Tracking behavioral patterns among students in an online educational system
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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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
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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