Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor
Mehak Maniktala, Christa Cody, Tiffany Barnes, and Min Chi

TL;DR
This paper introduces 'Assertions', a new hint delivery method in intelligent tutors, which significantly increases unsolicited hint usage and improves learning efficiency, especially for students with low prior proficiency, addressing help avoidance issues.
Contribution
The paper proposes and evaluates 'Assertions', a novel hint presentation mechanism that enhances unsolicited hint usage and student learning outcomes in intelligent tutoring systems.
Findings
Assertions significantly increase unsolicited hint usage.
Assertions help low-proficiency students generate more efficient solutions.
Pattern analysis shows productive persistence with Assertions.
Abstract
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…
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