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While there is evidence that user-adaptive support can greatly enhance theeffectiveness of educational systems, designing such support for exploratorylearning environments (e.g., simulations) is still challenging due to theopen-ended nature of their interaction. In particular, there is little a prioriknowledge of which student's behaviors can be detrimental to learning in suchenvironments. To address this problem, we focus on a data-driven user-modelingframework that uses logged interaction data to learn which behavioral oractivity patterns should trigger help during interaction with a specificlearning environment. This framework has been successfully used to provideadaptive support in interactive learning simulations. Here we present a novelapplication of this framework we are working on, namely to Massive Open OnlineCourses (MOOCs), a form of exploratory environment that could greatly benefitfrom adaptive support due to the large diversity of their users, but typicallylack of such adaptation. We describe an experiment aimed at investigating thevalue of our framework to identify student's behaviors that can justifyadapting to, and report some preliminary results.

A Framework to Counteract Suboptimal User-Behaviors in Exploratory Learning Environments: an Application to MOOCs