Analyzing Student Performance in Programming Education Using Classification Techniques

Authors

  • Kissinger Sunday Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University Sokoto, Sokoto, Nigeria
  • Patrick Ocheja Graduate School of Informatics, Kyoto University Kyoto, Japan
  • Sadiq Hussain Dibrugarh University, Dibrugarh, India
  • Solomon Sunday Oyelere School of Computing, University of Eastern Finland, Joensuu, Finland
  • Balogun Oluwafemi Samson School of Computing, University of Eastern Finland, Kuopio, Finland
  • Friday Joseph Agbo School of Computing University of Eastern Finland Joensuu, Finland

DOI:

https://doi.org/10.3991/ijet.v15i02.11527

Keywords:

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

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.

Author Biographies

Kissinger Sunday, Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University Sokoto, Sokoto, Nigeria

Kissinger Sunday is a lecturer with the Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria. His research interest focuses mainly on computing education, machine learning and educational data mining. He has several research publications.

Patrick Ocheja, Graduate School of Informatics, Kyoto University Kyoto, Japan

Patrick Ocheja is a Ph.D. student at Kyoto University. His research is focused on connecting lifelong learning using blockchain technology.

Sadiq Hussain, Dibrugarh University, Dibrugarh, India

SADIQ HUSSAIN is System Administrator at Dibrugarh University, Assam, India. He received his PhD. degree from Dibrugarh University, India. His research interest includes data mining and machine learning. He is associated with Computerization Examination System and Management Information System of Dibrugarh University.

Solomon Sunday Oyelere, School of Computing, University of Eastern Finland, Joensuu, Finland

Solomon Sunday Oyelere is a postdoctoral researcher at the University of Eastern Finland. His research interest focus mainly on improving learning environments through smart technology, pedagogy and content. He applies techniques from multidisciplinary areas including educational technology, interactive mobile computing, engineering, psychology, computing education, and educational data mining. He has several research publications.

Balogun Oluwafemi Samson, School of Computing, University of Eastern Finland, Kuopio, Finland

Balogun Oluwafemi Samson is a Postdoctoral researcher in Data Science at University of Eastern Finland. His research interest includes categorical data analysis, biostatistics and modeling.

Friday Joseph Agbo, School of Computing University of Eastern Finland Joensuu, Finland

Friday Joseph Agbo is a doctoral student in the School of Computing, University of Eastern Finland, Joensuu Finland. He obtained his M.Sc (Computer Science) from the University of Ilorin and B.Sc. (Computer Science) from the University of Jos, Nigeria. Agbo has both teaching experience at the university and industrial experience as a software developer & system engineer. His recent research interests include smart learning, technology-enhanced learning, computational thinking, and programming education. He has published several research papers in international/national journals and conferences. He is a member of International Association of Smart Learning Environment (IASLE)

Downloads

Published

2020-01-29

How to Cite

Sunday, K., Ocheja, P., Hussain, S., Oyelere, S. S., Samson, B. O., & Agbo, F. J. (2020). Analyzing Student Performance in Programming Education Using Classification Techniques. International Journal of Emerging Technologies in Learning (iJET), 15(02), pp. 127–144. https://doi.org/10.3991/ijet.v15i02.11527

Issue

Vol. 15 No. 02 (2020)

Section

Papers

License

The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.