Microteaching and Peer Assessment in Mathematics Teaching Practice
Bibliographic record
Abstract
Considering the characteristics that teachers should have, concepts such as critical thinking, active learning, taking responsibility, evaluation, analysis, self-evaluation and reflective thinking are important. In this context, micro-teaching and peer assessment methods come to the fore. Micro-teaching is defined as sharing a part of the course with peers and receiving feedback from peers or advisors. Peer assessment, on the other hand, involves providing constructive suggestions among peers, reviewing and giving feedback according to predetermined criteria. In the study, pre-service teachers' views on micro-teaching and peer assessment methods were evaluated. The study was designed as a case study, one of the qualitative research methods. The participants of the study consisted of eight pre-service mathematics teachers studying in the Department of Elementary Mathematics Teaching at a university in Turkey. Questionnaires about microteaching and peer assessment were used as data collection tools. A 14-week microteaching and peer assessment implementation process was carried out with the participants. As a result of the applications, pre-service mathematics teachers' opinions about these concepts were obtained. As a result, the pre-service teachers stated that the application provided positive developments in their cognitive, affective and psychomotor behaviors and that they gained professional experience.
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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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".