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

EXP：构建学习分析系统以改善学生学习并促进跨多个领域的适应性教学

• 批准号: 1216977
• 负责人: Marsha Lovett
• 金额: $ 49.63万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216977&HistoricalAwards=false
• 关键词: EXP; Building; Learning; Analytics; System

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.

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，该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。算法从学生学习数据中做出有效的可解释和可操作的推断，利用认知理论和统计数据使其工作。在辅导系统中，分析是在组件技能的水平，而不是在整个任务的最终表现。研究是围绕构建的算法推导学生的学习和学生的学习状态和学习方式的信号都向学习者和他们的老师学习者理解什么概念和技能，有能力，他们有麻烦。一个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。针对学习者本身的反馈将帮助学习者认识到他们下一步需要从事的活动，以提高他们的技能或理解。评估将包括学习者在这些工具可用的情况下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存储库所需的数据分析。因为学习仪表板将独立于任何特定的领域，并且因为元认知和自我评估是前景化的，所以学习仪表板以及关于设计有效学习仪表板的知识应该适用于跨学科和跨课程。该提案汇集了关于学习，元认知和智能辅导系统的知识，以解决及时的学习分析问题。