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What Predicts Variation in Reliability and Validity of Online Peer Assessment? A Large-Scale Cross-Context Study

Authors :
Xiong, Yao
Schunn, Christian D.
Wu, Yong
Source :
Journal of Computer Assisted Learning. 2023 39(6):2004-2024.
Publication Year :
2023

Abstract

Background: For peer assessment, reliability (i.e., consistency in ratings across peers) and validity (i.e., consistency of peer ratings with instructors or experts) are frequently examined in the research literature to address a central concern of instructors and students. Although the average levels are generally promising, both reliability and validity can vary substantially from context to context. Meta-analyses have identified a few moderators that are related to peer assessment reliability/validity, but they have lacked statistical power to systematically investigate many moderators or disentangle correlated moderators. Objectives: The current study fills this gap by addressing what variables influence peer assessment reliability/validity using a large-scale, cross-context dataset from a shared online peer assessment platform. Methods: Using multi-level structural equation models, we examined three categories of variables: (1) variables related to the context of peer assessment; (2) variables related to the peer assessment task itself; and (3) variables related to rating rubrics of peer assessment. Results and Conclusions: We found that the extent to which assessment documents varied in quality on the given rubric played a central role in mediating the effect from different predictors to peer assessment reliability/validity. Other variables that are significantly associated with reliability and validity included: Education Level, Language, Discipline, Average Ability of Peer Raters, Draft Number, Assignment Number, Class Size, Average Number of Raters, and Length of Rubric Description. The results provide information to guide practitioners on how to improve reliability and validity of peer assessments.

Details

Language :
English
ISSN :
0266-4909 and 1365-2729
Volume :
39
Issue :
6
Database :
ERIC
Journal :
Journal of Computer Assisted Learning
Publication Type :
Academic Journal
Accession number :
EJ1399761
Document Type :
Journal Articles<br />Reports - Research
Full Text :
https://doi.org/10.1111/jcal.12861