Abstract:
Maintaining a high level of education is a key task in university management. Despite continuous monitoring of student performance, educational institution management fails to adequately utilize performance forecasting methods when shaping student learning paths. The proposed approach differs from existing ones in several aspects. Firstly, it analyzes features containing grades for various assignments completed by students on the e-learning platform, expanding the feature space by normalizing grades on a single scale and creating new features: an index and changes in performance for different types of assignments. Secondly, it identifies students at academic risk. Thirdly, it predicts exam scores for each student using a linear regression model. Fourthly, it groups students with similar learning trajectories for personalized consultations. The approach to predicting exam results for individual students demonstrates a commitment to providing comprehensive support beyond simple assessment. Through analysis, modeling, and personalized consultations, the research aims to proactively enhance academic performance in university settings.
Keywords:academic performance, statistical analysis, linear discriminant analysis, regression, learning trajectories, distant learning system
UDC:
65.012.4 + 681 BBK:
65.9
Received: March 27, 2024 Published: September 30, 2024
Fulltext:
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