Comparative Action Sequence Analysis with Hidden Markov Models and Sequence Mining
John S. Kinnebrew, Gautam Biswas · 2011
Computer-based learning environments produce a wealth of data on student learning interactions. This paper presents an exploratory data mining methodology for assessing and comparing students’ learning behaviors from these interaction traces. In the rst phase of this methodology, hidden Markov models (HMMs) are generated to model learning behaviors of the student groups being compared (e.g., high versus low performers). In this paper, we supplement the HMM technique with a novel combination of sequence mining techniques to identify dierentially frequent patterns (between the student groups) in a nergrained analysis. We demonstrate the complete methodology through the analysis of learning trace data from a recent middle school classroom study with the Betty’s Brain learning environment. The results illustrate the eectiveness of this exploratory methodology and suggest further renements of the HMM generation and sequence mining techniques.