The Use of Peers in Assessment for Learning: A Case Study of Trainee Teachers at Bindura University of Science Education (BUSE), Zimbabwe
Bibliographic record
Abstract
The study was an exploration of trainee teachers’ understanding, perceptions of, and confidence in the use of peers in assessment for learning (AfL) at Bindura University of Science Education, Zimbabwe. Trainee teachers were enrolled in a programme that used a blended model of teaching and learning between February and June 2021. Trainees participated in online seminars and peer assessment in a course on curriculum development and completed questionnaire eliciting their attitudes toward peer assessment. A mixed-methods approach using both quantitative and qualitative methodologies was adopted. Quantitative data were analysed using descriptive statistics, mean item scores and the summated scores for the three constructs of confidence, benefits of and threats to peer assessment. Open-ended items were analysed qualitatively and emerging themes were reported. Summated scores of 4, meant trainees had positive attitudes toward peer assessment and believed in numerous benefits of using peer assessment. A summated mean score of 3 for threats to peer assessment meant trainee teachers had neutral views to the construct. Conflicting messages were evident. The same trainees who believed that peer assessment was useful still doubted sincerity of peers and preferred teacher assessment. Further research, using a larger population and sample and interviews to probe doubts in peer assessment, is recommended.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".