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Record W2194443509 · doi:10.5539/ass.v11n27p84

English as Medium of Instruction for Subject Courses in Tertiary Education: Lessons Learned from Thai Undergraduate Students

2015· article· en· W2194443509 on OpenAlexvenueno aff
Pennee Kantavong

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationAction researchComprehensionSubject (documents)PsychologyAction (physics)English languagePositive attitudeMedium of instructionEnglish as a foreign languagePedagogyComputer science

Abstract

fetched live from OpenAlex

This is a classroom action research project conducted in an undergraduate course using English as a medium of instruction. The objectives were to identify good approaches to developing students’ capacity to understand English and to promote a positive attitude towards the English language. Two cycles of action with various instructional approaches were administered. By the end of the action research cycles, 80% of the students showed a positive opinion towards learning with English. During the sessions, 20% of the students needed help regularly. The cooperative learning model was found to be one of the more effective leaning activities, stimulating students to develop their learning and relieve their tensions in using a foreign language. The reflections from students revealed that the learning approaches employed in this study helped students to develop both their English comprehension in the written material and a positive attitude towards learning in English.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.342
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2015
Admission routes1
Has abstractyes

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