Datalogic Relation Model for Automated Evaluating the Semantic Integrity of Test Tasks Sets by Machine Learning Means

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dc.contributor.authorМазурець, Олександр
dc.contributor.authorHardysh, D.
dc.contributor.authorMazurets, O.
dc.contributor.authorTyschenko, O.
dc.date.accessioned2025-02-11T07:52:26Z
dc.date.available2025-02-11T07:52:26Z
dc.date.issued2024
dc.description.abstractDatalogic relation model for automated evaluating the semantic integrity of test tasks sets was designed. The developed intelligent system has significant potential for use in educational institutions and organizations where assessing the alignment of test tasks with educational materials is critical. By automating this evaluation process, the system ensures objectivity and reliability in the results.
dc.identifier.citationHardysh D., Mazurets O., Tyschenko O. Datalogic Relation Model for Automated Evaluating the Semantic Integrity of Test Tasks Sets by Machine Learning Means. Innovative Solutions in Science: Balancing Theory and Practice. Proceedings 2nd International Conference. December 23-25, 2024. San Francisco, USA. 2024. Pp. 114-125.
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/18075
dc.language.isoen
dc.titleDatalogic Relation Model for Automated Evaluating the Semantic Integrity of Test Tasks Sets by Machine Learning Means
dc.typeСтаття
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