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Record W2179733709 · doi:10.5539/elt.v8n12p209

Lowering Foreign Language Anxiety through Self-Regulated Learning Strategy Use

2015· article· en· W2179733709 on OpenAlexvenueno aff
Armineh Martirossian, Anahid Hartoonian

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAnxietyForeign language anxietyCommunication apprehensionTest anxietyFear of negative evaluationForeign languageSelf-regulated learningCognitionApprehensionEnglish as a foreign languageDevelopmental psychologySocial psychologyMathematics educationCognitive psychologySocial anxiety

Abstract

fetched live from OpenAlex

Foreign language classroom anxiety (FLCA) and self-regulated learning strategies (SRLSs) are two important factors that influence language learning process in negative and positive ways respectively. The aim of this study was to explore the relationship between FLCA and SRLSs. To this end, 100 university students majoring in TEFL were selected. For collecting data, Foreign Language Classroom Anxiety Scale (Horwitz, Horwitz, & Cope, 1986) and Motivated Strategies for Learning Questionnaire (Pintrich & De Groot, 1990) were used. To analyze the data, Kendall correlation was run. The results revealed that there is a negative relationship between FLCA (communication apprehension, test anxiety, & fear of negative evaluation) and SRLSs (cognitive strategy use & self-regulation).

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.031
GPT teacher head0.259
Teacher spread0.228 · 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

Citations26
Published2015
Admission routes1
Has abstractyes

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