MétaCan
Menu
Back to cohort
Record W2914598325 · doi:10.5539/ijel.v9n2p30

Classroom Sources of English Language Anxiety: A Study of Fresh Engineering Students at Mehran UET, Pakistan

2019· article· en· W2914598325 on OpenAlexaffvenue
Illahi Bux, Rafique Ahmed Memon, Shabana Sartaj, Jahangir Bhatti, Abdul Sattar Gopang, Noor Muhammad Angaria

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
FundersUniversity of Sindh
KeywordsCommunication apprehensionAnxietyFear of negative evaluationPsychologyEnglish languagePresentation (obstetrics)ApprehensionForeign languageForeign language anxietyTest anxietyMedical educationMathematics educationClinical psychologyMedicineCognitive psychology

Abstract

fetched live from OpenAlex

The current study investigated anxiety-provoking classroom sources among undergraduate engineering students at Mehran UET, Pakistan. In this study, the participants (female 105 male 116) participated. The data were collected via questionnaire FLCAS and semi-structured interviews. The objective of this study was to identify classroom sources of anxiety quantitatively and qualitatively. The findings of study suggested some main sources of anxiety among fresh engineering students: (1) test anxiety; (2) apprehension in communication; (3) fear of negative evaluation; (4) presentation in English; (5) English language instructor; (6) competitiveness in English language; (7) negative self-evaluation; (8) individual tasks in classroom; (9) fear of making errors in English classroom; (10) English language difficulties. The participants have self-reported the main sources of anxiety in English language learning. These sources also confirm previous research on anxiety-causing sources among foreign language learners.

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)0.001
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.001
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 head0.338
Teacher spread0.324 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of English LinguisticsSame topicEducational Strategies and EpistemologiesFrench-language works237,207