Pre-Service Teachers’ Perceptions with Regard to Teaching-Learning Processes
Bibliographic record
Abstract
Teaching-learning process has a great important medium where pre-service teachers develop experiences and competences. Pre-service teachers are introduced to this process in a professional sense through the school experience course in teacher training. In this process, it is crucial to identify the encountered difficulties and matters. For this purpose, the perceptions of 48 pre-service teachers attending Pedagogical Formation Training at Mardin Artuklu University and taking the school experience course were got. The study group of the current research consists of the 48 pre-service teachers taking the school experience course in 2014-2015 academic year. The qualitative research method based on the data collection instrument involving two questions in the semi-structured interview form was used. The content analysis was conducted to analyze the data. According to the findings, it was revealed that the pre-service teachers observe or encounter 219 matters with regard to teaching and learning process in the school experience course. It was identified that the most encountered reasons are teacher, school, student related reasons and other ones, respectively. The pre-service teachers developed 229 suggestions to overcome these matters. The findings of this research were compared with the ones in the literature. In this regard, teaching-learning process was discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".