Teachers' Perceived English Proficiency and Self-Efficacy of English Teachers in the Academic World
Bibliographic record
Abstract
This research used descriptive correlational design to find the significant relationship between perceived English proficiency and self-efficacy of English teachers. The respondents of the study were 142 elementary teachers teaching English in Candelaria West District. It was conducted during the third quarter of SY 2022-2023. Majority of the respondents are within 26-30 and 41-45 years of age, mostly are female, majority of them are in the service for less than five years, with Bachelor Degree, graduate of Education but non-English major, and majority of them use English every day outside the classroom. The respondents use English in a week mainly in teaching an English class. The respondents perceived reading as their most proficient macro skills and speaking, their least proficient. They perceived general English proficiency, speaking skill, and writing skill in the proficient level while listening and reading skills in the highly proficient level. The respondents perceived efficacy to decision making, efficacy to influence resources, instructional efficacy, and efficacy to parental involvement, community and stakeholders in some influence level. Meanwhile, disciplinary selfefficacy and efficacy to create a positive school climate in a great deal level. When it is correlated the results revealed that majority of the self-perceptions in English proficiency are correlated to self-efficacy. However, certain variables found to have no significant relationship to each other. This includes the general English proficiency and the four macro skills to efficacy to influence decision making, efficacy to influence resources and efficacy to parental involvement, community and stakeholders both for writing skills
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".