analysing of perception, thought or behaviour NEURE: provides testing and evaluation of knowledge and skills through computerized tasks

5. NEURE Problem Area The children of 5-12 years old are studied numerical calculations Particularly, simple arithmetic knowledge of numbers and operations such as addition, subtraction, multiplication, division NEURE http://www.nmi.jyu.fi:8080/neure/ http://www.nmi.jyu.fi:8080/neure/

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

7. Example of test 2 + 3 = 5 The child at age of five years  solves package of the tasks; The teacher  evaluates child’s knowledge;  defines if child has problem or not;  In case of existence of the problem defines the class of disability.

8. Purposes of the expert system To evaluate knowledge of the child  how the child solves battery of tasks  where he or she usually makes errors Defines the type of disability or absence of it based on the previous step

9. Ordinal Classification Consists in assigning a set of alternatives evaluated on the number of criteria to one of the ordinal class. Class can be predefined by  profile - vectors of possible values or intervals of values for each class;  central reference object in each class.

10. Possible Tools for classification Expert systems Statistical models and Data Mining Different AI technique: Neural Networks, Principal Component Analysis MCDM analysis

11. Expert System + MCDM ES rules accumulate personal experience of expert ES rules obtained with MCDM method decreases the number of questions posed to the expert Combination of MCDM methods and ES overcome limitation of the both

12. Patterns of dissociation between operations predicted by the triple-code model of number processing (Cohen & Dehaene, 2000) MultiplicationAdditionSubtractionCommentary  –– Impaired rote verbal memory ––  Impaired quantity manipulations  – Impaired rote verbal memory + reliance on rote memory for addition –  Impaired quantity manipulations + reliance on quantity manipulations for addition  Global acalculia  –  Impossible pattern –  –

13. Description of the patterns by the set IF-THEN rules in an Expert System IF Problems in THEN Provide test MultiplicationImpaired rote verbal memory SubtractionImpaired quantity manipulations Multiplication AND Addition Impaired rote verbal memory AND reliance on rote memory for addition Addition AND SubtractionImpaired quantity manipulations + reliance on quantity manipulations for addition Multiplication AND Addition AND Subtraction Global acalculia (Multiplication AND Subtraction) OR Addition ERROR in the set of facts in the working memory of ES: Impossible pattern

14. Outranking MCDM Approaches to Classification Trichotomic Segmentation (MoscarolaJ.,Roy B., 1977); ELECTRE Tri (Yu W.); CLASS group of methods (Larichev O.I. et al.) : M-CLASS, DIFCLASS, ORCLASS, CYCLE); Other methods

15. MCDM definitions in context of learning environment (1) Decision Maker: psychologist or special teacher; Classes: no any problem, has problem (classification according to the problem); Alternatives: children at different age; Criteria : age, level of education, difficulty of task, reaction time, strategy of obtaining solution, correctness of results; Decision Rules: are based on information elicited from Decision Maker about his or her preferences.

16. MCDM definitions in context of learning environment (2) Scale for age: 5-6, 7-8, 9-10, 11-12; Scale for difficulty of the task: the answer less than 10, the answer is more than 10; Scale for reaction time (in seconds): less then 3, 4-20, above 20; Scale for strategy of obtaining solution: “count on finger”, “minimal element”, “decomposition”, “recovery”; Scale for correctness of results: from 1 to 20.

17. MCDM Classification Cartesian production of criterion scales represents all possible alternatives; Complete classification means defining every alternative in one class; The best and the worst alternatives are defined in the first and the last classes consequently; The rest of classification is organized through dialog between expert and system.

18. Classification with ORCLASS method Classification is made during dialog with expert carried in natural language Construction of questionnaire with most “informative” question Checking of expert’s information for consistency Derivation of the Decision Rules for explanation of obtained decisions

19. Current Results Psychological background for learning process is analyzed and based on it, ES is developed Preliminary survey of MCDM methods for classification is done The structure of the ES+MCDM is developed

20. Future direction of the work Analysis of MCDM methods for classification tasks Implementation of MCDM methods in the neuropsychological diagnostics Comparative study of the applied MCDM methods EUROOPAN UNIONI