Teacher Induction Program: First Experience in Turkey
Bibliographic record
Abstract
Perspectives on beginning teachers’ possible problems and their reasons force many of the countries to develop teacher induction programs. Teacher induction programs are extensive, consistent and ongoing professional processes aiming to train, support, and protect novice teachers. In Turkey, the process of “teacher induction program” which has been initiated in 2016 is a regulation to train novice teachers for six months. The rationale of the program for the novice teachers is to have more practical experience and in turn to teach effectively in their classrooms. As each new regulation can be accompanied by some obscurities, scientific research will serve to increase the quality of the processes and practices in teacher induction programs. In this sense, the current research aims to determine the views of 357 novice teachers on the goal achievement of the teacher induction program. A questionnaire form was developed to collect the data consisting of 43 questions one of which is an open-ended question. Results suggested that preservice education and teacher induction program have similar contributions in regard to the goal achievement of the teacher induction program. Moreover, considering the process as a whole, the mentor has an important role in the development of novice teachers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".