Applying a Framework for Student Modeling in Exploratory Learning Environments: Comparing Data Representation Granularity to Handle Environment Complexity

In IJAIED 27 (2)

Publication information


Clustering, Educational data mining, Exploratory learning environments, Interactive simulations, User modeling


Interactive simulations can facilitate inquiry learning. However, similarly to other Exploratory Learning Environments, students may not always learn effectively in these unstructured environments. Thus, providing adaptive support has great potential to help improve student learning with these rich activities. Providing adaptive support requires a student model that can both evaluate learning as well inform relevant feedback. Building such a model for interactive simulations is especially challenging because the exploratory nature of the interaction makes it hard to know a priori which behaviors are conducive to learning. To address this problem, in this paper we leverage the student modeling framework proposed in (Kardan and Conati, 2011) to specifically address the challenge of modeling students in interactive simulations. The framework has already been successfully applied to build a student model and to give adaptive interventions for an interactive simulation for constraint satisfaction. We seek to investigate the generality of the framework by building student models for a more complex simulation on electric circuits called Circuit Construction Kit (CCK). We evaluate alternative representations of logged interaction data with CCK, capturing different amounts of granularity and feature engineering. We then apply the student modeling framework proposed in (Kardan and Conati, 2011) to group students based on their interaction behaviors, map these behaviors into learning outcomes and leverage the resulting clusters to classify new learners. Data collected from 100 college students working with the CCK simulation indicates that the proposed framework is able to successfully classify students in groups of high and low learners and identify patterns of productive behaviors that are common across representations that can inform real-time feedback. In addition to presenting these results, we discuss trade-offs between levels of granularity and feature engineering in the tested interaction representations in terms of their ability to evaluate learning, classify students, and inform feedback.