A Systematic Review of Technology-Supported Peer Assessment Research
Bibliographic record
Abstract
With the advancement of information and communication technologies, technology-supported peer assessment has been increasingly adopted in education recently. This study systematically reviewed 134 technology-supported peer assessment studies published between 2006 and 2017 using a developed analysis framework based on activity theory. The results found that most peer assessment activities were implemented in social science and higher education in the past 12 years. Acting assignments such as performance, oral presentations, or speaking were the least common type of assignments assessed across the studies reviewed. In addition, most studies conducted peer assessment anonymously and assessors and assessees were randomly assigned. However, most studies implemented only one round of peer assessment and did not provide rewards for assessors. Across studies, it was more often the case that students received unstructured feedback from their peers than structured feedback. Noticeably, collaborative peer assessment did not receive enough attention in the past 12 years. Regarding the peer assessment tools, there were more studies that adopted general learning management systems for peer assessment than studies that used dedicated peer assessment tools. However, most tools used within these studies only provide basic functionalities without scaffolding. Furthermore, the results of cross analysis reveal that there are significant relationships between learning domains and anonymity as well as learning domains and assessment durations. Significant relationships also exist between assignment types and learning domains as well as assignment types and assessment durations.
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 | 0.019 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".