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Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic

Електронна бібліотека НАПН України

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Relation http://lib.iitta.gov.ua/726932/
http://ceur-ws.org/Vol-2879
 
Title Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic
Моделювання інтелектуальної системи оцінювання багаторівневих тестових завдань на основі нечіткої логіки
 
Creator Tsidylo, I.M.
Semerikov, Serhiy O.
Gargula, T. I.
Solonetska, H. V.
Zamora, Y. P.
Pikilnyak, A.V.
 
Subject 004.94 Simulation
378 Higher education. Universities. Academic study
 
Description The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given.
The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the
natural language. This closeness allows for closer interaction with a subject area expert and build
well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of
the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure
and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be
assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as
“poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures
the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.
The article describes the stages of modeling an intelligent system for evaluating multilevel test tasks based on fuzzy logic in the MATLAB application package, namely the Fuzzy Logic Toolbox. The analysis of existing approaches to fuzzy assessment of test methods, their advantages and disadvantages is given.
The considered methods for assessing students are presented in the general case by two methods: using fuzzy sets and corresponding membership functions; fuzzy estimation method and generalized fuzzy estimation method. In the present work, the Sugeno production model is used as the closest to the
natural language. This closeness allows for closer interaction with a subject area expert and build
well-understood, easily interpreted inference systems. The structure of a fuzzy system, functions and mechanisms of model building are described. The system is presented in the form of a block diagram of fuzzy logical nodes and consists of four input variables, corresponding to the levels of knowledge assimilation and one initial one. The surface of the response of a fuzzy system reflects the dependence of
the final grade on the level of difficulty of the task and the degree of correctness of the task. The structure
and functions of the fuzzy system are indicated. The modeled in this way intelligent system for assessing multilevel test tasks based on fuzzy logic makes it possible to take into account the fuzzy characteristics of the test: the level of difficulty of the task, which can be assessed as “easy”, “average", “above average”, “difficult”; the degree of correctness of the task, which can be assessed as “correct”, “partially correct”, “rather correct”, “incorrect”; time allotted for the execution of a test task or test, which can be assessed as “short”, “medium”, “long”, “very long”; the percentage of correctly completed tasks, which can be
assessed as “small”, “medium”, “large”, “very large”; the final mark for the test, which can be assessed as
“poor”, “satisfactory”, “good”, “excellent”, which are included in the assessment. This approach ensures
the maximum consideration of answers to questions of all levels of complexity by formulating a base of inference rules and selection of weighting coefficients when deriving the final estimate. The robustness of the system is achieved by using Gaussian membership functions. The testing of the controller on the test sample brings the functional suitability of the developed model.
 
Publisher CEUR Workshop Proceedings
 
Date 2021
 
Type Article
PeerReviewed
 
Format text
 
Language uk
 
Identifier http://lib.iitta.gov.ua/726932/1/paper30.pdf
- Tsidylo, I.M. (orcid.org/0000-0002-0202-348X <http://orcid.org/0000-0002-0202-348X>), Semerikov, Serhiy O. (orcid.org/0000-0003-0789-0272 <http://orcid.org/0000-0003-0789-0272>), Gargula, T. I. (orcid.org/0000-0003-3335-0501 <http://orcid.org/0000-0003-3335-0501>), Solonetska, H. V. (orcid.org/0000-0002-2527-8653 <http://orcid.org/0000-0002-2527-8653>), Zamora, Y. P. (orcid.org/0000-0001-6470-8233 <http://orcid.org/0000-0001-6470-8233>) and Pikilnyak, A.V. (orcid.org/0000-0003-0898-4756 <http://orcid.org/0000-0003-0898-4756>) (2021) Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic Proceedings of the 8th Workshop on Cloud Technologies in Education (CTE 2020) Kryvyi Rih, Ukraine, December 18, 2020 (2879). pp. 507-520. ISSN 1613-0073