Methods of automation and interpretation of the quality educational tests in distributed virtual learning systems

Authors

DOI:

https://doi.org/10.30837/ITSSI.2023.25.040

Keywords:

software engineering; knowledge bases; algebra of finite predicates; use of knowledge; rules

Abstract

The subject matter of the article is the development of mathematical and algorithmic support for intellectual tools, which allows you to conduct continuous control of the knowledge of students (subjects of study) objectively and comprehensively. The goal of the work is to create methods for assessing the quality of educational tests and automating such processes. The following tasks were solved in the article: formation of a testing model in a distributed virtual learning environment and a validity assessment model based on the content of sets of test tasks. The following methods used are – algebra of finite predicates and operations, methods of mathematical statistics and methods of intellectual data analysis. The following results were obtained – the principles of intellectual analysis of the value of reliability coefficients, validity coefficients, discriminability coefficient, the difficulty index of the task of assessing the knowledge of subjects of training are formulated. Conclusions: the application of methods of formalization of test evaluation, analysis of software requirements, software development confirm the need to introduce quantitative methods of assessing students' knowledge into educational practice. The introduction of quantitative methods involves the correct setting of control goals, the selection of the measurement object and the selection of measurement tools. The use of pedagogical tests contributes to the effective implementation of all control functions and corresponds to its main principles for solving the problem of assessing the quality of tests, based on distributed virtual learning models and analysis methods. A test example of calculations was developed, with the help of which the efficiency of the proposed methods was researched.

Author Biographies

Ihor Shubin, Kharkiv National University of Radio Electronics

PhD (Engineering Sciences), Associate Professor, Professor at the Software Department

Volodymyr Liashyk, Kharkiv National University of Radio Electronics

Postgraduate

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Shubin, І. "Development of conjunctive decomposition tools". CEUR Workshop Proceedings, 2021. Р. 890–900. available at: https://ceur-ws.org/Vol-2870/

Karataiev, O., Sitnikov, D., Sharonova, N. "A Method for Investigating Links between Discrete Data Features in Knowledge Bases in the Form of Predicate Equations", CEUR Workshop Proceedings, 2023, Р. 224–235. available at: https://ceur-ws.org/Vol-3387/paper17.pdf

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Williams, P. (2005), "E-learning: what the literature tells us about distance education". An overview. Aslib Proceedings. Vol. 57. P 109–122. DOI: https://doi.org/10.1108/00012530510589083

Omran, P. G., Wang, K., Wang, Z. (2021), "An Embedding-based Approach to Rule Learning in Knowledge Graphs", IEEE Transactions on Knowledge and Data Engineering. Vol. 33(4). Р. 1348–1359. DOI: 10.1109/TKDE.2019.2941685

Pellissier-Tanon, T., Weikum, G., Suchanek, F. (2020), "F. YAGO 4: A Reasonable Knowledge Base", 17th International Conference, ESWC 2020, Heraklion, Crete, Greece, May 31–June 4. P. 583–596. DOI:10.1007/978-3-030-49461-2_34

Kyrychenko, I., Malikin, D. "Research of Methods for Practical Educational Tasks Generation Based on Various Difficulty Levels" 6th International Conference on Computational Linguistics and Intelligent Systems (COLINS-2022), May 12–13, 2022, Gliwice, Poland. CEUR Workshop Proceedings 3171, Volume I: Main, Р. 1030–1042. available at: https://ceur-ws.org/Vol-3171/paper74.pdf

Omran, P. G., Wang, Z., Wang, K. (2018), "Scalable rule learning via learning representation", Twenty-Seventh International Joint Conference on Artificial Intelligence. IJCAI-18. Р. 2149-2155. DOI:10.24963/ijcai.2018/297

Svato, M., Schockaert, S., Davis, J. "STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment", in: ECAI, 2020. available at: https://ida.fel.cvut.cz/~kuzelka/pubs/ecai2020.pdf

Sharonova, N., Gruzdo, I., Tereshchenko, G. "Generalized Semantic Analysis Algorithm of Natural Language Texts for Various Functional Style Types". 6th International Conference on Computational Linguistics and Intelligent Systems (COLINS-2022), May 12–13, 2022, Gliwice, Poland. CEUR Workshop Proceedings 3171, Volume I: Main, Р. 16–26. available at: https://ceur-ws.org/Vol-3171/paper4.pdf

Barkovska, O. (2022), Research into Speech-to-text Transformation Module in the Proposed Model of a Speaker’s Automatic Speech Annotation. Innovative Technologies and Scientific Solutions for Industries. № 4 (22). Р. 5–13. DOI: https://doi.org/10.30837/ITSSI.2022.22.005

Published

2023-09-30

How to Cite

Shubin, I., & Liashyk, V. (2023). Methods of automation and interpretation of the quality educational tests in distributed virtual learning systems. INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, (3(25), 40–51. https://doi.org/10.30837/ITSSI.2023.25.040