Work

Assessing Reviewer's Performance Based on Mining Problem Localization in Peer-Review Data.

Year: 2010

Type: book-chapter

Abstract: . Current peer-review software lacks intelligence for responding to students ’ reviewing performance. As an example of an additional intelligent assessment component to such software, we propose an evaluation system that generates assessment on reviewers ’ reviewing skills regarding the issue of problem localization. We take a data mining approach, using standard supervised machine learning to build classifiers based on attributes extracted from peer-review data via Natural Language Processing techniques. Our work successfully shows it is feasible to provide intelligent support for peer-review systems to assess students ’ reviewing performance fully automatically. 1

Source: D-Scholarship@Pitt (University of Pittsburgh)

Authors: Wenting Xiong, Diane Litman, Christian D. Schunn

Institution: University of Pittsburgh

Language: English

Topic: Online Learning and Analytics

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: green

OpenAlex ID: W1501335461

FWCI: 20.17

Cites: 8

Cited by: 35

Related to: 10