ABSTRACT
Understanding how students solve computational problems is central to computer science education research. This goal is facilitated by recent advances in the availability and analysis of detailed multimodal data collected during student learning. Drawing on research into student problem-solving processes and findings on human posture and gesture, this poster utilizes a multimodal learning analytics framework that links automatically identified posture and gesture features with student problem-solving and dialogue events during one-on-one human tutoring of introductory computer science. The findings provide new insight into how bodily movements occur during computer science tutoring, and lay the foundation for programming feedback tools and deep analyses of student learning processes.
Index Terms
- Modeling student programming with multimodal learning analytics (abstract only)
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