EXP: Building a Learning Analytics System to Improve Student Learning and Promote Adaptive Teaching Across Multiple Domains

这个PI团队的目标是利用人工智能来利用从智能辅导系统收集的数据，在正确的时间有效地向学生和教师提供反馈。该团队正在使用一种新的分析方法，将分层建模引入学习分析，以研究如何更好地了解学生的学习状态。算法利用认知理论和统计数据，从学生的学习数据中做出有效的、可解释的和可操作的推断。就像在辅导系统中一样，分析是在组成技能的层面上，而不是从整体上看任务的最终表现。研究围绕着算法的构建来推断学生的学习和学生的学习状态，以及向学习者和他们的老师发出信号的学习方法，即学习者理解和能够掌握哪些概念和技能，以及他们在哪些方面有困难。学习仪表板将允许教师可视化整个班级的学习需求，并根据学生的需求调整活动。针对学习者自身的反馈将帮助学习者认识到他们需要参与的活动，以提高他们的技能或理解能力。评估将包括学习者在使用这些工具时发展元认知技能的程度。拟议的工作将有助于下一代智能辅导系统，并有助于利用大规模教育数据库所需的数据分析。因为学习仪表板将独立于任何特定领域，并且因为元认知和自我评估是最重要的，所以学习仪表板和关于设计有效学习仪表板的知识应该适用于各个学科和班级。该提案将已知的学习、元认知和智能辅导系统结合起来，以解决及时的学习分析问题。

This PI team aims to use artificial intelligence to exploit data collected from intelligent tutoring systems to provide feedback both to students and to teachers effectively and at the right times. The team is using a new analytic approach, which introduces hierarchical modeling to learning analytics, to investigate how to better understand students' learning states. Algorithms make valid interpretable and actionable inferences from student-learning data, drawing on cognitive theories and statistics to make it work. As in tutoring systems, analysis is at the level of component skills rather than looking at end performance on a task as a whole. Research is around construction of the algorithms for deducing student learning and student learning states and around learning ways of signaling both to learners and to their teachers what concepts and skills learners understand and are capable of and which they are having trouble with. A learning dashboard will allow teachers to visualize the learning needs of a whole class and adapt activities to student needs. Feedback aimed at learners themselves will help learners recognize activities they need to engage in next to better their skills or understanding. Evaluation will include the degree to which learners development of metacognitive skills when such tools are available. The proposed work will contribute towards the next generation of intelligent tutoring systems as well as contribute to the data analytics needed to make use of large-scale educational data repositories. Because the Learning Dashboard will be independent of any particular domain, and because metacognition and self-assessment are foregrounded, the Learning Dashboard and what is learned about designing an effective learning dashboard should be applicable across disciplines and classes. The proposal brings together what is known about learning, metacognition, and intelligent tutoring systems to address timely learning analytics issues.