Finnish-Russian Doctoral Seminar on Multicriteria Decision Aid and Optimization Iryna Yevseyeva...
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Finnish-Russian Doctoral Seminar on Multicriteria Decision Aid and Optimization
Iryna YevseyevaNiilo Mäki Institute
University of Jyväskylä, [email protected]
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