Analysis of effective machine learning techniques for improvement of student performance
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Date
2023
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Abstract
It is advantageous to use machine learning (ML) algorithms to assess student performance based on their prior performances and current behaviour because they can project both positive and negative outcomes at different educational levels. Learning outcomes can be improved by early performance prediction for students. The prediction of a student’s academic performance is important because it shapes changes in university academic policies, guides instructional strategies, evaluates the efficacy and efficiency of learning, provides teachers and students with pertinent feedback, and modifies learning environments. All of these elements support higher graduation rates. There is currently no clear winner among the various machine learning techniques for predicting student performance while enhancing learning outcomes. Therefore, this study is going to present the most effective machine learning techniques using and analysing open-source data from the Kaggle platform.
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machine learning algorithms, education, student’s academic performance, Kaggle platform