可视化智能导师交互以支持响应式教学
Visualizing Intelligent Tutor Interactions for Responsive Pedagogy
AI总结：
针对教师难以利用智能辅导数据支持响应式教学的问题，本文通过设计研究构建可视化分析系统VisTA，帮助教师解读交互数据并制定后续教学行动。
摘要：
智能辅导系统利用专家学习和学生知识的AI模型，为学生提供个性化辅导。尽管这些智能导师已显示出能提升学生学习成果，但教师如何将其整合到课程与教学规划中以支持响应式教学仍不明确。本文中，我们与五位在课堂上部署了智能辅导平台Apprentice Tutors的教师进行了设计研究。我们分析了他们在分析智能辅导系统学生交互数据时面临的挑战，并构建了VisTA（辅导分析可视化系统），一个在多个协调视图中展示详细溯源数据的可视化分析系统。我们与这五位教师一同评估了VisTA，发现可视化帮助他们更好地解读智能导师数据，洞察学生问题解决溯源，并决定必要的后续行动——例如为学生提供进一步支持或在课堂上复习技能。最后，我们讨论了VisTA扩展到序列查询与检测的潜在可能，以及这些可视化在鼓励学生自主学习方面的潜力。
英文摘要：
Intelligent tutoring systems leverage AI models of expert learning and student knowledge to deliver personalized tutoring to students. While these intelligent tutors have demonstrated improved student learning outcomes, it is still unclear how teachers might integrate them into curriculum and course planning to support responsive pedagogy. In this paper, we conducted a design study with five teachers who have deployed Apprentice Tutors, an intelligent tutoring platform, in their classes. We characterized their challenges around analyzing student interaction data from intelligent tutoring systems and built VisTA (Visualizations for Tutor Analytics), a visual analytics system that shows detailed provenance data across multiple coordinated views. We evaluated VisTA with the same five teachers, and found that the visualizations helped them better interpret intelligent tutor data, gain insights into student problem-solving provenance, and decide on necessary follow-up actions - such as providing students with further support or reviewing skills in the classroom. Finally, we discuss potential extensions of VisTA into sequence query and detection, as well as the potential for the visualizations to be useful for encouraging self-directed learning in students.
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。
