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Automation for Instruction Enhancing Feedback: (Abstract Only)

Published: 21 February 2018 Publication History

Abstract

Automated feedback needs instructor input to be most effective. The increasing demand in computing education necessitates automated feedback systems for teaching programming. However, most current automated feedback tools do not incorporate instructor input. Great strides are being made with identification and code edit steps for automated student feedback, but tools for instructor crafted feedback are lacking in the field of computing. My research, currently targeted at novice programmers aims to close that gap with a hybrid approach of a teacher in the loop feedback system I facilitate writing instructor feedback delivered to students in an automated fashion to give meaningful, instruction enhancing feedback. I also evaluate these mechanisms in classrooms by measuring learning gains, student perception, and other metrics.

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  1. Automation for Instruction Enhancing Feedback: (Abstract Only)

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    cover image ACM Conferences
    SIGCSE '18: Proceedings of the 49th ACM Technical Symposium on Computer Science Education
    February 2018
    1174 pages
    ISBN:9781450351034
    DOI:10.1145/3159450
    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: 21 February 2018

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    Author Tags

    1. assessment
    2. automated feedback
    3. cs education
    4. cs1
    5. instructional design
    6. knowledge components

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    SIGCSE '18
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    Acceptance Rates

    SIGCSE '18 Paper Acceptance Rate 161 of 459 submissions, 35%;
    Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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