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Record W2189526874 · doi:10.5539/elt.v9n1p12

Program Evaluation of the English Language Proficiency Program for Foreign Students A Case Study: University of the East, Manila Campus

2015· article· en· W2189526874 on OpenAlexvenueno aff
Esmaeel Ali Salimi, Mitra Farsi

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyForeign languageAcademic yearGraduate studentsMedical educationCompetence (human resources)Mathematics educationThe artsLanguage proficiencyDegree programPedagogyMedicinePolitical science

Abstract

fetched live from OpenAlex

<p>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.</p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.427
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2015
Admission routes1
Has abstractyes

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