Abstract
With the rapid advancement of Virtual Learning Environments (VLE) in higher education, the amount of available student data grows. Universities collect the information about students, their demographics, their study results and their behaviour in the online environment. By applying modelling and predictive analysis methods it is possible to predict student outcome or detect bottlenecks in course design. Our work aims at statistical simulation of student behaviour in the VLE in order to identify behavioural patterns leading to drop-out or passive withdrawal i.e. the state when a student is not studying, but he has not actively withdrawn from studies. For that purpose, the method called Markov chain modelling has been used. Recorded student activities in VLE (VLE logs) has been used for constructing of probabilistic representation that students will perform some activity in the next week based on their activities in the current week. The result is an instance of the family of absorbing Markov chains, which can be analysed using the property called time to absorption. The preliminary results show that interesting patterns in student VLE behaviour can be uncovered, especially when combined with the information about submission of the first assessment. Our analysis has been performed using Open University Learning Analytics dataset (OULAD) and research notes are available online (https://bit.ly/2JrY5zv) .
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References
Moodle, H.Q.: Moodle statistics. Moodle HQ (2018). https://moodle.org/stats/. Accessed 25 Apr 2018
Coursera Inc., “Coursera,” Coursera Inc. (2012). https://www.coursera.org/. Accessed 10 Apr 2018
Papamitsiou, Z., Economides, A.A.: Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. Educ. Technol. Soc. 17, 49–64 (2014)
Hlosta, M., Herrmannova, D., Vachova, L., Kuzilek, J., Zdrahal, Z., Wolff, A.: Modelling student online behaviour in a virtual learning environment. In: Proceedings of the 4th International Conference on Learning Analytics and Knowledge, Indianapolis (2014)
Okubo, F., Shimada, A., Taniguchi, Y., Konomi, S.: A visualization system for predicting learning activities using state transition graphs. In: Proceedings of 14th International Conference on Cognition and Exploratory Learning in Digital Age, Vilamoura (2017)
Davis, D., Chen, G., Hauff, C., Houben, G.-J.: Gauging MOOC learners’ adherence to the designed learning path. In: Proceedings of 9th International Conference on Educational Data Mining, Raleigh (2016)
Norris, J.R.: Markov Chains. Cambridge University Press, Cambridge (1997)
Kuzilek, J., Hlosta, M., Zdrahal, Z.: Open university learning analytics dataset. Sci. Data 4, 170171 (2017)
Wolff, A., Zdrahal, Z., Herrmannova, D., Kuzilek, J., Hlosta, M.: Developing predictive models for early detection of at-risk students on distance learning modules. In: Proceedings of the 4th International Conference on Learning Analytics and Knowledge, Indianapolis (2014)
Acknowledgement
This work was supported by junior research project by Czech Science Foundation GACR no. GJ18-04150Y.
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Kuzilek, J., Vaclavek, J., Fuglik, V., Zdrahal, Z. (2018). Student Drop-out Modelling Using Virtual Learning Environment Behaviour Data. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds) Lifelong Technology-Enhanced Learning. EC-TEL 2018. Lecture Notes in Computer Science(), vol 11082. Springer, Cham. https://doi.org/10.1007/978-3-319-98572-5_13
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DOI: https://doi.org/10.1007/978-3-319-98572-5_13
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