Implementation of Microteaching in Special Teaching Methods I And II Courses: An Action Research
Bibliographic record
Abstract
The aim of this study is to implement micro teaching method in Special Teaching Methods I and II courses in order to teach preservice teachers how to make lesson plans and help them gain experiences in classroom management issues; i.e. to improve their teaching skills. The study was designed according to the principles of action research. Pre-service teachers carried out micro teaching sessions during the fall and the spring term as a requirement of Special Teaching Methods I and II courses. The research process started at the beginning of each term. In the first 7 weeks, the instructor provided preservice teachers with theoretical knowledge about special teaching methods. The practice phase started after the mid-term exams, in which the participants were divided into groups and taught lessons on predetermined topics by using micro teaching methods. The teaching practices were video recorded. The data of the study were obtained from video recordings of the micro teaching sessions, the semi-structured interviews conducted with the participants and the learner diaries. A total of 40 preservice teachers participated in the study; however, the data from 10 participants were used in the analysis. The results revealed that preservice teachers gained experience in teaching and improved their teaching, classroom management, and lesson plan preparation skills thanks to this implementation.
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".