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Record W4297693619

An Investigation of University Students’ Willingness to Communicate in English in Relation to Some Learner Variables

2017· article· en· W4297693619 on OpenAlexaboutno aff
Murat Hişmanoğlu, Fatma Özüdoğru

Bibliographic record

VenueDergiPark (Istanbul University) · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsRelation (database)Mathematics educationPsychologyWillingness to communicateMathematicsSocial psychologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Over the last two decades, scientific studies on willingness to communicate have been carried out in many countries such as America, Canada, England, Germany, Spain, Italy, Japan, China, Iran and Turkey. Despite many studies on willingness to communicate in the world and our country, university students’ willingness to communicate has not been studied by researchers. With this in mind, this study aimed to examine the randomly selected 328 students’ willingness to communicate at a state university in the Western part of our country in relation to some student variables. In the present study, the willingness to communicate scale developed by McCroskey (1992) was used as a data collection instrument. The first part of the scale contained personal information such as age, gender, major, and having direct contact with English-speaking people at the university. In the second part of the scale, there were 20 items measuring students' willingness to communicate in English. However, eight filler items were not analyzed. The results of the study showed that students had moderate WTC in English. While it was found in the study that learner variables such as major and having direct contact with English speaking people had effect on university students’ willingness to communicate in English, learner variables such as age and gender were not found to have effect on their WTC 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 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)0.000
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 score0.325
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.035
GPT teacher head0.243
Teacher spread0.207 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueDergiPark (Istanbul University)Same topicEFL/ESL Teaching and LearningFrench-language works237,207