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Record W1586672262 · doi:10.5539/elt.v8n9p51

EFL Secondary School Teachers’ Views on Blended Learning in Tabuk City

2015· article· en· W1586672262 on OpenAlexvenueno aff
Abdulrahman Alfahadi, Abdulrhman A. Alsalhi, Abdullah S. Alshammari

Bibliographic record

VenueEnglish Language Teaching · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsBlended learningPsychologyMathematics educationProcess (computing)Sample (material)PedagogyEducational technologyComputer scienceChemistry

Abstract

fetched live from OpenAlex

The aim of this study is to investigate EFL Secondary School Teachers’ Views on Blended Learning. It also aims to investigate (a) the teachers’ views on blended learning content and process, and (b) how blended learning is effective in developing teachers’ performance. The study sample included 35 EFL Saudi teachers in Tabuk City, KSA. In order to collect the data required, the researchers developed a questionnaire that consisted of two sections, namely, process and content. The results indicate that the teachers' views toward blended learning were generally positive and very promising in both sections. Moreover, it was interesting to see that EFL teachers were highly optimistic about how blended learning would help them in improving their performance and how it would motivate their students to learn English. Finally, there are no significant differences between teachers’ responses to the content and process of blended learning, with regards to qualification, experience, and the amount of training done.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.028
GPT teacher head0.333
Teacher spread0.305 · 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

Citations6
Published2015
Admission routes1
Has abstractyes

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