PIs: Marsha Lovett, Christopher Genovese
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.