A matrix factorization model trained on sparse peer assessment data can predict grades for answers from students who did not participate in grading, reducing the grading burden on students.¶
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Evidence · 1 study · designr2·q2
Currency · newest study 2018 · checked against its sources 2026-10-30 (automated, 1 of 1 entry) · not reviewed by a person
Subclaims¶
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q2 i? A matrix factorization model trained on sparse peer assessment data can predict grades for answers from students who did not participate in grading, reducing the grading burden on students. → Luaces 2018
Evidence¶
Luaces 2018¶
Luaces, O., Díez, J., & Bahamonde, A. (2018). A peer assessment method to provide feedback, consistent grading and reduce students’ burden in massive teaching settings. Computers & Education. https://doi.org/10.1016/j.compedu.2018.07.016
q2 · i? · design · r2
The authors report that their multitask matrix factorization model, trained on peer assessment data from a subset of students, generalizes to grade answers from students outside the peer assessment loop, relieving part of the student body of grading work.
"models learned using the multitask approach can be extended to answers not at all involved in peer assessment. The consequence is that a part of the students can be relieved of the assessment task, thereby reducing the burden on students in processes of this kind."
Discussion¶
Related Claims¶
- A multitask matrix factorization approach that jointly learns grades across multiple assessment labels outperforms a single-task (binary relevance) baseline that learns each label separately. — related
- Peer assessment systems can generate explanatory feedback for students by having graders assess multiple labeled aspects of an answer, not just an overall grade. — related
- AGE reduces teacher grading burden by requiring only 1% of student answers in the UNT dataset to be graded — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
matrix-factorization-predicts-grades-for-nongrading-students |
title |
A matrix factorization model trained on sparse peer assessment data can predict grades for answers from students who did not participate in grading, reducing the grading burden on students. |
description |
A matrix factorization model trained on sparse peer assessment data can predict grades for answers from students who did not participate in grading, reducing the grading burden on students. |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2018 |
source_checked |
2026-10-30 |
generated |
by process:wiki-ingest on 2026-10-30 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — luaces-2018 |
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