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Modeling student programming with multimodal learning analytics (abstract only)

Published:06 March 2013Publication History

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.

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  1. Modeling student programming with multimodal learning analytics (abstract only)

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        • Published in

          cover image ACM Conferences
          SIGCSE '13: Proceeding of the 44th ACM technical symposium on Computer science education
          March 2013
          818 pages
          ISBN:9781450318686
          DOI:10.1145/2445196

          Copyright © 2013 Copyright is held by the owner/author(s)

          Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 6 March 2013

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          • poster

          Acceptance Rates

          SIGCSE '13 Paper Acceptance Rate111of293submissions,38%Overall Acceptance Rate1,595of4,542submissions,35%

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