Finnish-Russian Doctoral Seminar on Multicriteria Decision Aid and Optimization

Iryna YevseyevaNiilo Mäki Institute

University of Jyväskylä, Finlandiyevsev@cc.jyu.fi

Expert Classification of Expert Classification of Children’s Learning Abilities Children’s Learning Abilities

Utilizing Multicriteria AnalysisUtilizing Multicriteria Analysis

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

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

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

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/

Task Editor

Task Explorer

Subject UI

Teacher UI

DB Server

ApplicationServer

Subject Management

Expert System

Expert UI

Structure of NEURE project

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.

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

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.

Possible Tools for classification

Expert systems

Statistical models and Data Mining

Different AI technique: Neural Networks, Principal Component Analysis

MCDM analysis

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

Patterns of dissociation between operations predicted by the triple-code model of number processing (Cohen & Dehaene, 2000)Multiplication Addition Subtraction Commentary

– – 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– – Impossible pattern

Description of the patterns by the set IF-THEN rules in an Expert System

IF Problems in THEN Provide test Multiplication Impaired rote verbal memory

Subtraction Impaired quantity manipulations

Multiplication AND Addition

Impaired rote verbal memory AND reliance on rote memory for addition

Addition AND Subtraction Impaired 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

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

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.

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.

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 .

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

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

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