Investigation of the Self-Efficacy Beliefs of Turkish Language and Literature Teachers in Practicing Constructivist Approach in Terms of Various Variables
Bibliographic record
Abstract
It is a scientifically and developmentally undeniable reality that the educational activities that guide education and training activities and aim to raise the nation according to the requirements of the age and keep up with the necessary arrangements for this purpose. With the periodic developments in the world, the transformation in the philosophy of education and understanding of education has brought along the application of new approaches and understandings in education. The constructivist approach that started to be applied in education with these developments is also one of the new educational approaches. Constructivist understanding is defined as a process in which students are actively involved in educational activities and new information is built on pre-learning. Constructivism is a contemporary understanding that covers all kinds of practices that the student can actively engage in the learning process, and it emphasizes that education can be successful to the extent that it can serve individual differences. It has been fifteen years since the practices on constructivism started to be implemented in our country. During this period and as a point reached, it is a question of how much this understanding is applied. With this research, it is aimed to examine the self-efficacy beliefs of Turkish language and literature teachers towards applying constructivist approach in terms of various variables.
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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 | 0.004 | 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".