Using Markov Chains and Data Mining Techniques to Predict Students’ Academic Performance

Authors

Saed Mallak, Electrical Engineering Department, Faculty of Engineering and Technology, Palestine Technical University, Kadoorie, Tulkarm, PalestineFollow
Mohammad Kanan, Industrial Engineering Department, Jeddah College of Engineering, University of Business and Technology, Jeddah 21448, Saudi ArabiaFollow
Nidal Al-Ramahi, Department of accounting, Business Faculty, Zarqa University, Zarqa 11831, JordanFollow
Aya Qedan, Electrical Engineering Department, Faculty of Engineering and Technology, Palestine Technical University, Kadoorie, Tulkarm, PalestineFollow
Hadi Khalilia, Electrical Engineering Department, Faculty of Engineering and Technology, Palestine Technical University, Kadoorie, Tulkarm, PalestineFollow
Ahmad KhassatiFollow

Abstract

In this study, the academic performance of students from the E-Commerce department at Palestine Technical University – Kadoorie is predicted using a Markov chains model and educational data mining. Based on the complete data regarding the achievements of the students from the 2016 cohort of students obtained from the university’s admissions and registration department, a Markov chain is built, in which the states are divided according to the semester average of the student, and the ratio of students in each state is calculated in the long run. The results obtained are compared with the data from the 2015 cohort, which demonstrates the efficiency of the Markov chains model. For educational data mining, the classification technique is applied, and the decision tree algorithm is used to predict the academic performance of the students, generalizing results with an accuracy of 41.67%.

Recommended Citation

Mallak, Saed; Kanan, Mohammad; Al-Ramahi, Nidal; Qedan, Aya; Khalilia, Hadi; and Khassati, Ahmad (2023) "Using Markov Chains and Data Mining Techniques to Predict Students’ Academic Performance," Information Sciences Letters: Vol. 12 : Iss. 9 , PP -.
Available at: https://digitalcommons.aaru.edu.jo/isl/vol12/iss9/15