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OVERCOMING FOREIGN LANGUAGE SPEAKING ANXIETY BY USING A LANGUAGE EXCHANGE WEBSITE IN ONLINE LEARNING

2022· article· en· W4221123357 on OpenAlexaff
Emma Martina Pakpahan, Imeldawaty Gultom

Bibliographic record

VenuePROJECT (Professional Journal of English Education) · 2022
Typearticle
Languageen
FieldComputer Science
TopicEnglish Language Learning and Teaching
Canadian institutionsKootenay Association for Science & Technology
FundersDirektorat Jenderal Pendidikan Tinggi
KeywordsForeign language anxietyAnxietyForeign languagePsychologyIndonesianMathematics educationLinguistics

Abstract

fetched live from OpenAlex

The use of website in learning Foreign Language helps the students feel comfortable to speak. The objectives of the research is to investigate the Indonesian students’ self-rated degrees of their foreign language speaking anxiety (FLSA) and the effect of using language exchange website in online learning in overcoming the students’ FLSA. This study examined 100 undergraduate students of 4th and 6th semester majoring English education. Foreign Language Classroom Anxiety Scale (FLCAS) by Horwitz, Horwitz & Cope (1986) was adapted and distributed twice in the beginning of the class to find the state of speaking anxiety and in the end to see whether there is any significance effect after learning speaking by using a language exchange website. Finally, the six participants were chosen for interview. The interview was conducted to get more data about the foreign language speaking anxiety that they feel. It is found that the number of students who felt anxiety decreases after using language exchange website. Keywords: Foreign Language, Speaking Anxiety, Language Exchange Website

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)0.001
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.014
GPT teacher head0.311
Teacher spread0.297 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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