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’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.
| Original language | English |
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| Title of host publication | Proceedings of the 11'th International Conference on Educational Data Mining |
| Publisher | EDM / Educational Data Mining |
| Publication date | 2018 |
| Pages | 280-285 |
| Publication status | Published - 2018 |
| Event | 11th International Conference on Educational Data Mining, EDM 2018 - Buffalo, United States Duration: 15 Jul 2018 → 18 Jul 2018 |
Conference
| Conference | 11th International Conference on Educational Data Mining, EDM 2018 |
|---|---|
| Country/Territory | United States |
| City | Buffalo |
| Period | 15/07/2018 → 18/07/2018 |
| Sponsor | ACTNext, Central China Normal University, University at Buffalo, YiXue Inc. |
Keywords
- Educational systems
- Non-negative matrix factorization
- Student clustering