An Examination of Using Self-, Peer-, and Teacher-Assessment in Higher Education: A Case Study in Teacher Education
Bibliographic record
Abstract
This study focuses on the process of implementing self-, peer- and teacher-assessment in teacher education in order to examine the ways of applying these assessment practices and specifically aims at finding out the level of agreement among pre-service teachers’ self-, peer- and teacher-assessments of presentation performances. Pre-service teachers’ presentation performances including an application of a teaching method assessed by peers and teacher and also by themselves through criteria based assessment forms. The analysis of the data revealed that there are statistically significant differences among self-, peer- and teacher-assessment scores. Peer-assessment of pre-service teachers’ presentations is found to be significantly higher compared with teacher-assessment and self-assessment. With regard to the comparison of teacher-assessment scores and self-assessment scores, it is revealed that there are no significant differences between teacher- and self-assessments. In teacher training programmes beside summative approach self-, peer- and teacher-assessments can be implemented in a formative way as useful practices in developing more succesful performance, higher confidence, effective presenting skills and essential competencies required for effective teaching.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".