Program Evaluation of the English Language Proficiency Program for Foreign Students A Case Study: University of the East, Manila Campus
Bibliographic record
Abstract
This study on evaluating an English program of studies for foreign students seeking admission to the UE Graduate School attempts to examine the prevailing conditions of foreign students in the UE Graduate School with respect to their competence and competitiveness in English proficiency. It looks into the existing English programs of studies in the College of Arts and Sciences and how it addresses the need for an improved academic performance of the foreign students. This study was conducted in the University of the East, Manila campus, particularly in the Graduate School in three groups. All the three groups of respondents have passed the ELPPFS before their admission to UE Graduate School and was enrolled second semester of 2011-2012 in their respective Master and Doctorate courses. Our results show that the three groups of respondents assess that there are significant positive changes in their academic performance as a result of their training in the ELPPFS program. Moreover, there are significant positive changes in the academic performance of the three groups of respondents as a result of their ELPPFS training . The prevailing conditions of foreign students enrolled in degree programs of UE Graduate School with respect to the level of their academic performance clearly show satisfactory evaluation marks.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".