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Intelligent Instructional Hand Offs

2017; EDM; Fanscal, S.E.; Yudelson, M.V.; Berman, S.R.

Learners in various contemporary settings (e.g., K-12 classrooms, online courses, professional/vocational training) find themselves in situations in which they have access to multiple technology-based learning platforms and often one or more non-technological resources (e.g., human instructors or on-demand human tutors). Instructors, similarly, find themselves in situations in which they can provide learners with a variety of options for instruction, practice, homework, and other activities. We sought data-driven guidance to help facilitate intelligent instructional “hand offs” between learning resources. To begin this work, we focused on an important element of self-regulated learning, namely help seeking. We built classifier models based on proxies for learner prior knowledge and data-driven inferences about learners’ disengaged behavior (e.g., gaming the system) and affective states (e.g., confusion). This led us to determine the extent to which (and when) learners tended to seek out help via human tutoring while using an intelligent tutoring system for mathematics. Insights into cognitive, behavioral, and affective factors associated with help seeking outside of a system will drive future work into providing automated, intelligent guidance to both learners and instructors. The paper closes with discussion of the limitations of the present analysis and avenues for future work on intelligently guiding instructional hand offs.