|
[1] A., S., Vinodhini, G., and Chandrasekaran, R. M. (2018). Predicting students’ academic performance in the university using meta decision tree classifiers. J. Comput. Sci., 14(5):654–662. [2] Arnold, K. E. and Pistilli, M. D. (2012). Course signals at purdue: using learning analytics to increase student success. In Dawson, S., Haythornthwaite, C., Shum, S. B.,Gasevic, D., and Ferguson, R., editors, Second International Conference on Learning Analytics and Knowledge, LAK 2012, Vancouver, BC, Canada, April 29 - May 02, 2012, pages 267–270. ACM. [3] Asif, R., Merceron, A., Ali, S. A., and Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Comput. Educ., 113:177–194. [4] Baradwaj, B. and Pal, S. (2011). Mining educational data to analyze students’ performance. International Journal of Advanced Computer Science and Applications, 2:63–69. [5] Coulom, R. (2006). Efficient selectivity and backup operators in monte-carlo tree search. In van den Herik, H. J., Ciancarini, P., and Donkers, H. H. L. M., editors, Computers and Games, 5th International Conference, CG 2006, Turin, Italy, May 29-31, 2006. Revised Papers, volume 4630 of Lecture Notes in Computer Science, pages 72–83. Springer. [6] Dittenbach, M., Merkl, D., and Rauber, A. (2000). The growing hierarchical self-organizing map. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, volume 6, pages 15–19. IEEE. [7] Esteban, C., Hyland, S., and Rätsch, G. (2017). Real-valued (medical) time series generation with recurrent conditional gans. [8] Fairos, W., Wan Yaacob, W. F., Azlin, S., Nasir, S., Faizah, W., Sobri, N., Mara, C., and Kelantan, M. (2019). Supervised data mining approach for predicting student performance. Indonesian Journal of Electrical Engineering and Computer Science, 16:1584–1592. [9] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems, pages 2672–2680. [10] Grayson, A., Miller, H., and Clarke, D. D. (1998). Identifying barriers to help-seeking: a qualitative analysis of students’ preparedness to seek help from tutors. British Journal of Guidance & Counselling, 26(2):237–253. [11] Guo, J., Lu, S., Cai, H., Zhang, W., Yu, Y., and Wang, J. (2018). Long text generation via adversarial training with leaked information. [12] Hadamard (1898). Les surfaces `a courbures opposées et leurs lignes géodésiques. Journal de Mathématiques Pures et Appliquées, 4:27–74. [13] Hadriche, A., Jmail, N., and Elleuch, R. (2014). Different methods of partitioning the phase space of a dynamic system. International Journal of Computer Applications,93:1–5. [14] Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780. [15] Hu, Y.-H., Lo, C.-l., and Shih, S.-P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior. [16] Kennel, M. and Buhl, M. (2003). Estimating good discrete partitions from observed data: Symbolic false nearest neighbors. Physical review letters, 91:084102. [17] Khatkhate, A. (2018). Anomaly detection in electromechanical systems using symbolic dynamics. [18] Kim, B., Vizitei, E., and Ganapathi, V. (2018). Gritnet: Student performance prediction with deep learning. In Boyer, K. E. and Yudelson, M., editors, Proceedings of the 11th International Conference on Educational Data Mining, EDM 2018, Buffalo, NY, USA, July 15-18, 2018. International Educational Data Mining Society (IEDMS). [19] Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9):1464–1480. [20] Kumar, A., Selvam, R., and Kumar, K. (2018). Review on prediction algorithms in educational data mining. International Journal of Pure and Applied Mathematics, 118:531–536. [21] Lin, T. Y., Chuang, H. H. C., and Yu, F. (2018). Tracking supply chain process variability with unsupervised cluster traversal. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pages 966–973. IEEE. [22] Luo, J., Sorour, S. E., Mine, T., and Goda, K. (2015). Predicting student grade based on free-style comments using word2vec and ANN by considering prediction results obtained in consecutive lessons. In Santos, O. C., Boticario, J., Romero, C., Pechenizkiy, M., Merceron, A., Mitros, P., Luna, J. M., Mihaescu, M. C., Moreno, P., Hershkovitz, A., Ventura, S., and Desmarais, M. C., editors, Proceedings of the 8th International Conference on Educational Data Mining, EDM 2015, Madrid, Spain, June 26-29, 2015, pages 396–399. International Educational Data Mining Society (IEDMS). [23] Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., and Loumos, V. (2009). Early and dynamic student achievement prediction in e-learning courses using neural networks. J. Assoc. Inf. Sci. Technol., 60(2):372–380. [24] Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations. In 5-th Berkeley Symposium on Mathematical Statistics and Probability, pages 281–297. [25] Okubo, F., Yamashita, T., Shimada, A., and Ogata, H. (2017). A neural network approach for students’ performance prediction. pages 598–599. [26] Rajagopalan, V., Ray, A., Samsi, R., and Mayer, J. (2007). Pattern identification in dynamical systems via symbolic time series analysis. Pattern Recognition, 40:2897– 2907. [27] Rattadilok, P. and Roadknight, C. (2018). Improving student’s engagement through the use of learning modules, instantaneous feedback and automated marking. In 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pages 802–806. [28] Rendle, S. (2010). Factorization machines. In Webb, G. I., Liu, B., Zhang, C., Gunopulos, D., and Wu, X., editors, The 10th IEEE International Conference on Data Mining, pages 995–1000. IEEE Computer Society. [29] Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., and Riedmiller, M. (2014). Deterministic policy gradient algorithms. 31st International Conference on Machine Learning, ICML 2014, 1. [30] Sloane, N. and Wyner, A. (2009). Prediction and entropy of printed english. pages 194–208. [31] Soni, A., Kumar, V., Kaur, R., and Hemavathi, D. (2018). Predicting student performance using data mining techniques. International Journal of Pure and Applied Mathematics, 119:221–226. [32] Subbu, A. and Ray, A. (2008). Space partitioning via hilbert transform for symbolic time series analysis. Applied Physics Letters, 92:084107–084107. [33] Sweeney, M., Lester, J., and Rangwala, H. (2015). Next-term student grade prediction. In 2015 IEEE International Conference on Big Data, Big Data 2015, Santa Clara, CA, USA, October 29 - November 1, 2015, pages 970–975. IEEE Computer Society. [34] Tien, Y., Hsu, C., and Yu, F. (2019). Hiseqgan: Hierarchical sequence synthesis and prediction. In Tetko, I. V., Kurková, V., Karpov, P., and Theis, F. J., editors, Artificial Neural Networks and Machine Learning - ICANN 2019: Deep Learning - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part II, volume 11728 of Lecture Notes in Computer Science, pages 621–638. Springer. [35] Vega-Márquez, B., Rubio-Escudero, C., Riquelme, J. C., and Nepomuceno-Chamorro, I. A. (2019). Creation of synthetic data with conditional generative adversarial networks. In 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) - Seville, Spain, May 13-15, 2019, Proceedings, volume 950 of Advances in Intelligent Systems and Computing, pages 231–240. Springer. [36] Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn., 8(3–4):229–256. [37] Williams, R. J. and Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1(2):270–280. [38] Yu, L., Zhang, W., Wang, J., and Yu, Y. (2017). Seqgan: Sequence generative adversarial nets with policy gradient. In Singh, S. P. and Markovitch, S., editors, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA, pages 2852–2858. AAAI Press.
|