Tracking Behavioral Patterns among Students in an Online Educational System

TL;DR

This paper analyzes extensive online student activity data using matrix factorization to uncover behavioral patterns, track individual changes, and suggest improvements for online learning systems.

Contribution

It introduces a novel application of non-negative matrix factorization to analyze student behavior and track changes over time in large-scale online educational data.

Findings

Identified dependencies among activity time, subject, and performance.

Tracked behavioral changes of individual students over time.

Provided suggestions to optimize learning experiences.

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.