Please use this identifier to cite or link to this item:
Title: Using Markov Chains and Data Mining Techniques to Predict Students’ Academic Performance
Authors: Mallak, Saed
Kanan, Mohammad
Al-Ramahi, Nidal
Qedan, Aya
Khalilia, Hadi
Khassati, Ahmad
Wannan, Rania
Mara'beh, Mohammad
Alsadi, Samer
Keywords: Prediction;Markov Chains;Academic Performance;Data Mining;Educational Data Mining;Decision Tree
Issue Date: 1-Sep-2023
Publisher: Information Sciences Letters
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:
Series/Report no.: 12(9);2073-2083
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%.
Appears in Collections:Engineering and Technology Faculty

Files in This Item:
File Description SizeFormat 
Using Markov Chains and Data Mining Techniques to Predict Student.pdf644.63 kBAdobe PDFThumbnail

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.