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Record W4393261709 · doi:10.5430/wjel.v14n3p471

The Effect of Multi-Media Usage in Cognitive Demands for Teaching EFL among Jordanian Secondary School Learners

2024· article· en· W4393261709 on OpenAlexvenueno aff
Soleman Alzobidy, Maha Jamal Al-qadi, Saleh Belgacem Belhassen, Issa Mohammad Muflih Naser, Shahab Ahmad Al Maaytah

Bibliographic record

VenueWorld Journal of English Language · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceMathematics educationCognitionPsychology

Abstract

fetched live from OpenAlex

The role of teaching methods in EFL context is too essential and it should receive a continuous improvement due to time, environment and need changes. Several studies have examined whether certain methods improve and develop teaching process. However, cognitive demands for teaching EFL focusing on planning and critical thinking among students need further attention and focus. Accordingly, this study is aimed at investigating the effect of multi-media usage in cognitive demands for teaching EFL among Jordanian secondary school learners. This study adopted quantitative research design by distributing a questionnaire. The target population was EFL teachers at public and private secondary school. The findings indicated that multimedia (YouTube, Video, Picture, and PowerPoint) enhance cognitive demands of for teaching EFL. Multimedia more specifically, allow students to improve critical thinking and plans well when learning EFL. The study concluded that pictures help in enhancing critical thinking and planning of EFL learners as well as multimedia provide an opportunity that allow these learners to think out of the box and plan their lessons and topic effectively.

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)0.004
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.335
Teacher spread0.325 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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