Sequence Modeling for Analysing Student Interaction with Educational Systems

TY - GEN

T1 - Sequence Modeling for Analysing Student Interaction with Educational Systems

AU - Hansen, Christian

AU - Hansen, Casper

AU - Hjuler, Niklas Oskar Daniel

AU - Alstrup, Stephen

AU - Lioma, Christina

PY - 2017/6/25

Y1 - 2017/6/25

N2 - 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.

AB - 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.

M3 - Article in proceedings

T3 - Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25 – 28, 2017

SP - 232

EP - 237

BT - Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25-28, 2017

A2 - Hu, Xiangen

A2 - Barnes, Tiffany

A2 - Hershkovitz, Arnon

A2 - Paquette, Luc

PB - International Educational Data Mining Society (IEDMS)

T2 - 10th International Conference on Educational Data Mining

Y2 - 25 June 2017 through 28 June 2017

ER -