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The volume and quality of data, but also their relevance, are crucial when performing data analysis. In this paper, a study of the influence of different types of data is presented, particularly in the context of educational data obtained from Learning Management Systems (LMSs). These systems provide a large amount of data from the student activity but they usually do not describe the results of the learning process, i.e., they describe the behaviour but not the learning results. The starting hypothesis states that complementing behavioural data with other more relevant data (regarding learning outcomes) can lead to a better analysis of the learning process, that is, in particular it is possible to early predict the student final performance. A learning platform has been specially developed to collect data not just from the usage but also related to the way students learn and progress in training activities. Data of both types are used to build a progressive predictive system for helping in the learning process. This model is based on a classifier that uses the Support Vector Machine technique. As a result, the system obtains a weekly classification of each student as the probability of belonging to one of three classes: high, medium and low performance. The results show that, supplementing behavioural data with learning data allows us to obtain better predictions about the results of the students in a learning system. Moreover, it can be deduced that the use of heterogeneous data enriches the final performance of the prediction algorithms.
behavioural data, learning analytics, learning data, prediction
[1] Long, P. & Siemens, G., Penetrating the fog: analytics in learning and education. Educase Review, 2011.
[2] Siemens, G., Learning analytics: envisioning a research discipline and a domain of practice.
International Conference on Learning Analytics and Knowledge LAK, pp. 4–8, 2012. http://dx.doi.org/10.1145/2330601.2330605
[3] Macfadyen, L.P. & Dawson, S., Mining LMS data to develop an ‘early warning system’ for educators: a proof of concept. Computers & Education, 54(2), pp. 588–599, 2010. http://dx.doi.org/10.1016/j.compedu.2009.09.008
[4] Kotsiantis, S.B., Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades. Artificial Intelligence Review, 37(4), pp. 331–344, 2012. http://dx.doi.org/10.1007/s10462-011-9234-x
[5] Huang, S. & Fang, N., Predicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical models. Computers & Education, 61(0), pp. 133–145, 2013.
http://dx.doi.org/10.1016/j.compedu.2012.08.015
[6] Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V. & Loumos, V., Early and dynamic student achievement prediction in e-learning courses using neural networks. Journal of the American Society for Information Science and Technology, 60(2), pp. 372–380, 2009. http://dx.doi.org/10.1002/asi.20970
[7] Illanas Vila, A.I., Calvo Ferrer, J.R., Gallego-Durán, F.J. & Llorens Largo, F., Predicting student performance in translating foreing languages with a serious game. In INTED2013 Proceedings, Valencia, Spain, pp. 52–59, 2013.
[8] Yoo, J. & Kim, J., Can online discussion participation predict group project performance?
investigating the roles of linguistic features and participation patterns. International Journal of Artificial Intelligence in Education, 24(1), pp. 8–32, 2014. http://dx.doi.org/10.1007/s40593-013-0010-8
[9] Schalk, P.D., Wick, D.P., Turner, P.R. & Ramsdell, M.W., Predictive assessment of student performance for early strategic guidance. In Frontiers in Education Conference (FIE), pp.
S2H–1–S2H–5, 2011.
http://dx.doi.org/10.1109/fie.2011.6143086
[10] Petkovic, D., Okada, K., Sosnick, M., Iyer, A., Zhu, S., Todtenhoefer, R. & Huang, S., Work in progress: a machine learning approach for assessment and prediction of teamwork effectiveness in software engineering education. In Frontiers in Education Conference (FIE), pp. 1–3, 2012.
http://dx.doi.org/10.1109/fie.2012.6462205
[11] Ley, T. & Kump, B., Which user interactions predict levels of expertise in work-integrated learning? In EC-TEL, 8095, pp. 178–190, 2013.
http://dx.doi.org/10.1007/978-3-642-40814-4_15
[12] Wang, A.Y. & Newlin, M.H., Characteristics of students who enroll and succeed in psychology web-based classes. Journal of Educational Psychology, 92(1), p. 137, 2000. http://dx.doi.org/10.1037/0022-0663.92.1.137
[13] Wang, A.Y. & Newlin, M.H., Predictors of performance in the virtual classroom: identifying and helping at-risk cyber-students. The Journal of Higher Education, 29(10), pp. 21–25, 2002. [14] Campbell, J.B. & Oblinger, D.G., Academic analytics. Educase, 2007.
[15] Goldstein, P.J. & Katz, R.N., Academic analytics: the uses of management information and technology in higher education. Educase, 2005.
[16] Villagrá-Arnedo, C., Castel De Haro, M.J., Gallego-Durán, F.J., Pomares Puig, C., Suau Pérez, P. & Cortés Vaíllo, S., Real-time evaluation. In EDULEARN09 Proceedings, Barcelona, Spain, pp. 3361–3369, 2009.
[17] Cortes, C. & Vapnik, V., Support-vector networks. Machine Learning, 20(3), pp. 273–297, 1995. http://dx.doi.org/10.1007/BF00994018
[18] Vapnik, V.N., The Nature of Statistical Learning Theory, Springer-Verlag New York, Inc.: New York, NY, USA, 1995.
[19] Vapnik, V.N., Statistical Learning Theory, 1st edn., Wiley, 1998.
[20] Wu, T.F., Lin, C-J. & Weng, R.C., Probability estimates for multi-class classification by pairwise coupling. The Journal of Machine Learning Research, 5, pp. 975–1005, 2004.
[21] Villagrá-Arnedo, C., Gallego-Durán, F.J., Molina-Carmona, R. & Llorens-Largo, F., Boosting the learning process with progressive performance prediction. In Design for Teaching and Learning in a Networked World, vol. 9307, eds. G. Conole, T. Klobučar, C. Rensing, J. Konert & É. Lavoué, Springer International Publishing: Cham, pp. 638–641, 2015. http://dx.doi.org/10.1007/978-3-319-24258-3_77
[22] Szczepańska, A., Research design and statistical analysis, third edition by Jerome L. Myers, Arnold D. Well, Robert F. Lorch, Jr. International Statistical Review, 79(3), pp. 491–492, 2011.