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Record W3065626680 · doi:10.5539/elt.v13n9p72

The Effectiveness of Blended Learning in Enhancing Saudi Students’ Competence in Paragraph Writing

2020· article· en· W3065626680 on OpenAlexvenueno aff
O AlRouji

Bibliographic record

VenueEnglish Language Teaching · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsParagraphBlended learningPsychologyMathematics educationCompetence (human resources)PedagogyMedical educationEducational technologyComputer scienceSocial psychologyMedicine

Abstract

fetched live from OpenAlex

Blended learning approach has become an emerging trend in the field of education and has caught the attention and interest of many researchers as a new approach to encourage students in their learning process. The present study aimed at examining the effectiveness of blended learning in improving Saudi EFL students’ English paragraph writing at Shaqra University. The sample of the study consists of 70 EFL students at Shaqra University. The experiment was conducted for six weeks (2018). A pre-test and post-paragraph writing test was used to measure the effectiveness of the blended learning in enhancing paragraph writing skills. Results showed that there were significant differences in the mean scores of the control group (traditional teaching mode) and the experimental group (blended learning) in favor of the experimental group. This proved the effectiveness of blended learning approach in developing Saudi students’ competence in paragraph writing.

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)0.003
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.324
Teacher spread0.313 · 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

Citations16
Published2020
Admission routes1
Has abstractyes

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