The assistance dilemma is a well-recognized challenge to determine
when and how to provide help during problem solving
in intelligent tutoring systems. This dilemma is particularly
challenging to address in domains such as logic proofs,
where problems can be solved in a variety of ways. In this
study, we investigate two data-driven techniques to address
the when and how of the assistance dilemma, combining a
model that predicts when students need help learning efficient
strategies, and hints that suggest what subgoal to achieve.
We conduct a study assessing the impact of the new pedagogical
policy against a control policy without these adaptive
components. We found empirical evidence which suggests
that showing subgoals in training problems upon predictions
of the model helped the students who needed it most
and improved test performance when compared to their control
peers. Our key findings include significantly fewer steps
in posttest problem solutions for students with low prior proficiency
and significantly reduced help avoidance for all students
in training.