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Record W2140919369 · doi:10.5430/ijhe.v4n1p136

EFL College Students’ Perceptions of the Difficulties in Oral Presentation as a Form of Assessment

2015· article· en· W2140919369 on OpenAlexvenueno aff
Nowreyah A. Al-Nouh, Muneera M. Abdul-Kareem, Hanan A. Taqi

Bibliographic record

VenueInternational Journal of Higher Education · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsLikert scalePresentation (obstetrics)PsychologyPerceptionNationalityMedical educationMathematics educationScale (ratio)Point (geometry)PedagogyMedicineImmigrationDevelopmental psychology

Abstract

fetched live from OpenAlex

Oral presentation skills are considered one of the most important proficiencies needed for higher education and future careers. Thus, the present study is interested in eliciting English as a Foreign Language (EFL) college students’ perceptions of the difficulties they face in oral presentation as a form of assessment. Participants were 500 female EFL college students from different grade levels enrolled in a four-year pre-service teacher education program at the College of Basic Education (CBE) in Kuwait City, Kuwait. A five-point Likert Scale questionnaire was used and divided into three main sections: personal traits, oral presentation skills, and instructor and audience. Independent variables measured were students’ ages, year at college, Grade Point Average (GPA), and nationality. In addition, a structured interview to solicit instructors’ opinions was carried out. Results showed students’ perceptions of the difficulties they experienced at a medium level (M=3.10). However, significant differences in the results were found when students’ nationalities and GPAs were taken into account.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.043
GPT teacher head0.399
Teacher spread0.356 · 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

Citations72
Published2015
Admission routes1
Has abstractyes

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