技能掌握层面的学习者控制与可解释学习分析如何影响学生在辅导练习中完成任务与避免损失的意愿
How Learner Control and Explainable Learning Analytics on Skill Mastery Shape Student Desires to Finish and Avoid Loss in Tutored Practice
AI总结：
本研究探究不同控制层级结合可解释学习分析对辅导系统练习中学生选择策略的影响，通过中学生访谈发现学习者控制、技能掌握分析与假设解释可分别提升自我调节、引导弱项练习与强化优势掌握，为学习调节支持提供新思路。
摘要：
个性化习题选择能够提升辅导系统中的学生练习效果。过往研究聚焦于支持学习者控制的透明化习题选择机制，但很少让学习者实际参与练习材料的选择过程。我们探究了不同控制层级（即完全AI控制、共享控制、完全学习者控制）结合技能掌握情况学习分析展示与可视化假设解释（what-if explanations），如何在需要高度自我调节的练习场景（如家庭作业）中为学生提供支持。\n对6名中学生的半结构化访谈得出三项关键发现：（1）参与者高度重视学习者控制，认为其能提升学习体验、改善自我调节能力，尤其是因为多数学生希望避免技能掌握程度出现损失；（2）仅查看自身技能掌握预估值时，参与者往往会根据薄弱项选择习题；（3）假设解释促使参与者更多关注自身优势，并推动技能提升直至完全掌握。\n这些发现揭示了在学生拥有练习内容控制权时，可解释学习分析如何塑造学生的选择策略，也为辅导系统练习场景下帮助学生学会调节自身努力程度、动机与目标提供了有前景的方向。
英文摘要：
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 weaknesses; and (3) what-if explanations stimulated participants to focus more on their strengths and improve skills until they were mastered. These findings show how explainable learning analytics could shape students' selection strategies when they have control over what to practice. They suggest promising avenues for helping students learn to regulate their effort, motivation, and goals during practice with tutoring systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
