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abstract

Comparison Activities to Support Students' Self-Regulated Learning in Computing Education

Published:05 March 2021Publication History

ABSTRACT

Comparison activities, such as comparing a code against peers' code, rubric, exemplar or other are found to be an effective teaching strategy for building programming skills. Comparison activities support self-regulated learning through a process of abstraction. That is, students are facilitated to engage in a conversation with their work and produce inner feedback that becomes the raw material for their learning and development [1]. This workshop is designed to support educators in their designing and implementing effective comparison activities and administering the process to facilitate student self-regulation. This highly interactive and collaborative workshop will provide participants with materials and ideas that are immediately useable and applicable to a wide variety of teaching practices. In particular, we guide participants in their developing insights about comparison activities for students self-regulated learning; we describe and discuss ubiquitous, implicit comparison opportunities, and facilitate participants identifying concrete opportunities for explicitly implementing comparison activities within their teaching practice; we introduce participants to a framework that guides their decisions on design and implementation; and provide case examples. These segments will be interspersed with reflective and experiential exercises to highlight the benefits and processes of comparison-based internal feedback. Comparison activities can be deployed across all learning settings including in classroom settings, individual and group assignments, and online tasks and interactions.

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  1. Comparison Activities to Support Students' Self-Regulated Learning in Computing Education

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    • Published in

      cover image ACM Conferences
      SIGCSE '21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education
      March 2021
      1454 pages
      ISBN:9781450380621
      DOI:10.1145/3408877

      Copyright © 2021 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.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 5 March 2021

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      Overall Acceptance Rate1,595of4,542submissions,35%

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