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A Dashboard to Provide Instructors with Automated Feedback on Students’ Peer Review Comments

Published: 13 March 2023 Publication History

Abstract

Writing-to-Learn (WTL) is an evidence-based instructional practice which can help students construct knowledge across many disciplines. Though it is known to be an effective practice, many instructors do not implement WTL in their courses due to time constraints and inability to provide students with personalized feedback. One way to address this is to include peer review, which allows students to receive feedback on their writing and benefits them as they act as reviewers. To further ease the implementation of peer review and provide instructors with feedback on their students’ work, we labeled students’ peer review comments across courses for type of feedback provided and trained a machine learning model to automatically classify those comments, improving upon models reported in prior work. We then created a dashboard which takes students’ comments, labels the comments using the model, and allows instructors to filter through their students’ comments based on how the model labels the comments. This dashboard can be used by instructors to monitor the peer review collaborations occurring in their courses. The dashboard will allow them to efficiently use information provided by peers to identify common issues in their students’ writing and better evaluate the quality of their students’ peer review.

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Cited By

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  • (2024)SLADE: A Method for Designing Human-Centred Learning Analytics SystemsProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636847(24-34)Online publication date: 18-Mar-2024
  • (2024)Automated Text Analysis of Organic Chemistry Students’ Written HypothesesJournal of Chemical Education10.1021/acs.jchemed.3c00757101:3(807-818)Online publication date: 15-Feb-2024
  • (2023)Implementation of an R Shiny App for Instructors: An Automated Text Analysis Formative Assessment Tool for Evaluating Lewis Acid–Base Model UseJournal of Chemical Education10.1021/acs.jchemed.3c00400100:8(3107-3113)Online publication date: 27-Jul-2023

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        cover image ACM Other conferences
        LAK2023: LAK23: 13th International Learning Analytics and Knowledge Conference
        March 2023
        692 pages
        ISBN:9781450398657
        DOI:10.1145/3576050
        Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 13 March 2023

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        Author Tags

        1. Writing-to-Learn
        2. automated feedback
        3. instructor dashboards
        4. machine learning
        5. peer review

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        View all
        • (2024)SLADE: A Method for Designing Human-Centred Learning Analytics SystemsProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636847(24-34)Online publication date: 18-Mar-2024
        • (2024)Automated Text Analysis of Organic Chemistry Students’ Written HypothesesJournal of Chemical Education10.1021/acs.jchemed.3c00757101:3(807-818)Online publication date: 15-Feb-2024
        • (2023)Implementation of an R Shiny App for Instructors: An Automated Text Analysis Formative Assessment Tool for Evaluating Lewis Acid–Base Model UseJournal of Chemical Education10.1021/acs.jchemed.3c00400100:8(3107-3113)Online publication date: 27-Jul-2023

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