Abstract
Improving the performance of students is an important challenge for higher education institutions. At most European universities, duration and completion rate of degrees are highly varying and consulting services are offered to increase student achievement. Here, we propose a data analytics approach to determine optimal choices for the courses of the next term. We use machine learning techniques to predict the performance of a student in upcoming courses. These prediction form the transition probabilities of a Markov decision process (MDP) that describes the course of studies of a student. Using this model we plan to explore the effect of different strategies on student performance.
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References
Elbadrawy, A., Polyzou, A., Ren, Z., Sweeney, M., Karypis, G., Rangwala, H.: Predicting student performance using personalized analytics. Computer 49(4), 61–69 (2016)
Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning, vol. 1. Springer series in statistics Springer, Berlin (2001). https://doi.org/10.1007/978-0-387-84858-7
Kocsis, L., Szepesvári, C.: Bandit based Monte-Carlo planning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 282–293. Springer, Heidelberg (2006). https://doi.org/10.1007/11871842_29
Murphy, K.P.: Machine Learning: A Probabilistic Perspective. MIT press, Cambridge (2012)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Polyzou, A., Karypis, G.: Grade prediction with models specific to students and courses. Int. J. Data Sci. Anal. 2(3–4), 159–171 (2016)
Romero, C., Ventura, S.: Educational data mining: a review of the state of the art. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40(6), 601–618 (2010)
Bachelor Program Regulations, Saarland University (2015). http://www.ps-mint.uni-saarland.de/fileadmin/user_upload/_imported/fileadmin/Benutzerdaten/Downloads/Formulare/Informatik/StO_PO/StO_BA_Informatik_2015.pdf. Accessed June 2017
Vossensteyn, H., Kottmann, A., Jongbloed, B., Kaiser, F., Cremonini, L., Stensaker, B., Hovdhaugen, E., Wollscheid, S.: Dropout and completion in higher education in Europe: main report (2015)
Acknowledgments
We thank Jens Dittrich, Endre Palatinus, and Thilo Krüger for pre-processing the data and interesting discussions.
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Backenköhler, M., Wolf, V. (2018). Student Performance Prediction and Optimal Course Selection: An MDP Approach. In: Cerone, A., Roveri, M. (eds) Software Engineering and Formal Methods. SEFM 2017. Lecture Notes in Computer Science(), vol 10729. Springer, Cham. https://doi.org/10.1007/978-3-319-74781-1_3
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DOI: https://doi.org/10.1007/978-3-319-74781-1_3
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