Tracking Behavioral Patterns among Students in an Online Educational System
This work addresses improving online education systems for students and educators by providing data-driven insights, though it is incremental as it applies existing methods to new data.
The study analyzed log data from 14,810 students with 3 million sessions in an online educational system to uncover behavioral patterns using non-negative matrix factorization, revealing dependencies among factors like time of day and performance to suggest optimizations for learning.
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.