Predicting Student Performance Using Educational Data Mining: A Review
PDF

Keywords

classification
educational data mining
student performance prediction
literature review
decision tree
academic achievement

How to Cite

Kumari, V., Meghji, A. F., Qadir, R., Gianchand, U., & Shaikh, F. B. (2024). Predicting Student Performance Using Educational Data Mining: A Review. KIET Journal of Computing and Information Sciences, 7(1). https://doi.org/10.51153/kjcis.v7i1.212

Abstract

Educational Data Mining (EDM) strategies facilitate the efficient and in-depth analysis of student data. EDM provides useful insights into comprehending student learning patterns and identifying factors that influence academic success. This review aims to evaluate the efficacy of classification algorithms popularly explored in EDM for predicting student performance and identifying common trends in existing EDM research. The review follows a systematic approach, relevant research articles have been cited following an inclusion and exclusion criteria to ensure the selection of studies that specifically address the use of EDM techniques for predicting student academic achievement. According to the review findings, most researchers have utilized the features of cumulative grade point average, internal and external assessment, and demographic information to predict student performance. The most common techniques in EDM for predicting students’ performance are Naïve Bayes and Decision Trees. The review also focuses on the potential for bias, key examination of challenges, and possible future directions in the field. In the context of student performance prediction, ethical considerations regarding privacy, data handling, and the interpretation of results are also identified

https://doi.org/10.51153/kjcis.v7i1.212
PDF

References

Alhazmi, E., & Sheneamer, A. (2023). Early predicting of students performance in higher education. IEEE Access, 11, 27579-27589.

Abu Saa, A., Al-Emran, M., & Shaalan, K. (2019). Factors affecting students’ performance in higher education: a systematic review of predictive data mining techniques. Technology, Knowledge and Learning, 24, 567-598.

Karthikeyan, K., & Kavipriya, P. (2017). On improving student performance prediction in education systems using enhanced data mining techniques. International Journal of Advanced Research in Computer Science and Software Engineering, 7(5).

Çetinkaya, A., Baykan, Ö. K., & K?rg?z, H. (2023). Analysis of Machine Learning Classification Approaches for Predicting Students’ Programming Aptitude. Sustainability, 15(17), 12917.

Daligcon, A. G., Priyadarshini, J., & Decena, L. R. (2024). Unveiling the Best-fit Model: A Comparative Analysis of Classification Methods in Predicting Student Success. International Journal of Information Technology, Research and Applications, 3(1), 12-19.

Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students' performance using educational data mining. Computers & education, 113, 177-194.

Bujang, S. D. A., Selamat, A., Ibrahim, R., Krejcar, O., Herrera-Viedma, E., Fujita, H., & Ghani, N. A. M. (2021). Multiclass prediction model for student grade prediction using machine learning. IEEE Access, 9, 95608-95621.

Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering–a systematic literature review. Information and software technology, 51(1), 7-15.

Sahlaoui, H., Nayyar, A., Agoujil, S., & Jaber, M. M. (2021). Predicting and interpreting student performance using ensemble models and shapley additive explanations. IEEE Access, 9, 152688-152703.

Mengash, H. A. (2020). Using data mining techniques to predict student performance to support decision making in university admission systems. IEEE Access, 8, 55462-55470.

Mehboob, B., Liaqat, R. M., & Saqib, N. A. (2016). Predicting student performance and risk analysis by using data mining approach. International Journal of Computer Science and Information Security (IJCSIS), 14(7), 69-76.

Ashraf, A., Anwer, S., & Khan, M. G. (2018). A Comparative study of predicting student’s performance by use of data mining techniques. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS), 44(1), 122-136.

Shruthi, P., & Chaitra, B. P. (2016). Student performance prediction in education sector using data mining. International Journal of Advanced Research in Computer Science and Software Engineering, 6(3), 212–218.

Jishan, S. T., Rashu, R. I., Haque, N., & Rahman, R. M. (2015). Improving accuracy of students’ final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique. Decision Analytics, 2, 1-25.

Pavithra, A., & Dhanaraj, S. (2018). Prediction Accuracy on Academic Performance of Students Using Different Data Mining Algorithms with Influencing Factors. International Journal of Scientific Research & Management Studies, 7(5).

Rafique, A., Khan, M. S., Jamal, M. H., Tasadduq, M., Rustam, F., Lee, E., ... & Ashraf, I. (2021). Integrating learning analytics and collaborative learning for improving student’s academic performance. IEEE Access, 9, 167812-167826.

Durairaj, M., & Vijitha, C. (2014). Educational data mining for prediction of student performance using clustering algorithms. International Journal of Computer Science and Information Technologies, 5(4), 5987-5991.

Khudhur, M. E., Ahmed, M. S., & Maher, S. M. (2021). Prediction of the Academic Achievement of Pupils Using Data Mining Techniques. Webology, 18(2), 1355-1364.

Ahmad, F., Ismail, N. H., & Aziz, A. A. (2015). The prediction of students’ academic performance using classification data mining techniques. Applied mathematical sciences, 9(129), 6415-6426.

Zohair, A., & Mahmoud, L. (2019). Prediction of Student’s performance by modelling small dataset size. International Journal of Educational Technology in Higher Education, 16(1), 1-18.

Mueen, A., Zafar, B., & Manzoor, U. (2016). Modeling and predicting students' academic performance using data mining techniques. International Journal of Modern Education and Computer Science, 8(11), 36.

Wong, J., Khalil, M., Baars, M., de Koning, B. B., & Paas, F. (2019). Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course. Computers & Education, 140, 103595.

Ya?c?, M. (2022). Educational data mining: prediction of students' academic performance using machine learning algorithms. Smart Learning Environments, 9(1), 11.

Meghji, A. F., Mahoto, N. A., Asiri, Y., Alshahrani, H., Sulaiman, A., & Shaikh, A. (2023). Early detection of student degree-level academic performance using educational data mining. PeerJ Computer Science, 9, e1294.

Osmanbegovi?, E., Sulji?, M., & Agi?, H. (2014). Determining dominant factor for students performance prediction by using data mining classification algorithms. Tranzicija, 16(34), 147-158.

Altujjar, Y., Altamimi, W., Al-Turaiki, I., & Al-Razgan, M. (2016). Predicting critical courses affecting students performance: a case study. Procedia Computer Science, 82, 65-71.

Mousa, H., & Maghari, A. (2017). School student’s performance prediction using data mining classification. International Journal of Advanced Research in Computer and Communication Engineering, 6(8), 136-141.

Singh, W., & Kaur, P. (2016). Comparative Analysis of Classification Techniques for Predicting Computer Engineering Students' Academic Performance. International Journal of Advanced Research in Computer Science, 7(6).

Pallathadka, H., Wenda, A., Ramirez-Asís, E., Asís-López, M., Flores-Albornoz, J., & Phasinam, K. (2023). Classification and prediction of student performance data using various machine learning algorithms. Materials today: proceedings, 80, 3782-3785.