Using Social Media for Peer Assessment in Higher Education: A Systematic Review of the Literature
Bibliographic record
Abstract
The modern affordances of technology and social media networks' popularity enable unique educational opportunities. In this systematic review, our objective was to outline insights regarding the current use of social media for peer assessment in higher education. Specifically, what does the current research indicate are common characteristics, benefits, and challenges, and how can they guide future research and practice? We searched the OMNI information consortium consisting of 392 databases to gain insights. From 2,450 identified articles, we included 12 consisting of 702 participants in our review. The included articles are empirical and peer-reviewed, focusing on higher education and retrieved through the OMNI information consortium. The results were synthesized through a three-step integrated approach to afford the qualitative assimilation of our findings. Facebook and YouTube were the most commonly used platforms, while educational studies used social media for peer assessment most often. The articles referenced in our review primarily used mixed methods approaches and were of medium quality. We found benefits associated with attaining learning objectives while fostering the co-creation of knowledge, self-awareness, and motivation. In contrast, educators may encounter challenges with implementing peer assessment through social media related to technology issues and student behaviours. We outline further insights into our findings and practical recommendations in our discussion.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".