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Record W4319160763 · doi:10.5430/wjel.v13n3p61

Towards Enhancing Effective Participation of Reluctant EFL Students in Presentation Sessions at Qassim University

2023· article· en· W4319160763 on OpenAlexvenueno aff
Abdulghani Eissa Tour Mohammed

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsnot available
FundersQassim University
KeywordsPresentation (obstetrics)CurriculumPublic universitySample (material)Public speakingMedical educationPsychologyMathematics educationComputer sciencePedagogyMedicineLinguisticsPolitical science

Abstract

fetched live from OpenAlex

The current paper's objective is to determine why some EFL students at Qassim University, KSA, find it difficult to give their public presentations demanded by some departments’ curricula as a college requirement. Recently, the author observed that some EFL students are very reluctant to participate in public speaking sessions until the allotted time expires although they know that they are going to lose grades at the end of the day for not participating. A quantitative research methodology is used to acquire the data. A questionnaire containing (17) items was designed and distributed to a sample of (52) EFL students representing the total number of students enrolled in two theoretical linguistic courses during the academic year (1443 -1444). After collecting and analysing the data, the study showed that some students feel shy and intentionally avoid public speech in EFL classes because they come with hardly negligible experiences in public speech when enrolled as tertiary-level 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)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.029
GPT teacher head0.422
Teacher spread0.393 · 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 designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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