Mignemi, Giuseppe, Chen, Yunxiao 
ORCID: 0000-0002-7215-2324 and Moustaki, Irini ORCID: 0000-0001-8371-1251
(2025)
Statistical analysis of peer grading: a latent variable approach.
In: Pollice, Alessio and Mariani, Paolo, (eds.)
Methodological and Applied Statistics and Demography IV: SIS 2024, Short Papers, Contributed Sessions 2.
Methodological and Applied Statistics and Demography IV.
UNSPECIFIED, pp. 263-268.
ISBN 97830316444677
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Text (Statistical_Analysis_of_Peer_Grading)
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Abstract
Peer grading is an educational system in which students assess each other’s work. It is commonly applied under Massive Open Online Course (MOOC) and offline classroom settings. Peer grading data have a complex network structure, where each student is a vertex of the network, and each peer grade serves as an edge connecting one student as a grader to another student as an examinee. We introduce a latent variable model framework for analyzing peer grading data and develop a fully Bayesian procedure for its statistical inference. The proposed approach produces more accurate aggregated grades by modelling the heterogeneous grading behaviour with latent variables and provides a way to assess each student’s performance as a grader. It may be used to identify a pool of reliable graders or generate feedback to help students improve their grading. Thanks to the Bayesian approach, uncertainty quantification is straightforward when inferring the student-specific latent variables as well as the structural parameters of the model. The proposed method is applied to a real-world dataset.
| Item Type: | Book Section |
|---|---|
| Additional Information: | © 2025 Springer Nature |
| Divisions: | Statistics |
| Subjects: | L Education H Social Sciences > HA Statistics |
| Date Deposited: | 28 Jan 2025 10:12 |
| Last Modified: | 01 Feb 2025 04:35 |
| URI: | http://eprints.lse.ac.uk/id/eprint/127081 |
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