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Record W2916588664 · doi:10.5539/ijel.v9n2p162

An Analysis of English Oral Communication Apprehension and Anxiety of Engineering Undergraduates in Pakistan

2019· article· en· W2916588664 on OpenAlexvenueno aff
Muhammad Arif Soomro, Insaf Ali Siming, Mansoor Ahmed Channa, Syed Hyder Raza Shah, Nadeem Naeem, Abdul Malik Abbasi

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsCommunication apprehensionAnxietyApprehensionPresentation (obstetrics)PsychologyCommunication skillsMedical educationVariety (cybernetics)Focus groupEnglish languageMathematics educationMedicineComputer scienceMarketingBusiness

Abstract

fetched live from OpenAlex

This paper investigates the communication apprehension (CA) and a form of anxiety which affects the engineering undergraduates’ oral communicative skills in English and particularly in oral presentations. However, this study was mainly based on the research question to be investigated: What barriers prevail among undergraduates that handicap their successful language learning and oral communicative skills in English? This study used qualitative instruments for collecting data; the instruments used were included as semi-structured interviews with eight participants and two focus group discussion to explore the barriers among Pakistani undergraduates. The data were analyzed using content analysis of the gathered data. The results revealed that communication apprehension can cause the variety of barriers among undergraduates during oral presentation. The results provided positive insights to communication practitioners and language educators on the issues related to communication apprehension; therefore, certain measures need to be taken to surmount the identified barriers.

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)0.006
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 score0.101
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.389
Teacher spread0.365 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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