ARCHIVES

1. Yukselturk, E., Ozekes, S., & Turel, Y. K. (2014). Predicting dropout student: An application of data mining methods in an online
education program. European Journal of Open, Distance and E-Learning., 17(1), 118–133.
2. Hu, Y.-H., Lo, C.-L., & Shih, S.-P. (2014). Developing early warning systems to predict students’ online learning
performance. Computers in Human Behavior, 36, 469–478.
3. Jia, P., & Maloney, T. (2015). Using predictive modelling to identify students at risk of poor university outcomes. Higher
Education, 70(1), 127–149.
4. Chun-Teck, L. (2010). Predicting preuniversity students’ mathematics achievement (published conference proceedings style).
In: International conference on mathematics education research, multimedia university, Malaysia (pp. 299–306).
5. Adhatrao, K., Gaykar, A., Dhawan, A., Jha, R., & Honrao, V. (2013). Predicting students’ performance using ID3 and C4.5 classification
algorithms. arXiv Preprint
6. 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.
7. Sales, A., Balby, L., & Cajueiro, A. (2016). Exploiting academic records for predicting student drop out: A case study in Brazilian higher
education. Journal of Data, Information and Management, 7(2), 166.
8. Kitchenham, B., & Charters, S. (2007). Guidelines for performing Systematic Literature reviews in SoftwareEngineering Version
2.3. Engineering, 45(4), 1051.
9. Okoli, C., & Schabram, K. (2012). A Guide to Conducting a Systematic Literature Review of Information Systems Research. SSRN
Electron J [Internet].. https://doi.org/10.2139/ssrn.1954824.
10. Moola, S. (2017). Checklist for analytical cross sectional studies. Joanna Briggs Institute Rev Man. (pp. 1–7)