English Teachers’ Effectiveness and Students’ English Proficiency at Selected Colleges in Dili, East Timor: Input for Enhancement Programs
Bibliographic record
Abstract
This research is designed to determine the relationship between English teachers’ effectiveness and students’ English proficiency at Selected Colleges in Dili, East Timor, as input for enhancement programs. The English teachers’ effectiveness consist of three aspects namely (1) English Proficiency, (2) Pedagogical Knowledge, and (3) Socio-Affective Skills. Meanwhile, the students’ English proficiency was measured in terms of the following aspects: (1) Listening, (2) Writing, (3) Reading, and (4) Speaking. The collected data is analyzed by using the Statistical Package for Social Sciences whereby charts, figures and tables were used to present the findings. The overall mean for English Teachers’ Effectiveness is 3.64, interpreted as effective. This implies that English Teachers at Selected Colleges demonstrate the effectiveness required of them, but it is in Socio-Affective Skills that they are performing best. The overall mean for Student Respondents’ Level of English Proficiency is 3.85, interpreted as good. Observing closely, the mean score of speaking which is 3.51 is close to “Average” (down) because in the Range of Mean Values of 2.51 - 3.50 is Average. The results of the Pearson Correlation showed that all independent variables on English teachers’ effectiveness are correlated with the dependent variable on students’ English proficiency. The correlation coefficient values are between of ± 0.50 to ± 0.74, which means “High or Strong Relationship”. Based on a reasonable output of this study, the researcher proposed training programs to enhance the English teachers’ effectiveness and students’ English proficiency in Selected Colleges in Dili, East Timor.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".