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Determining when and whether to provide personalized support is a well-knownchallenge called the assistance dilemma. A core problem in solving theassistance dilemma is the need to discover when students are unproductive sothat the tutor can intervene. Such a task is particularly challenging foropen-ended domains, even those that are well-structured with defined principlesand goals. In this paper, we present a set of data-driven methods to classify,predict, and prevent unproductive problem-solving steps in the well-structuredopen-ended domain of logic. This approach leverages and extends the HintFactory, a set of methods that leverages prior student solution attempts tobuild data-driven intelligent tutors. We present a HelpNeed classification,that uses prior student data to determine when students are likely to beunproductive and need help learning optimal problem-solving strategies. Wepresent a controlled study to determine the impact of an Adaptive pedagogicalpolicy that provides proactive hints at the start of each step based on theoutcomes of our HelpNeed predictor: productive vs. unproductive. Our resultsshow that the students in the Adaptive condition exhibited better trainingbehaviors, with lower help avoidance, and higher help appropriateness (a higherchance of receiving help when it was likely to be needed), as measured usingthe HelpNeed classifier, when compared to the Control. Furthermore, the resultsshow that the students who received Adaptive hints based on HelpNeedpredictions during training significantly outperform their Control peers on theposttest, with the former producing shorter, more optimal solutions in lesstime. We conclude with suggestions on how these HelpNeed methods could beapplied in other well-structured open-ended domains.

Extending the Hint Factory for the assistance dilemma: A novel, data-driven HelpNeed Predictor for proactive problem-solving help