  Within intelligent tutoring systems, considerable research has investigated
hints, including how to generate data-driven hints, what hint content to
present, and when to provide hints for optimal learning outcomes. However, less
attention has been paid to how hints are presented. In this paper, we propose a
new hint delivery mechanism called "Assertions" for providing unsolicited hints
in a data-driven intelligent tutor. Assertions are partially-worked example
steps designed to appear within a student workspace, and in the same format as
student-derived steps, to show students a possible subgoal leading to the
solution. We hypothesized that Assertions can help address the well-known hint
avoidance problem. In systems that only provide hints upon request, hint
avoidance results in students not receiving hints when they are needed. Our
unsolicited Assertions do not seek to improve student help-seeking, but rather
seek to ensure students receive the help they need. We contrast Assertions with
Messages, text-based, unsolicited hints that appear after student inactivity.
Our results show that Assertions significantly increase unsolicited hint usage
compared to Messages. Further, they show a significant aptitude-treatment
interaction between Assertions and prior proficiency, with Assertions leading
students with low prior proficiency to generate shorter (more efficient)
posttest solutions faster. We also present a clustering analysis that shows
patterns of productive persistence among students with low prior knowledge when
the tutor provides unsolicited help in the form of Assertions. Overall, this
work provides encouraging evidence that hint presentation can significantly
impact how students use them and using Assertions can be an effective way to
address help avoidance.

Artificial Intelligence