Evaluating the English Proficiency of Faculty Members of a Higher Education Institution: Using Results to Develop Responsive Professional Development Program
Bibliographic record
Abstract
Current literatures reveal that English proficiency of Filipino workforce has declined through the years. The untrained and non-proficient teachers are heavily blamed on this pressing concern. With the aim of addressing the leading cause of the problem, this study investigated the level of English proficiency of faculty members of a higher education institution in the Philippines and proposed a program that could reverse the alarming trend. Utilizing mixed methods research design with 41 full-time faculty members as samples, this study found that majority of the teachers are in B1 and B2 levels (Intermediate and Upper Intermediate). In terms of specific language skill, writing is the lowest with majority of the teachers placed in A1 and A2 levels (Basic Users). Results of the study suggest that faculty members need to undergo several language enhancement courses such as Effective Communication, Academic and Professional Communication, Academic Writing with Research, and Effective Business and Report Writing, while the higher education institution involved in this study needs to support teachers in their formal higher studies, participation in workshops and trainings, publishing in scholarly journals, and serving as speakers or presenters in various academic forums. Discussion points that arise include implications of the findings and required actions from stakeholders. The study concludes with its limitations and important recommendations.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".