TL;DR
This study investigates how different levels of learner control and explainable analytics influence middle school students' problem selection and motivation in tutoring systems, highlighting the importance of transparency and control for self-regulation.
Contribution
It demonstrates how combining learner control with explainable learning analytics can positively influence students' practice strategies and motivation in high self-regulation tasks.
Findings
Students value learner control for better self-regulation.
Seeing skill mastery estimates influences problem choices.
What-if explanations encourage focus on strengths.
Abstract
Personalized problem selection enhances student practice in tutoring systems. Prior research has focused on transparent problem selection that supports learner control but rarely engages learners in selecting practice materials. We explored how different levels of control (i.e., full AI control, shared control, and full learner control), combined with showing learning analytics on skill mastery and visual what-if explanations, can support students in practice contexts requiring high degrees of self-regulation, such as homework. Semi-structured interviews with six middle school students revealed three key insights: (1) participants highly valued learner control for an enhanced learning experience and better self-regulation, especially because most wanted to avoid losses in skill mastery; (2) only seeing their skill mastery estimates often made participants base problem selection on their…
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