Standard
Sequence Modeling for Analysing Student Interaction with Educational Systems. / Hansen, Christian; Hansen, Casper; Hjuler, Niklas Oskar Daniel; Alstrup, Stephen; Lioma, Christina.
Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25-28, 2017. ed. / Xiangen Hu; Tiffany Barnes; Arnon Hershkovitz; Luc Paquette. International Educational Data Mining Society (IEDMS), 2017. p. 232-237 (Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25 – 28, 2017).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Hansen, C, Hansen, C, Hjuler, NOD
, Alstrup, S & Lioma, C 2017,
Sequence Modeling for Analysing Student Interaction with Educational Systems. in X Hu, T Barnes, A Hershkovitz & L Paquette (eds),
Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25-28, 2017. International Educational Data Mining Society (IEDMS), Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25 – 28, 2017, pp. 232-237, 10th International Conference on Educational Data Mining, Wuhan, China,
25/06/2017. <
https://arxiv.org/abs/1708.04164>
APA
Hansen, C., Hansen, C., Hjuler, N. O. D.
, Alstrup, S., & Lioma, C. (2017).
Sequence Modeling for Analysing Student Interaction with Educational Systems. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.),
Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25-28, 2017 (pp. 232-237). International Educational Data Mining Society (IEDMS). Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25 – 28, 2017
https://arxiv.org/abs/1708.04164Vancouver
Hansen C, Hansen C, Hjuler NOD, Alstrup S, Lioma C. Sequence Modeling for Analysing Student Interaction with Educational Systems. In Hu X, Barnes T, Hershkovitz A, Paquette L, editors, Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25-28, 2017. International Educational Data Mining Society (IEDMS). 2017. p. 232-237. (Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25 – 28, 2017).
Author
Hansen, Christian ; Hansen, Casper ; Hjuler, Niklas Oskar Daniel ; Alstrup, Stephen ; Lioma, Christina. / Sequence Modeling for Analysing Student Interaction with Educational Systems. Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25-28, 2017. editor / Xiangen Hu ; Tiffany Barnes ; Arnon Hershkovitz ; Luc Paquette. International Educational Data Mining Society (IEDMS), 2017. pp. 232-237 (Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25 – 28, 2017).
Bibtex
@inproceedings{d3a02942036349b7a43fd765f2b3595a,
title = "Sequence Modeling for Analysing Student Interaction with Educational Systems",
abstract = "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.",
author = "Christian Hansen and Casper Hansen and Hjuler, {Niklas Oskar Daniel} and Stephen Alstrup and Christina Lioma",
year = "2017",
month = jun,
day = "25",
language = "English",
series = "Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25 – 28, 2017",
pages = "232--237",
editor = "Xiangen Hu and Barnes, {Tiffany } and Arnon Hershkovitz and Paquette, {Luc }",
booktitle = "Proceedings of the 10th International Conference on Educational Data Mining, EDM 2017, Wuhan, Hubei, China, June 25-28, 2017",
publisher = "International Educational Data Mining Society (IEDMS)",
note = "10th International Conference on Educational Data Mining, EDM 2017 ; Conference date: 25-06-2017 Through 28-06-2017",
}
RIS
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 -
