Measuring Students' Academic Performance through Educational Data Mining

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.

Comparison of Academic Performance for Management Accounting: Prior and During the Covid-19 Pandemic

“…There are various studies that examine students' performance from various perspectives. Some studies focus on the effectiveness of a change in teaching structure towards the performance of students in an introductory management accounting subject (Baird and Narayanan, 2010); while others look into the prediction of students' learning performances (Wong and Yip, 2020). This prediction enables early intervention being incorporated in the design of students activities to suit their skills and knowledge.…”

Application of Decision Tree Algorithm in Educational Data Mining

“…Y. ( 2020) [3] ,Wang, F., Zhao, C. (2018) [4] , Alaee, S. , Silberglitt, R. (2020) [5] , Patakamuri, R. D., & George, B. C. (2018) [6] demonstrated an approach for analyzing student performance using decision tree algorithm and prediction students' future performance by inputting basic information about the student and their historical performance, so as to help educators to develop student-specific educational programmes and to improve student performance and learning ability.…”