Clustering and Profiling Students According to their Interactions with an Intelligent Tutoring System Fostering Self-Regulated Learning

Francois Bouchet, Jason M. Harley, Gregory J. Trevors, Roger Azevedo
2013 Zenodo  
In this paper, we present the results obtained using a clustering algorithm (Expectation-Maximization) on data collected from 106 college students learning about the circulatory system with MetaTutor, an agent-based Intelligent Tutoring System (ITS) designed to foster self-regulated learning (SRL). The three extracted clusters were validated and analyzed using multivariate statistics (MANOVAs) in order to characterize three distinct profiles of students, displaying statistically significant
more » ... erences over all 12 variables used for the clusters formation (including performance, use of note-taking and number of sub-goals attempted). We show through additional analyses that variations also exist between the clusters regarding prompts they received by the system to perform SRL processes. We conclude with a discussion of implications for designing a more adaptive ITS based on an identification of learners' profiles
doi:10.5281/zenodo.3554613 fatcat:eaeu335si5hjnobd26xrw5336a