Sequence Modeling for Analysing Student Interaction with Educational Systems
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
The analysis of log data generated by online educational systems is an important task for improving the systems, and furthering our knowledge of how students learn. This paper uses previously unseen log data from Edulab, the largest provider of digital learning for mathematics in Denmark, to analyse the sessions of its users, where 1.08 million student sessions are extracted from a subset of their data. We propose to model students as a distribution of different underlying student behaviours, where the sequence of actions from each session belongs to an underlying student behaviour. We model student behaviour as Markov chains, such that a student is modelled as a distribution of Markov chains, which are estimated using a modified k-means clustering algorithm. The resulting Markov chains are readily interpretable, and in a qualitative analysis around 125,000 student sessions are identified as exhibiting unproductive student behaviour. Based on our results this student representation is promising, especially for educational systems offering many different learning usages, and offers an alternative to common approaches like modelling student behaviour as a single Markov chain often done in the literature.
Original language | English |
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Title of host publication | Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25-28, 2017 |
Editors | Xiangen Hu, Tiffany Barnes, Arnon Hershkovitz, Luc Paquette |
Publisher | International Educational Data Mining Society (IEDMS) |
Publication date | 25 Jun 2017 |
Pages | 232-237 |
Publication status | Published - 25 Jun 2017 |
Event | 10th International Conference on Educational Data Mining - Wuhan, China Duration: 25 Jun 2017 → 28 Jun 2017 |
Conference
Conference | 10th International Conference on Educational Data Mining |
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Land | China |
By | Wuhan |
Periode | 25/06/2017 → 28/06/2017 |
Series | Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25 – 28, 2017 |
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ID: 189882360