[1] C. Mi, Q. Deng, X. Peng, D. Yin,Y. Liu, “The Business Process Optimization of Early Warning Education in Colleges and Universities —Taking H College for Example (in Chinese),” Modern Educational Technology, Vol. 28, No. 3, pp. 92-98, 2018 [2] S. Zheng, “An Investigation and Analysis of College English Students' Learning Situation in Our College (in Chinese),”Journal of Huizhou University, No. 3, pp. 86-92, 1988 [3] L. Razzaq, J. Patvarczki, S. F. Almeida, M. Vartak, M. Feng, N. T. Heffernan, et al., “The Assistment Builder: Supporting the Life Cycle of Tutoring System Content Creation,” IEEE Transactions on Learning Technologies, Vol. 2, No. 2, pp. 157-166, 2009 [4] S. K. Yadav, B. Bharadwaj,S. Pal, “Data Mining Applications: A Comparative Study for Predicting Students' Performance,” International Journal of Innovative Technology & Creative Engineering, Vol. 1, No. 12, pp. 13-19, 2011 [5] T. M.Christian and M. Ayub, “Exploration of Classification using NB Tree for Predicting Students' Performance,” in Proceedings of the International Conference on Data and Software Engineering, pp. 1-6, Bandung, Indonesia, 2014 [6] C. Romero, M. I. López, J. M. Luna,S. Ventura, “Predicting Students' Final Performance from Participation in On-Line Discussion Forums,”Computers & Education, Vol. 68, pp. 458-472, 2013 [7] S. T. Jishan, R. I. Rashu, N. Haque,R. M. Rahman, “Improving Accuracy of Students' Final Grade Prediction Model using Optimal Equal Width Binning and Synthetic Minority over-Sampling Technique,” Decision Analytics, Vol. 2, No. 1, pp. 1-25, 2015 [8] U. B. Mat, N. Buniyamin, P. M. Arsad,R. Kassim, “An Overview of using Academic Analytics to Predict and Improve Students' Achievement: A Proposed Proactive Intelligent Intervention,” in Proceedings of the IEEE 5th International Conference on Engineering Education, pp. 126-130, Selangor, Malaysia, 2013 [9] B. B.Minaei and W. Punch, “Using Genetic Algorithms for Data Mining Optimization in an Educational Web-based System,” inProceedings of Genetic and Evolutionary Computational Conference, pp. 2252-2263, Chicago, Illinois, USA, 2003 [10] J. P. Campbell, “Utilizing Student Data within the Course Management System to Determine Undergraduate Student Academic Success: An Exploratory Study,” Doctoral Dissertation, pp. 31-61, Purdue University, 2007 [11] R. S. Baker, D. Lindrum, M. J. Lindrum,D. Perkowski, “Analyzing Early at-Risk Factors in Higher Education E-Learning Courses,” inProceedings of the 8th International Conference on Educational Data Mining, pp. 150-155, National University for Distance Education, Madrid, Spain, 2015 [12] 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,”Journal of Information Processing, Vol. 26, pp. 170-176, 2018 [13] J. Bravo, S. Sosnovsky,A. Ortigosa, “Detecting Symptoms of Low Performance using Prediction Rules,” inProceedings of the 2nd Educational Data Mining Conference, pp. 31-40, Universidad de Cordoba, Cordoba, Spain, 2009 [14] S. M. Jayaprakash, E. W. Moody, E. J. M.Lauría, J. R. Regan, and J. D. Baron, “Early Alert of Academically at-Risk Students: an Open Source Analytics Initiative,” Journal of Learning Analytics, Vol. 1, No. 1, pp. 6-47, 2014 [15] A. K. Hamoud, A. M. Humadi, W. A. Awadh,A. S. Hashim, “Students' Success Prediction based on Bayes Algorithms,” International Journal of Computer Applications, Vol. 178, No. 7, pp. 6-12, 2017 [16] C. Mi, X. Peng,Q. Deng, “An Artificial Neural Network Approach to Student Study Failure Risk Early Warning Prediction based on TensorFlow,”Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 219, pp. 326-333, 2018 [17] W. Xing, R. Guo, E. Petakovic,S. Goggins, “Participation-based Student Final Performance Prediction Model Through Interpretable Genetic Programming: Integrating Learning Analytics, Educational Data Mining and Theory,”Computers in Human Behavior, Vol. 47, pp. 168-181, 2015 [18] C. Mi, Q. Deng, J. Lin,X. Deng, “A Dynamic Early Warning Method of Student Study Failure Risk based on Fuzzy Synthetic Evaluation,” International Journal of Performability Engineering, Vol. 14, No. 4, pp. 639-646, 2018 |