Atif, A., Richards, D., Bilgin, A., & Marrone, M. (2013). Learning analytics in higher education: A summary of tools and approaches. 30th Australasian Society for Computers in Learning in Tertiary Education Conference, Sydney.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324.
Campbell, J., DeBlois, P., & Oblinger, D. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 40-57.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794). ACM. https://doi.org/10.1145/2939672.2939785.
Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17-26.
Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and implementation of a learning analytics toolkit for teachers. Educational Technology & Society, 15(3), 58-76.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189-1232. https://doi.org/10.1214/aos/1013203451.
Guido, R., Ferrisi, S., Lofaro, D., & Conforti, D. (2024). An overview on the advancements of support vector machine models in healthcare applications: A review. Information, 15(4), 235. https://doi.org/10.3390/info1504023.
Hamim, T., Benabbou, F., & Sael, N. (2022). Student profile modeling using boosting algorithms. International Journal of Web-Based Learning and Teaching Technologies, 17(5), 1-13. https://doi.org/10.4018/IJWLTT.293281.
Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). New York: Springer. https://doi.org/10.1007/978-0-387-84858-7.
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and Their Applications, 13(4), 18-28. https://doi.org/10.1109/5254.708428.
Kalofolias, V. (2016). How to learn a graph from smooth signals. Journal of Machine Learning Research, 17(1), 1-21.
Kalofolias, V., & Perraudin, N. (2019). Large scale graph learning from smooth signals. International Conference on Learning Representations.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In I. Guyon, U. von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 30) .
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (Vol. 30) .
Miguéis, V. L., Freitas, A., Garcia, P. J. V., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36-51. https://doi.org/10.1016/j.dss.2018.09.001.
Mingyu, Z., Sutong, W., Yanzhang, W., & Dujuan, W. (2021). An interpretable prediction method for university student academic crisis warning. Complex & Intelligent Systems, 8(1), 323-336. https://doi.org/10.1007/s40747-021-00566-7.
Ortega, A. (2022). Introduction to graph signal processing. Cambridge University Press. https://doi.org/10.1017/9781108889862.
Parhizkar, R. (2013). Euclidean distance matrices: Properties, algorithms, and applications (PhD thesis). EPFL, Lausanne.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. In Advances in Neural Information Processing Systems (Vol. 31) .
Rish, I. (2001). Empirical study of the naive Bayes classifier. In IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence (Vol. 3, No. 22, pp. 41-46) .
Sandryhaila, A., & Moura, J. M. F. (2013). Discrete signal processing on graphs. IEEE Transactions on Signal Processing, 61(7), 1644-1656. https://doi.org/10.1109/TSP.2013.2238935.
Sandryhaila, A., & Moura, J. M. F. (2014). Big data analysis with signal processing on graphs: Representation and processing of massive data sets with irregular structure. IEEE Signal Processing Magazine, 31(5), 80-90. https://doi.org/10.1109/MSP.2014.2329213.
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications, and research directions. SN Computer Science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x.
Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A., & Vandergheynst, P. (2013). The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine, 30(3), 83-98. https://doi.org/10.1109/MSP.2012.2235192.
Smith, V. C., Lange, A., & Huston, D. R. (2012). Predictive modeling to forecast student outcomes and drive effective interventions in online community college courses. Journal of Asynchronous Learning Networks, 16(3), 51-61.
Song, Y. Y., & Lu, Y. (2015). Decision tree methods: Applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130-135. https://doi.org/10.11919/j.issn.1002-0829.215044.
Sperandei, S. (2014). Understanding logistic regression analysis. Biochemia Medica (Zagreb), 24(1), 12-18. https://doi.org/10.11613/BM.2014.003.
van Barneveld, A., Arnold, K. E., & Campbell, J. P. (2012). Analytics in higher education: Establishing a common language. EDUCAUSE Learning Initiative, 1, 1-11.