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Published in:
2021 | OriginalPaper | Chapter
Predicting the Academic Performance of Undergraduate Computer Science Students Using Data Mining
Authors : Faiza Khan, Gary M. Weiss, Daniel D. Leeds
Published in: Advances in Software Engineering, Education, and e-Learning
Publisher: Springer International Publishing
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Abstract
There are myriad factors which can affect a student’s academic performance as measured by Grade Point Average (GPA). Identifying characteristics of students with high GPA can help more students understand how to achieve the best grades possible. In this paper, a variety of data mining algorithms are used to predict the GPA of undergraduate students majoring in Computer Science based on survey questions. The results demonstrate that the number of hours of sleep per night, the frequency of illicit drug use, the number of hours spent studying per week, and the number of hours spent on social media platforms per week are important factors that can be used to classify student GPA. The Random Forest data mining algorithm performed the best and was able to achieve a predictive accuracy of 95% when placing students into one of four academic performance groupings.