References
- Anuja, J., Varsha, S., & Vivek, S. (2017). Big data mining using supervised machine learning approaches for Hadoop with Weka distribution. Computational Intelligence Research, 13(8), 2095–2111.
- Aulck, L., Velagapudi, N., Blumenstock, J., & West, J. (2016). Predicting student dropout in higher education. In Proceedings of ICML workshop on #Data4Good: Machine learning in social good applications (pp. 16–20). New York: Cornell University.
- Berens, J., Oster, S., Scheider, K., Görtz, S., & Burgoff, J. (2018). Early detection of students at risk – predicting student dropouts using administrative student data and machine learning methods (CESifo Working Paper Series 7259). Munich: CESifo Group.
- Chongsheng Z., Jingjun B., Paolo S. (2017). Feature selection and resampling in class imbalance learning: Which comes first? An empirical study in the biological domain. In IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 933–938).
- Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students’ academic failure in introductory programming courses. Computers in Human Behavior, 73, 247–256. doi: https://doi.org/10.1016/j.chb.2017.01.047
- Demsar, J. (2006). Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7, 1–30.
- Domingos, P. (1999). Metacost: A general method for making classifiers cost-sensitive. In KDD ‘99 Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 155–164). San Diego: ACM.
- Gitinabard, N., Khoshnevisan, F., Collin, F. L., & Wang, E. Y. (2018). Your actions or your associates? Predicting certification and dropout in MOOCs with behavioral and social features. In Proceedings of the 11th international conference on educational data mining (pp. 404–410). New York: University at Buffalo.
- Gontzis, A., Kotsiantis, S., Panagiotakopoulos, S., & Verykios, V. (2018). Measuring engagement to assess performance of students in distance learning. In Proceedings of 9th international conference on information, intelligence, systems and applications (pp. 1–7). Zakynthos: IEEE.
- Keerthi, S. S., Shevade, S. K., Bhattacharyya, C., & Murthy, K. R. K. (2001). Improvements to Platt's SMO algorithm for SVM classifier design. Neural Computation, 13(3), 637–649. doi: https://doi.org/10.1162/089976601300014493
- Kostopoulos, G., Kotsiantis, S., & Pintelas, P. (2015). Predicting student performance in distance higher education using semi-supervised techniques. In Proceedings of the 5th international conference on model and data engineering (pp. 259–270). Rhodes: Springer-Verlag.
- Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … Duchesnay, E. (2011). Scikit-learn: Machine learning in python. The Journal of Machine Learning Research, 12, 2825–2830.
- Quinlan, R. (1993). C4.5: Programs for machine learning. San Francisco, CA: Morgan Kaufmann Publishers Inc.
- Romero, C., & Ventura, S. (2016). Educational data science in massive open online courses. WIRES Data Mining and Knowledge Discovery, 7(1), 1–12.