Environmental Factors for the Advancement of Teachers’ Self-Efficacy in Professional Development
Bibliographic record
Abstract
There is a shifting paradigm in gifted education from person-based approaches (i.e., identifying giftedness) to process-based approaches (i.e., transacting giftedness). This new framework is centered on enriching educational opportunities that will make the process meaningful (i.e., gifted) to everyone in a setting. However, little is known about how this renewed perspective can be applied in teacher professional development. In line with the socio-ecological models, our study aims to identify the best appropriate model to describe teacher self-efficacy (i.e., the dependent variable in the study) as professional development from an ecological perspective and to propose an ecologically intelligent school (EIS) for the advancement of self-efficacy. Structural equation modeling (SEM) was performed to create a model using TALIS 2018 dataset. Afterward, indices of goodness-of-fit criteria were examined for each model. The results indicate that there is a complex ecological background, in that various factors affect the dependent variable. Model 3 was determined as the most suitable model that can be proposed as an ecologically intelligent school (EIS) for the advancement of self-efficacy. The factors within the three layers of the socio-ecological model-communication with teachers, communication with students, school climate, and feeling valued by the national level-altogether created an appropriate model explaining teacher professional development, regarding self-efficacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".