MétaCan
Menu
Back to cohort
Record W2801621001 · doi:10.5539/elt.v11n5p74

The Role of E-learning in Studying English as a Foreign Language in Saudi Arabia: Students’ and Teachers’ Perspectives

2018· article· en· W2801621001 on OpenAlexvenueno aff
Ibrahim Mutambik

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyContext (archaeology)Government (linguistics)PerceptionThematic analysisChristian ministryMathematics educationPedagogyEmpirical researchEnglish as a foreign languageQualitative researchSociologyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Over the past few decades, there have been tremendous increase in technology advancement and the significance of this in the field of education cannot be overemphasised. The adoption and use of E-learning in studying EFL, in particular, is one such areas that has experienced such fast-paced development for some time now. As a result, the government all over the world are committing a lot of resources to keep up with this technology advancement. In this light, the government of Saudi Arabia through its Ministry of Education has recently made commitment, both as the practical and policy levels, with the hope to also benefit from using E-learning in studying EFL in Saudi Schools. However, little is known about the perception of students and teachers regarding the role of E-learning is studying EFL in the Saudi context. In an attempt to contribute to this research base, this paper draws on an empirical investigation using group interviews with students and teachers in order to gain insight into their perception about the role of E-learning in studying EFL in Saudi Arabia. The findings are presented and discussed in four thematic areas: promoting key learning skills, independent learning, flexible learning and interactive learning. The paper also highlights the limitations of the research and concludes by making a number of recommendations.

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)0.005
metaresearch head score (Gemma)0.010
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 score0.233
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.008
GPT teacher head0.311
Teacher spread0.302 · 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

Citations47
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language TeachingSame topicOnline and Blended LearningFrench-language works237,207