Open-ended educational tools can encourage creativity and active engagement, and may be used beyond the classroom. Being able to model and predict learner performance in such tools is a critical component to assist the student, and enable tool refinement. However, open-ended educational domains typically allow an extremely broad range of learner input. As such, building the same kind of cognitive models often used to track and predict student behavior in existing systems is challenging. In addition, the resulting large spaces of user input coupled with comparatively sparse observed data, limits the applicability of straightforward classification methods. We address these difficulties with a new algorithm that combines Markov models, state aggregation, and player heuristic search, dynamically selecting between these methods based on the amount of available data. Applied to a popular educational game, our hybrid model achieved greater predictive accuracy than any of the methods alone, and performed significantly better than a random baseline. We demonstrate how our model can learn player heuristics on data from one task that accurately predict performance on future tasks, and explain how our model retains parameters that are interpretable to non-expert users
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