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Record W2575300346 · doi:10.5539/elt.v10n2p73

Effect of Foreign Language Anxiety on Gender and Academic Achievement among Yemeni University EFL Students

2017· article· en· W2575300346 on OpenAlexvenueno aff
Norizan Abdul Razak, Amr Abdullatif Yassin, Tengku Nor Rizan Tengku Mohamad Maasum

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAnxietyStratified samplingSignificant differenceTest anxietyAcademic achievementForeign languageTest (biology)PopulationPearson product-moment correlation coefficientMathematics educationClinical psychologyDemographyStatisticsSociology

Abstract

fetched live from OpenAlex

This study aimed to investigate the gender differences in terms of anxiety among Yemeni university EFL learners. It also aimed to investigate the correlation between the level of anxiety and the academic achievement of the students. The participants of this study were 155 students chosen from the population through stratified random sampling. The participants are selected from English Department, Faculty of Arts, Ibb University, Yemen. The data was collected by using a questionnaire adopted from Yassin (2015), and the data was analysed by using 22nd version of the SPSS. The data of the first question was analysed by using T-test and the result of the analysis showed that the females experienced higher level of anxiety than male students, but the difference between both groups is not significant. The second question was analysed by using Pearson Product Moment Correlation and the result showed that there is not significant correlation between the level of anxiety and the academic achievement of the students.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.282
Teacher spread0.266 · 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

Citations48
Published2017
Admission routes1
Has abstractyes

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