An Examination of Using Self-, Peer-, and Teacher-Assessment in Higher Education: A Case Study in Teacher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it