  Determining when and whether to provide personalized support is a well-known
challenge called the assistance dilemma. A core problem in solving the
assistance dilemma is the need to discover when students are unproductive so
that the tutor can intervene. Such a task is particularly challenging for
open-ended domains, even those that are well-structured with defined principles
and goals. In this paper, we present a set of data-driven methods to classify,
predict, and prevent unproductive problem-solving steps in the well-structured
open-ended domain of logic. This approach leverages and extends the Hint
Factory, a set of methods that leverages prior student solution attempts to
build data-driven intelligent tutors. We present a HelpNeed classification,
that uses prior student data to determine when students are likely to be
unproductive and need help learning optimal problem-solving strategies. We
present a controlled study to determine the impact of an Adaptive pedagogical
policy that provides proactive hints at the start of each step based on the
outcomes of our HelpNeed predictor: productive vs. unproductive. Our results
show that the students in the Adaptive condition exhibited better training
behaviors, with lower help avoidance, and higher help appropriateness (a higher
chance of receiving help when it was likely to be needed), as measured using
the HelpNeed classifier, when compared to the Control. Furthermore, the results
show that the students who received Adaptive hints based on HelpNeed
predictions during training significantly outperform their Control peers on the
posttest, with the former producing shorter, more optimal solutions in less
time. We conclude with suggestions on how these HelpNeed methods could be
applied in other well-structured open-ended domains.
