Analyzing Student Performance in Programming Education Using Classification Techniques

Kissinger Sunday, Patrick Ocheja, Sadiq Hussain, Solomon Sunday Oyelere, Balogun Oluwafemi Samson, Friday Joseph Agbo

Abstract


In this research, we aggregated students log data such as Class Test Score (CTS), Assignment Completed (ASC), Class Lab Work (CLW) and Class Attendance (CATT) from the Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria. Similarly, we employed data mining techniques such as ID3 & J48 Decision Tree Algorithms to analyze these data. We compared these algorithms on 239 classification instances. The experimental results show that the J48 algorithm has higher accuracy in the classification task compared to the ID3 algorithm. The important feature attributes such as Information Gain and Gain Ratio feature evaluators were also compared. Both the methods applied were able to rank search method and the experimental results confirmed that the two methods derived the same set of attributes with a slight deviation in the ranking. From the results analyzed, we discovered that 67.36 percent failed the course titled Introduction to Computer Programming, while 32.64 percent passed the course. Since the CATT has the highest gain value from our analysis; we concluded that it is largely responsible for the success or failure of the students.

Keywords


Learning Analytics; Educational data mining; Programming education; Classification algorithm.

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Copyright (c) 2020 Kissinger Sunday, Solomon Sunday Oyelere, Sadiq Hussain, Patrick Ocheja


International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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