  Research has shown assistance can provide many benefits to novices lacking
the mental models needed for problem solving in a new domain. However, varying
approaches to assistance, such as subgoals and next-step hints, have been
implemented with mixed results. Next-Step hints are common in data-driven
tutors due to their straightforward generation from historical student data, as
well as research showing positive impacts on student learning. However, there
is a lack of research exploring the possibility of extending data-driven
methods to provide higher-level assistance. Therefore, we modified our
data-driven Next-Step hint generator to provide Waypoints, hints that are a few
steps ahead, representing problem-solving subgoals. We hypothesized that
Waypoints would benefit students with high prior knowledge, and that Next-Step
hints would most benefit students with lower prior knowledge. In this study, we
investigated the influence of data-driven hint type, Waypoints versus Next-Step
hints, on student learning in a logic proof tutoring system, Deep Thought, in a
discrete mathematics course. We found that Next-Step hints were more beneficial
for the majority of students in terms of time, efficiency, and accuracy on the
posttest. However, higher totals of successfully used Waypoints were correlated
with improvements in efficiency and time in the posttest. These results suggest
that Waypoint hints could be beneficial, but more scaffolding may be needed to
help students follow them.
