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Record W2430696731 · doi:10.4236/jss.2016.46014

An Investigation of Academic Preparation 5 Students’ and Instructors’ Preference of ESL Writing Feedback

2016· article· en· W2430696731 on OpenAlexaffabout
Madelaine Campbell

Bibliographic record

VenueOpen Journal of Social Sciences · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsPreferenceMathematics educationPsychologyAcademic yearSteering committeePedagogyEngineeringMathematicsEngineering management

Abstract

fetched live from OpenAlex

The study examines AP5 students’ and instructors’ attitudes toward ESL writing feedback. The survey research took place in the English Learning Centre (ELC) at Vancouver Island University (VIU) in the Spring of 2016. VIU is a small degree granting university located in Nanaimo, on Vancouver Island. The English Learning Centre is part of the Faculty of International Education, and ESL students come here to study pre-academic English language skills in order to become ready for their university courses. There are approximately 200 students enrolled in our ELC. The survey results showed an equal preference for typed and handwritten feedback, with female students preferring hand written, and other forms of feedback while male students prefer typed feedback. The instructors surveyed prefer giving feedback orally.

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)0.004
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.166
GPT teacher head0.393
Teacher spread0.227 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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