M.J. Sandeep, W.M. Erik, J.M.L. Eitel, J.R. Regan, and J.D. Baron, "Early alert of academically at-risk students: an open source analytics initiative", J. Learn. Anal., vol. 1, pp. 6-47, 2014.
B.B. Minaei, and W. Punch, "Using genetic algorithms for data mining optimization in an educational web-based system In ", Proceedings of Genetic and Evolutionary Computational Conference Chicago, Illinois, USA, 2003, pp. 2252-2263.
L.V. Morris, S. Wu, and C. Finnegan, "Predicting retention in online general education courses", Am. J. Dist. Edu., vol. 19, pp. 23-36, 2005.
J.P. Campbell, Utilizing student data within the course management system to determine undergraduate student academic success: an exploratory study. Doctoral dissertation, Purdue University, pp.
L.P. Macfadyen, and S. Dawson, "Mining LMS data to develop an early warning system for educators: a proof of concept", Compu. Edu., vol. 54, pp. 588-599, 2010.
R.S. Baker, D. Lindrum, M.J. Lindrum, and D. Perkowski, "Analyzing early at-risk factors in higher education e-learning courses In ", Proceedings of the 8th International Conference on Educational Data Mining, 2015pp. 150-155
B. Jay, M. James, Z. Anne, L. Eitel, M.J. Sandeep, and B. Josh, "Using learning analytics to predict at-risk students in online graduate public affairs and administration education", J. Public Aff. Educ., vol. 21, pp. 247-262, 2015.
S.J.H. Yang, O.H.T. Lu, A.Y.Q. Huang, J.C.H. Huang, H. Ogata, and A.J.Q. Lin, "Predicting students’ academic performance using multiple linear regression and principal component analysis", J. Inform. Process., vol. 26, pp. 170-176, 2018.
G. Chen, C. Liu, K. Ou, and B.J. Liu, "Discovering decision knowledge from web log portfolio for managing classroom processes by applying decision tree and data cube technology", J. Educ. Comput. Res., vol. 23, pp. 305-332, 2000.
Y. Ma, B. Liu, C.K. Wong, P.S. Yu, and S.M. Lee, "Targeting the right students using data mining In ", Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining Boston, Massachusetts, USA pp. 457-464, 2000.
J. Bravo, S. Sosnovsky, and A. Ortigosa, "Detecting symptoms of low performance using prediction rules In ", Proceedings of the 2nd Educational Data Mining Conference Universidad de Cordoba, Cordoba, Spain,, 2009, pp. 31-40.
S. Mack, L. Jaime, R. Huzefa, and J. Aditya, "Next-term student performance prediction: a recommender systems approach", Edu. Data Mining, vol. 8, pp. 22-51, 2016.
C. Mollica, and L. Petrella, "Bayesian binary quantile regression for the analysis of bachelor-to-master transition", J. Appl. Stat., vol. 44, pp. 2791-2812, 2017.
A.K. Hamoud, A.M. Humadi, W.A. Awadh, and A.S. Hashim, "Students’ success prediction based on bayes algorithms", Int. J. Comput. Appl., vol. 178, pp. 6-12, 2017.
C. Geng, L. Dandan, W. Haowen, and W. Guochang, "Analysis and prediction model of financial income in guangzhou", Stat. Appl., vol. 4, pp. 187-195, 2015.