Abstract
Personalized learning is a powerful tool in online education, yet its application in inquiry based modeling environments remains underexplored. Previous work has shown that learners that engage in a cycle of construction, parameterization, and simulation, which we refer to as the exploration cycle, create models with higher complexity and variety. In order to further study these findings we present an “exploration coach” that provides personalized feedback within the Virtual Experimental Research Assistant (VERA)—an interactive learning environment for conceptual modeling of complex systems that evaluates models through agent-based simulations. Our architecture, which classifies the learners into groups
using clustering techniques, allows us to determine what type of feedback would be useful to a learner at any point in their modeling journey. The coach then uses procedural scaffolding to guide learners through the exploration cycle. Lastly we illustrate how these categorizations and the exploration cycle map onto the cycle of self-directed learning.
