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Scaling Classroom Education with Pee...

Yuan, Zheng.

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Scaling Classroom Education with Peer Review: A Natural Language Processing Approach.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Scaling Classroom Education with Peer Review: A Natural Language Processing Approach./
Author:	Yuan, Zheng.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	103 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
Contained By:	Dissertations Abstracts International82-01B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27995169
ISBN:	9798662378391

Scaling Classroom Education with Peer Review: A Natural Language Processing Approach.
Yuan, Zheng.

Scaling Classroom Education with Peer Review: A Natural Language Processing Approach. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 103 p.

Source: Dissertations Abstracts International, Volume: 82-01, Section: B.

Thesis (Ph.D.)--Northwestern University, 2020.

This item must not be sold to any third party vendors.

Peer review is a commonly used tool to manage large classes. It allows students to grade and provide feedback to each other based on rubrics provided by instructors. Peer review has been proved to be effective in improving students' learning outcomes by many research. During providing peer review, students are exposed to more solutions to the same question, which makes students more innovative. Also, peer review reduces instructors' or TAs' workload on providing feedback despite teaching big classes.However, peer review still has shortcomings. First, peer review increases students' workload on reviewing other submissions. To collect enough peer reviews for submissions, each peer usually needs to review several submissions per homework. Some peers may spend more time on peer reviewing than finishing the homework. Second, since peers typically lack the subject matter mastery of the instructor, peer grades exhibit both bias and variance, which makes consensus grade estimation a challenging task.This dissertation addresses these limitations of peer review using Natural Language Processing techniques. Specifically, this dissertation proposes novel neural models that predict important parts of submissions. By doing so, peers can save time by only focusing on the essential parts. Our models take advantage of textual reviews and review labels to improve prediction accuracy. This dissertation also proposes novel peer grading methods, which enhance peer grading accuracy by using historical instructor grades to estimate peer bias, and textual review comments to estimate review quality.

ISBN: 9798662378391Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Highlighted text prediction

Scaling Classroom Education with Peer Review: A Natural Language Processing Approach.
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500 $a Source: Dissertations Abstracts International, Volume: 82-01, Section: B.
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506 $a This item must not be sold to any third party vendors.
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