Abstract
As machine learning evolves, it is significant to apply machine learning techniques to the intelligent analysis on educational data and the establishment of more intelligent academic early warning system. In this paper, we use Gaussian process (GP)-based models to discover valuable inherent information in the educational data and make intelligent predictions. Specifically, the mixtures of GP regression model is adopted to select personalized key courses and the GP regression model is applied to predict the course scores. We conduct experiments on real-world data which are collected from two grades in a certain university. The experimental results show that our approaches can make reasonable analysis on educational data and provide prediction information about the unknown scores, thus helping to make more precise academic early warning.
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References
Sun, S.: Computational education science and ten research directions. Commun. Chin. Assoc. Artif. Intell. 9(5), 15–16 (2015)
Hsu, T.: Research methods and data analysis procedures used by educational researchers. Int. J. Res. Method Educ. 28(2), 109–133 (2005)
Weng, C.: Mining fuzzy specific rare itemsets for education data. Knowl. Based Syst. 24(5), 697–708 (2011)
Limprasert, W., Kosolsombat, S.: A case study of data analysis for educational management. In: International Joint Conference on Computer Science and Software Engineering, pp. 1–5. IEEE, New York (2016)
Yin, M., Zhao, J., Sun, S.: Key course selection for academic early warning based on Gaussian processes. In: Yin, H., et al. (eds.) IDEAL 2016. LNCS, vol. 9937, pp. 240–247. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46257-8_26
Rasmussen, C.E., Williams, C.K.I.: Gaussian Process for Machine Learning. MIT Press, Cambridge (2006)
Ghahramani, Z.: Probabilistic machine learning and artificial intelligence. Nature 521(7533), 452–459 (2015)
Titsias, M.K.: Variational learning of inducing variables in sparse Gaussian processes. In: Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, pp. 567–574. JMLR, Cambridge (2009)
Zhu, J., Sun, S.: Single-task and multitask sparse Gaussian processes. In: Proceedings of the International Conference on Machine Learning and Cybernetics, pp. 1033–1038. IEEE, New York (2013)
Sun, S., Xu, X.: Variational inference for infinite mixtures of Gaussian processes with applications to traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 12(2), 466–475 (2011)
Sun, S.: Infinite mixtures of multivariate Gaussian processes. In: Proceedings of the International Conference on Machine Learning and Cybernetics, pp. 1011–1016. IEEE, New York (2013)
Luo, C., Sun, S.: Variational mixtures of Gaussian processes for classification. In: Proceedings of 26th International Joint Conference on Artificial Intelligence, pp. 4603–4609. Morgan Kaufmann, San Francisco (2017)
Álvarez, M.A., Lawrence, N.D.: Computationally efficient convolved multiple output Gaussian processes. J. Mach. Learn. Res. 12(May), 1459–1500 (2011)
Wilson, A.G., Knowles, D.A., Ghahramani, Z.: Gaussian process regression networks. In: Proceedings of the 29th International Conference on Machine Learning, pp. 599–606. ACM, New York (2012)
Damianou, A., Lawrence, N.: Deep Gaussian processes. In: International Conference on Artificial Intelligence and Statistics, pp. 207–215. JMLR, Cambridge (2013)
Wang, Y., Chaib-draa, B.: KNN-based kalman filter: an efficient and non-stationary method for Gaussian process regression. Knowl. Based Syst. 114(10), 148–155 (2016)
Rasmussen, C.E., Ghahramani, Z.: Infinite mixtures of Gaussian process experts. Adv. Neural Inf. Process. Syst. 14(4), 881–888 (2002)
Meeds, E., Osindero, S.: An alternative infinite mixture of Gaussian process experts. Adv. Neural Inf. Process. Syst. 18(1), 883–890 (2006)
Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: Recent advances and emerging challenges of feature selection in the context of big data. Knowl. Based Syst. 86(5), 33–45 (2015)
Acknowledgments
The first two authors Jiachun Wang and Jing Zhao are joint first authors. The corresponding author is Jing Zhao. This work is sponsored by Shanghai Sailing Program, NSFC Project 61673179 and Shanghai Knowledge Service Platform Project (No. ZF1213).
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Appendix
Appendix
The following is the comparison table for abbreviations and full names of different courses.
Abbreviation | Full Name |
|---|---|
AA | Abstract Algebra |
AI | Artificial Intelligent |
AM | Advanced Mathematics |
C | C Programming |
COA | Computer Organization and Architecture |
COAP | Computer Organization and Architecture Practice |
CP | College Physics |
CPP | Computer Programming Practice |
DLP | Digital Logic and Practice |
DM | Discrete Mathematics |
Edu. | Education |
Eng. | English |
ICSP | Introduction to Computer Science and Practice |
LA | Linear Algebra |
MC | Modular Class |
OCMH | Outline of Chinese Modern History |
OS | Operating System |
PE | Physical Education |
PMS | Probability and Mathematical Statistics |
Psy. | Psychology |
XML | XML Programming |
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Wang, J., Zhao, J., Sun, S., Shi, D. (2018). Intelligent Educational Data Analysis with Gaussian Processes. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_30
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DOI: https://doi.org/10.1007/978-3-030-04224-0_30
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