Online Student Peer-Assessment in Higher Education: A Systematic Review of the Literature
Bibliographic record
Abstract
Peer-assessment is an active process of socially mediated learning that can enhance student learning and metacognitive abilities while developing skills required for success in the modern world. The process has been explored in previous reviews and shown to be valuable through in-person applications. However, a comprehensive review of the literature focusing on online higher education applications has yet to be completed. Our purpose was to conduct a systematic review of the literature on peer-assessment in online higher education classes. Guided by the PRISMA framework, we used a mixed-method integrated methodology to review and synthesize 66 peer-reviewed empirical quantitative, qualitative, and mixed-methods studies published between 2008 and 2023. Following the research context and insight regarding instructional design, two themes emerged: academic impact and student comfort. We identify eight limitations and five recommendations for further research at the end of the paper. The results reflect the context of use along with benefits and challenges related to perceptions of learning, motivation, academic achievement, quality, anonymity, open identification, and time. We provide further context and recommendations for implementation in the discussion section.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".