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Concept-Level Knowledge Visualization For Supporting Self-Regulated Learning

Published:07 March 2017Publication History

ABSTRACT

Mastery Grids is an intelligent interface that provides access to different kinds of practice content for an introductory programming course. A distinctive feature of the interface is a parallel topic-level visualization of student progress and the progress of their peers. This contribution presents an extended version of the original system that features a fine-grained visualization of student knowledge on the level of the detailed concepts that are associated with the course. The student model is based on a Bayesian-network which is built using students performance history in the learning activities.

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References

  1. P. Brusilovsky, S. Somyurek, J. Guerra, R. Hosseini, V. Zadorozhny, and P. Durlach. 2016. Open Social Student Modeling for Personalized Learning. IEEE Transactions on Emerging Topics in Computing 4, 3 (2016), 450--461.Google ScholarGoogle ScholarCross RefCross Ref
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  1. Concept-Level Knowledge Visualization For Supporting Self-Regulated Learning

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      • Published in

        cover image ACM Conferences
        IUI '17 Companion: Companion Proceedings of the 22nd International Conference on Intelligent User Interfaces
        March 2017
        246 pages
        ISBN:9781450348935
        DOI:10.1145/3030024

        Copyright © 2017 Owner/Author

        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.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 7 March 2017

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        Acceptance Rates

        IUI '17 Companion Paper Acceptance Rate63of272submissions,23%Overall Acceptance Rate746of2,811submissions,27%

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