Tracking behavioral patterns among students in an online educational system

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Tracking behavioral patterns among students in an online educational system. / Lorenzen, Stephan; Hjuler, Niklas; Alstrup, Stephen.

Proceedings of the 11'th International Conference on Educational Data Mining. EDM / Educational Data Mining, 2018. p. 280-285.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Lorenzen, S, Hjuler, N & Alstrup, S 2018, Tracking behavioral patterns among students in an online educational system. in Proceedings of the 11'th International Conference on Educational Data Mining. EDM / Educational Data Mining, pp. 280-285, 11th International Conference on Educational Data Mining, EDM 2018, Buffalo, United States, 15/07/2018.

APA

Lorenzen, S., Hjuler, N., & Alstrup, S. (2018). Tracking behavioral patterns among students in an online educational system. In Proceedings of the 11'th International Conference on Educational Data Mining (pp. 280-285). EDM / Educational Data Mining.

Vancouver

Lorenzen S, Hjuler N, Alstrup S. Tracking behavioral patterns among students in an online educational system. In Proceedings of the 11'th International Conference on Educational Data Mining. EDM / Educational Data Mining. 2018. p. 280-285

Author

Lorenzen, Stephan ; Hjuler, Niklas ; Alstrup, Stephen. / Tracking behavioral patterns among students in an online educational system. Proceedings of the 11'th International Conference on Educational Data Mining. EDM / Educational Data Mining, 2018. pp. 280-285

Bibtex

@inproceedings{96955e1ea6964606a7b517362ff9d8dc,
title = "Tracking behavioral patterns among students in an online educational system",
abstract = "Analysis of log data generated by online educational systems is an essential task to better the educational systems and increase our understanding of how students learn. In this study we investigate previously unseen data from Clio Online, the largest provider of digital learning content for primary schools in Denmark. We consider data for 14,810 students with 3 million sessions in the period 2015-2017. We analyze student activity in periods of one week. By using non-negative matrix factorization techniques, we obtain soft clusterings, revealing dependencies among time of day, subject, activity type, activity complexity (measured by Bloom{\textquoteright}s taxonomy), and performance. Furthermore, our method allows for tracking behavioral changes of individual students over time, as well as general behavioral changes in the educational system. Based on the results, we give suggestions for behavioral changes, in order to optimize the learning experience and improve performance.",
keywords = "Educational systems, Non-negative matrix factorization, Student clustering",
author = "Stephan Lorenzen and Niklas Hjuler and Stephen Alstrup",
year = "2018",
language = "English",
pages = "280--285",
booktitle = "Proceedings of the 11'th International Conference on Educational Data Mining",
publisher = "EDM / Educational Data Mining",
note = "11th International Conference on Educational Data Mining, EDM 2018 ; Conference date: 15-07-2018 Through 18-07-2018",

}

RIS

TY - GEN

T1 - Tracking behavioral patterns among students in an online educational system

AU - Lorenzen, Stephan

AU - Hjuler, Niklas

AU - Alstrup, Stephen

PY - 2018

Y1 - 2018

N2 - Analysis of log data generated by online educational systems is an essential task to better the educational systems and increase our understanding of how students learn. In this study we investigate previously unseen data from Clio Online, the largest provider of digital learning content for primary schools in Denmark. We consider data for 14,810 students with 3 million sessions in the period 2015-2017. We analyze student activity in periods of one week. By using non-negative matrix factorization techniques, we obtain soft clusterings, revealing dependencies among time of day, subject, activity type, activity complexity (measured by Bloom’s taxonomy), and performance. Furthermore, our method allows for tracking behavioral changes of individual students over time, as well as general behavioral changes in the educational system. Based on the results, we give suggestions for behavioral changes, in order to optimize the learning experience and improve performance.

AB - Analysis of log data generated by online educational systems is an essential task to better the educational systems and increase our understanding of how students learn. In this study we investigate previously unseen data from Clio Online, the largest provider of digital learning content for primary schools in Denmark. We consider data for 14,810 students with 3 million sessions in the period 2015-2017. We analyze student activity in periods of one week. By using non-negative matrix factorization techniques, we obtain soft clusterings, revealing dependencies among time of day, subject, activity type, activity complexity (measured by Bloom’s taxonomy), and performance. Furthermore, our method allows for tracking behavioral changes of individual students over time, as well as general behavioral changes in the educational system. Based on the results, we give suggestions for behavioral changes, in order to optimize the learning experience and improve performance.

KW - Educational systems

KW - Non-negative matrix factorization

KW - Student clustering

UR - http://www.scopus.com/inward/record.url?scp=85068327108&partnerID=8YFLogxK

M3 - Article in proceedings

SP - 280

EP - 285

BT - Proceedings of the 11'th International Conference on Educational Data Mining

PB - EDM / Educational Data Mining

T2 - 11th International Conference on Educational Data Mining, EDM 2018

Y2 - 15 July 2018 through 18 July 2018

ER -

ID: 240193925