[1. Asif, R., A. Merceron, M. K. Pathan. Predicting Student Academic Performance at Degree Level: A Case Study. – International Journal of Intelligent Systems and Applications, Vol. 7, 2015, No 1, pp. 49-61.10.5815/ijisa.2015.01.05]Search in Google Scholar
[2. Chen, S. M., T. K. Li. Evaluating Students’ Learning Achievement by Automatically Generating the Importance Degrees of Attributes of Questions. – Expert Systems with Applications, Vol. 38, 2011, No 8, pp. 10614-10623.10.1016/j.eswa.2011.02.124]Search in Google Scholar
[3. Chen, J. F., H. N. Hsieh, Q. H. Do. Predicting Student Academic Performance: A Comparison of Two Meta-Heuristic Algorithms Inspired by Cuckoo Birds for Training Neural Networks. – Journal of Algorithms, Vol. 7, 2014, No 4, pp. 538-553.10.3390/a7040538]Search in Google Scholar
[4. Cortes, C., V. Vapnik. Support-Vector Networks. – Machine Learning, Vol. 20, 1995, No 3, pp. 273-297.10.1007/BF00994018]Search in Google Scholar
[5. Drachsler, H., K. Verbert, O. C. Santos, N. Manouselis. Panorama of Recommender Systems to Support Learning. Recommender Systems Handbook. Part III. New York, Springer, 2015, pp. 421-451.10.1007/978-1-4899-7637-6_12]Search in Google Scholar
[6. Garcia, E., C. Romero, S. Ventura, C. D. Castro. An Architecture for Making Recommendations to Courseware Authors Using Association Rule Mining and Collaborative Filtering. – In: User Modeling and User-Adapted Interaction. Vol. 19. No 1. Springer Netherlands Publisher, 2009, pp. 99-132.10.1007/s11257-008-9047-z]Search in Google Scholar
[7. Gray, G., C. McGuinness, P. Owende. An Application of Classification Models to Predict Learner Progression in Tertiary Education. – In: Advance IEEE International Computing Conference (IACC’14), 2014, pp. 549-554.10.1109/IAdCC.2014.6779384]Search in Google Scholar
[8. Golding, P., S. McNamarah. Predicting Academic Performance in the School of Computing and Information Technology (SCIT). – In: Proc. of 35th ASEE/IEEE Frontiers in Education Conference, S2H, 2005.]Search in Google Scholar
[9. Golding, P., O. Donaldson. Predicting Academic Performance. – In: Proc. of 36th Annual Conference in Frontiers in Education, 2006, pp. 21-26.10.1109/FIE.2006.322661]Search in Google Scholar
[10. Hilary, L. S. Studies in the History of Probability and Statistics. XV Historical Development of the Gauss Linear Model. – Journal of Biometrika, Vol. 54, 1967, No 1/2, pp. 1-24.10.1093/biomet/54.1-2.1]Search in Google Scholar
[11. Huang, S., N. Fang. Predicting Student Academic Performance in an Engineering Dynamics Course: A Comparison of Four Types of Predictive Mathematical Models. – Computers and Education, Vol. 61, 2013, pp. 133-145.10.1016/j.compedu.2012.08.015]Search in Google Scholar
[12. Kabakchieva, D. Predicting Student Performance by Using Data Mining Methods for Classification. – Cybernetics and Information Technologies, Vol. 13, 2013, No 1, pp. 61-72.10.2478/cait-2013-0006]Search in Google Scholar
[13. Mackay, D. J. C. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2012. 640 p.]Search in Google Scholar
[14. Manouselis, N., H. Drachsler, R. Vuorikari, H. Hummel, R. Koper. Recommender Systems in Technology Enhanced Learning. 1st Recommender Systems Handbook. Publisher, Berlin, Springer, 2010, pp. 387-415.10.1007/978-0-387-85820-3_12]Search in Google Scholar
[15. Mat, U. B., 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: Proc. of International IEEE Conference on Engineering Education (ICEED’13), 2013, pp. 126-130.]Search in Google Scholar
[16. Melville, P., V. Sindhwani. Recommender Systems. Encyclopaedia of Machine Learning Book. – New York, Springer, 2011, pp. 829-838.10.1007/978-0-387-30164-8_705]Search in Google Scholar
[17. Osmanbegovic, E., M. Suljic. Data Mining Approach for Predicting Student Performance. – Economic Review, Vol. 10, 2012, No 1, pp. 3-12.]Search in Google Scholar
[18. Peña-Ayala, A. Educational Data Mining: A Survey and a Data Mining-Based Analysis of Recent Works. – Expert Systems with Applications, Vol. 41, 2014, No 4, pp. 1432-1462.10.1016/j.eswa.2013.08.042]Search in Google Scholar
[19. Quinlan, J. R. Simplifying Decision Trees. – International Journal of Human-Computer Studies, Vol. 51, 1999, No 2, pp. 497-510.10.1006/ijhc.1987.0321]Search in Google Scholar
[20. Romero, C., S. Ventura. Educational Data Mining: A Survey from 1995 to 2005. – Expert Systems with Application, Vol. 33, 2007, No 1, pp. 135-146.10.1016/j.eswa.2006.04.005]Search in Google Scholar
[21. Romero, C., S. Ventura. Educational Data Mining: A Review of the State of the Art. – IEEE Transactions on Systems, Man and Cybernetics, Vol. 40, 2010, No 6, pp. 601-618.10.1109/TSMCC.2010.2053532]Search in Google Scholar
[22. Sen, B., E. Ucar, D. Delen. Predicting and Analyzing Secondary Education Placement Test Scores: A Data Mining Approach. – Expert Systems with Applications, Vol. 39, 2012, No 10, pp. 9468-9476.10.1016/j.eswa.2012.02.112]Search in Google Scholar
[23. Shahiri, A. M., W. Husain, N. A. Rashid. A Review on Predicting Students’ Performance Using Data Mining Techniques. – Procedia Computer Science, Vol. 72, 2015, pp. 414-422.10.1016/j.procs.2015.12.157]Search in Google Scholar
[24. Strecht, P., J. Mendes-Moreira, C. Soares. Merging Decision Trees: A Case Study in Predicting Student Performance. – In: Advanced Data Mining and Applications. Lecture Notes in Computer Science. Springer International Publishing, 2014, pp. 535-548.]Search in Google Scholar
[25. Strecht, P., L. Cruz, C. Soares, J. Mendes-Moreira, R. Abreu. A Comparative Study of Classification and Regression Algorithms for Modelling Students’ Academic Performance. – In: Proc. of 8th International Conference on Educational Data Mining (EDM’15), 2015, pp. 392-395.]Search in Google Scholar
[26. Thai-Nghe, N., P. Janecek, P. Haddawy. A Comparative Analysis of Techniques for Predicting Academic Performance. – In: Proc. of 37th Annual Frontiers in Education Conference – Global Engineering: Knowledge Without Borders, Opportunities Without Passports, 2007, pp. T2G-7–T2G-12.]Search in Google Scholar
[27. Thai-Nghe, N. Predicting Student Performance in an Intelligent Tutoring System. PhD Thesis at Hildesheim University, 2011.]Search in Google Scholar
[28. Thai-Nghe, N., T. Horvath. Personalized Forecasting Student Performance. – In: Proc. of 11th IEEE International Conference on Advanced Learning Technologies (ICALT’11), 2011, pp. 412-414.10.1109/ICALT.2011.130]Search in Google Scholar
[29. Toscher, A., M. Jahrer. Collaborative Filtering Applied to Educational Data Mining. KDD Cup 2010: Improving Cognitive Models with Educational Data Mining, 2010.]Search in Google Scholar
[30. Zimmermann, J., K. H. Brodersen, J.-P. Pellet, E. August, J. M. Buhmann. Predicting Graduate-Level Performance from Undergraduate Achievements. – In: Proc of 4th International Educational Data Mining Conference (EDM’11), 2011, pp. 357-358.]Search in Google Scholar