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Modern digital learning systems are becoming more student-centered than means of traditional learning due to the opportunities for monitoring and predicting student academic performance. As an experiment, the dependence of student academic performance on the number of test scores during the semester was analyzed. By means of correlation and regression analysis the general tendencies specific to the educational process were revealed. The main advantage of the obtained results is the opportunity to use them in predicting student learning outcomes. The closer the value of the coefficient of determination to one the higher the prediction accuracy. Thus, having calculated the linear regression and Pearson correlation coefficients for several tests and the final grade for the course, it is possible to choose the regression that can provide the most reliable prediction of students’ academic performance. #COMESYSO1120.

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Predicting Students’ Academic Performance Using Correlation and Regression Analysis

  • 会议论文

  • 发表日期：

    2025年

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  • Rukiya Deetjen-Ruiz,
  • Ivana Roncevic,
  • Roman Bandurin,
  • Ashot Gevorgyan,
  • Irina Nikolaeva,
  • Mareks Parfjonovs

摘要

Modern digital learning systems are becoming more student-centered than means of traditional learning due to the opportunities for monitoring and predicting student academic performance. As an experiment, the dependence of student academic performance on the number of test scores during the semester was analyzed. By means of correlation and regression analysis the general tendencies specific to the educational process were revealed. The main advantage of the obtained results is the opportunity to use them in predicting student learning outcomes. The closer the value of the coefficient of determination to one the higher the prediction accuracy. Thus, having calculated the linear regression and Pearson correlation coefficients for several tests and the final grade for the course, it is possible to choose the regression that can provide the most reliable prediction of students’ academic performance. #COMESYSO1120.