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Record W2292647392 · doi:10.5539/ies.v9n2p42

Korean College EFL Learners’ Task Motivation in Written Language Production

2016· article· en· W2292647392 on OpenAlexvenueno aff
Bomin Kim, Haedong Kim

Bibliographic record

VenueInternational Education Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
FundersHankuk University of Foreign Studies
KeywordsCohesion (chemistry)PsychologySophisticationForeign languageTask (project management)Lexical diversityTask analysisMathematics educationQuality (philosophy)LinguisticsCognitive psychologyVocabulary

Abstract

fetched live from OpenAlex

<p>The aims of the present study were to explore the effects of two different types of task conditions (topic choice vs. no choice) on the quality of written production in a second language (lexical complexity, syntactic complexity, and cohesion) and to investigate the effects of these two different task conditions on task motivation. This research was conducted by means of a task motivation questionnaire and a collation of the writings of 31 Korean college students learning English as a foreign language. The data was analyzed using Coh-Metrix 3.0. The major findings were as follows: 1) The writings of participants in the topic choice condition were better than those in the no-choice condition in terms of lexical sophistication and temporal cohesion. However, participants’ written production in the no-choice condition was better than that in the topic choice condition in terms of syntactic complexity. 2) The participants’ task motivation levels were higher for the perceived choice domain in the topic choice condition than in the no-choice condition. These findings should help L2 writing instructors, materials developers, and researchers to design L2 writing instruction with a focus on written production specifically for Korean college-level learners.</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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.434
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.084
GPT teacher head0.455
Teacher spread0.370 · 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 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

Citations9
Published2016
Admission routes1
Has abstractyes

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