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.

Creator

• Yuan, Zheng

DOI

• https://doi.org/10.21985/n2-p864-ae21

Subject

• Computer science

Language

• en

Alternate Identifier

• etdadmin_upload_744729
• http://dissertations.umi.com/northwestern:15148

Keyword

• peer review
• peer grading
• highlighted text prediction
• open domain named entity typing
• natural language processing

Date created

• 2020-01-01

Resource type

• Dissertation

Rights statement

• In Copyright