Research on Teacher Self-efficacy in Turkey: 2000-2017
Bibliographic record
Abstract
In this study, 48 empirical studies on teacher self-efficacy published between 2000-2017 were reviewed in terms ofcertain basic characteristics, and the research trends were revealed in this area. According to the results obtained inthe review, teacher self-efficacy studies in the Turkish context showed an increase in the course of time. Teacherself-efficacy in a specific area was observed to be frequently examined within the context of teaching a subject orusing technology in education. This increase in the number of studies did not reflect much on the diversity of theresearch methods employed, and the studies were often conducted by using quantitative methods. Elementary andmiddle schools were mostly chosen as the research context. Furthermore, the amount of research on threeself-efficacy areas (i.e. collective teacher efficacy, teacher self-efficacy sources, and teachers’ general self-efficacy)were considerably limited. Moreover, none of the studies examined the relationship between teacher self-efficacyand collective teacher efficacy and student achievement. Common findings of the studies showed that teacherself-efficacy was a strong predictor or a mediator variable. In this sense, researchers are suggested to study howself-efficacy can be developed experimental and longitudinal works and to conduct studies to reveal insights aboutcollective efficacy in schools, teachers’ general self-efficacy, and the outcomes of these efficacy characteristics.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".