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Automatically Generated Feedback for CS student Work: Best Practices (Abstract Only)

Published: 24 February 2015 Publication History

Abstract

This session invites educators interested in sharing and/or learning about experiences with tools for automatic feedback on technical work: the "if", "why" and "how". This includes experiences with program testing, problem-solving exercises, or quizzes, generated or checked with engines with expert-level technical capabilities, to scale up feedback to cope with burgeoning enrollment in CS courses while maintaining or improving student learning outcomes. Commercial, free and open-source tools now exist to assist in this endeavor.
The benefits of providing timely, detailed, and insightful feedback for student effort are well known. Yet as enrollment in CS courses increases, many are hard pressed to find the human resources to scale up their feedback efforts. TAs and instructors may struggle to deal with the increased evaluation load, resulting in inconsistent, untimely, or lessened insight in feedback.
Automatic grading and feedback offer a way to scale detailed and individualized feedback and for instructors to write materials with enhanced shelf-life, but with additional courseware engineering and administrative cost. It raises pedagogical questions about good ways for machine-based feedback to be blended with other types of learning activities, both conventional and novel. It has significance and relevance to departments facing the "scaling up" situation when it is understood if, how and why it can help address the problem.

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cover image ACM Conferences
SIGCSE '15: Proceedings of the 46th ACM Technical Symposium on Computer Science Education
February 2015
766 pages
ISBN:9781450329668
DOI:10.1145/2676723
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: 24 February 2015

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Author Tags

  1. autograding
  2. automatic feedback
  3. scaling instruction

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SIGCSE '15
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SIGCSE '15 Paper Acceptance Rate 105 of 289 submissions, 36%;
Overall Acceptance Rate 1,787 of 5,146 submissions, 35%

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The 56th ACM Technical Symposium on Computer Science Education
February 26 - March 1, 2025
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