Evaluation the effectiveness of various machine learning methods in predicting student academic performance
Abstract
The article examines the possibilities of using machine learning to predict student performance in the context of a modern educational environment with evergrowing volumes of data. Key aspects of evaluating the effectiveness of various machine learning algorithms are analyzed. Attention is focused on the importance of adequate metrics, strict validation of models, and consideration of ethical considerations when implementing forecasting technologies.
About the Authors
V. V. Chernykh
Federal State Budgetary Educational Institution of Higher Education "Lugansk State University named after Vladimir Dahl"
Russian Federation
Russian Federation
Candidate of Technical Sciences
A. V. Balalaechnikov
Federal State Budgetary Educational Institution of Higher Education "Lugansk State University named after Vladimir Dahl"
Russian Federation
Russian Federation
Senior Lecturer
References
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Review
For citations:
Chernykh V.V., Balalaechnikov A.V. Evaluation the effectiveness of various machine learning methods in predicting student academic performance. Social-economic and technical systems: research, design and optimization. 2025;(3):231-241. (In Russ.)
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