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Record W3002822313 · doi:10.5539/hes.v10n1p121

EFL Teachers’ Perceptions of the Effectiveness of Online Professional Development in Higher Education in Saudi Arabia

2020· article· en· W3002822313 on OpenAlexvenueno aff
Fatimah Yousef Alzahrani, Abeer Sultan Althaqafi

Bibliographic record

VenueHigher Education Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPerceptionMedical educationTeaching methodBlended learningEducational technologyPedagogyMedicine

Abstract

fetched live from OpenAlex

This study aimed at examining EFL teachers’ perceptions of the effectiveness of an OPD program provided by a Saudi University. The program lasted for almost one year, and consisted of different courses about teaching English language. An online questionnaire was sent to the teachers to examine their perceptions of the effectiveness of the program through examining teachers’ perceptions of the significant features of the program, their learning in the program, and their use of the new knowledge and skills. Findings indicated limitations in teachers’ positive perceptions of the features of the OPD courses, teachers’ learning, and teachers’ use of new skills and knowledge. Providers of OPD need to take into consideration teachers’ needs and preferences, and the obstacles that might inhibit teachers’ learning. Needs analysis might be helpful to discover their needs and preferences and solutions for the obstacles they face.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
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)0.0040.000

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 head0.064
GPT teacher head0.413
Teacher spread0.349 · 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

Citations23
Published2020
Admission routes1
Has abstractyes

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