Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor
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Avoiding Help Avoidance: Using Interface Design Changes to Promote Unsolicited Hint Usage in an Intelligent Tutor Mehak Maniktala1
· Christa Cody1 · Tiffany Barnes1 · Min Chi1
© International Artificial Intelligence in Education Society 2020
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 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. Keywords Intelligent tutoring system · Help avoidance · User experience · Unsolicited hints · Aptitude-treatment interaction · Logic proofs · Productive persistence · Clustering · problem solving Mehak Maniktala
[email protected] 1
Department of Computer Science, North Carolina State University, Raleigh, NC, USA
International Journal of Artificial Intelligence in Education
Introduction Studies suggest that hints, when provided appropriately, can augment students’ learning experience (Bunt et al. 2004; Puustinen 1998) and improve their performance (Bartholom´e et al. 2006). However, students may not use hints optimally (Duong et al. 2013; Aleven et al. 2006); some abuse hints to expedite problem completion, and some avoid seeking help when they are in need (Aleven and Koedinger 2000; Price et al. 2017c). Our goal is to redesign the hint interface to solve this help avoidance problem. Considerable research has investigated hints from several perspectives, including hint generation (Barnes et al. 2008; Pri
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