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Within intelligent tutoring systems, considerable research has investigatedhints, including how to generate data-driven hints, what hint content topresent, and when to provide hints for optimal learning outcomes. However, lessattention has been paid to how hints are presented. In this paper, we propose anew hint delivery mechanism called "Assertions" for providing unsolicited hintsin a data-driven intelligent tutor. Assertions are partially-worked examplesteps designed to appear within a student workspace, and in the same format asstudent-derived steps, to show students a possible subgoal leading to thesolution. We hypothesized that Assertions can help address the well-known hintavoidance problem. In systems that only provide hints upon request, hintavoidance results in students not receiving hints when they are needed. Ourunsolicited Assertions do not seek to improve student help-seeking, but ratherseek to ensure students receive the help they need. We contrast Assertions withMessages, text-based, unsolicited hints that appear after student inactivity.Our results show that Assertions significantly increase unsolicited hint usagecompared to Messages. Further, they show a significant aptitude-treatmentinteraction between Assertions and prior proficiency, with Assertions leadingstudents with low prior proficiency to generate shorter (more efficient)posttest solutions faster. We also present a clustering analysis that showspatterns of productive persistence among students with low prior knowledge whenthe tutor provides unsolicited help in the form of Assertions. Overall, thiswork provides encouraging evidence that hint presentation can significantlyimpact how students use them and using Assertions can be an effective way toaddress help avoidance.

Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor