Sequence Analysis of Learning Behavior in Different Consecutive Activities

TL;DR

This study analyzes student behavior across different activities using log data and unsupervised learning to identify individual students and common behavioral patterns.

Contribution

It introduces a method to identify students and shared patterns through behavior analysis of log data from educational activities.

Findings

Students can be statistically identified by their behavior.

Shared behavioral patterns exist among students.

Unsupervised learning reveals common activity patterns.

Abstract

The purpose of this research is to study the possibility of identifying students, statistically, by analyzing their behavior in different consecutive activities. In this project, there are three different sorts of activities: animated example, basic example, and parameterized exercises. We extracted the behavior of each student from the log activities of the Mastery Grids platform. Additionally, we investigate by using unsupervised learning technique, whether there are common patterns, that students share or not while performing these activities. We conclude that we are able to identify students from their behavior, besides that there are some common patterns.