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Record W4407865758 · doi:10.18806/tesl.v41i2/1411

The Impact of a College EAP Writing Program: Former Students' Perspectives

2024· article· en· W4407865758 on OpenAlexaffvenueabout
Sheila Windle, Leanne Johnny, Valerie Smith

Bibliographic record

VenueTESL Canada Journal · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsAlgonquin College
Fundersnot available
KeywordsMathematics educationPedagogyPsychologyEnglish for academic purposesSociology

Abstract

fetched live from OpenAlex

This paper reports the findings of a small-scale qualitative study aimed at exploring the academic experiences of college students (n=11) who had previously engaged in the EAP program at a mid-sized college in Ontario. The primary objective was to unveil student perspectives on the effectiveness of the EAP writing program, and to determine which skills acquired from the EAP were most helpful in their subsequent academic pursuits. Thematic analysis of focus group data reveals three major skills: conducting online research (citing sources and paraphrasing), paragraph writing and grammar, and one genre (reports) as most efficacious in preparing students for their Programs of Study (POS). Three areas of perceived need are stronger connections to POS via vocabulary and referencing systems, more summarizing, and more collaborative writing. A final emergent theme, “Value of EAP,” comprises students’ descriptions of being empowered and successful in their POS as a result of EAP participation. The implications of these findings for future research on EAP and college-level writing are discussed.

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)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0140.006
Scholarly communication0.0090.003
Open science0.0010.006
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.014
GPT teacher head0.301
Teacher spread0.287 · 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 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

Citations0
Published2024
Admission routes3
Has abstractyes

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