Exploring Teacher Candidates’ Attitudes towards Pedagogical Teacher Training Based on Different Variables
Bibliographic record
Abstract
The raising of the life standards of individuals living within a society is only possible through the provision of quality education. Quality education can be realized only through the training of teachers with the necessary skills and positive attitudes towards the training provided at faculties of education and through teacher training programs. The purpose of this study is to compare the attitudes of teacher candidates enrolled on teacher training programs towards this type of education with their views concerning this model of education within the context of numerous variables factors. The participants in this study, that was designed according to a descriptive model, consisted of an appropriate sample of 202 teacher candidates from the teacher training program program at a state university situated in Istanbul. The data of the research were collected using “an attitude scale with regard to teacher training” and “an identification scale of the level of support provided to become a teacher”. The data were analyzed using a Wilcoxon Signed Rank Test, a Spearman Brown Rank Correlation Coefficient (test) and Binary Logistic Regression (method). A positive development was seen to have taken place in the attitudes of teacher trainees from the point prior to embarking on Teacher Training to completion of the training. Teacher candidates were of the view that teacher training contributed only slightly to the acquisition of skills relating to the learning and teaching process, however contributed greatly to development of the skills of being able to recognize diversity (among students) and in those related to class management. In accordance with the logistic regression model, it was realized that the above may represent predictor variables outside the model adopted.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".