Abstract—Based on a mix of real world data and a simulated dataset for predicting the students’ academic performance, we study/compare various decision tree (DT) based algorithms (which include ID3, C4.5 and CART) with different choices of information entropy metrics (which include Shannon, Quadratic, Havrda and Charvát, Rényi, Taneja, Trigonometric and R-norm entropies) to build a decision tree in order to provide appropriate counseling/advise at an earlier stage. DT is one such important technique in educational data mining (EDM) which creates hierarchical structures of classification rules “If ⋯, Then ⋯” building a tree structure by incrementally breaking down the datasets in smaller subsets. The results suggest that basic training of the students has no significant predictive power on performance, while information about their abilities, diligence, motivation and activity in the learning process can predict their grades. As such, the resulting forecasts can be used by the instructor in optimizing the learning process and designing the course content and schedule.
Index Terms—Decision tree algorithms, educational data mining, entropy metrics and students’ academic performance.
Jeff Chak Fu Wong and Tony Chun Yin Yip are with Department of Mathematics, the Chinese University of Hong Kong, Hong Kong (e-mail: firstname.lastname@example.org, email@example.com).
Cite:Jeff Chak Fu Wong and Tony Chun Yin Yip, "Measuring Students' Academic Performance through Educational Data Mining," International Journal of Information and Education Technology vol. 10, no. 11, pp. 797-804, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).