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Record W2620360088 · doi:10.5539/elt.v10n6p190

The Effect of Blended Learning in Enhancing Female Students’ Satisfaction in the Saudi Context

2017· article· en· W2620360088 on OpenAlexvenueno aff
Sarah Al LHassan, Nadia Shukri

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyContext (archaeology)Blackboard (design pattern)Blended learningAffect (linguistics)English as a foreign languageVariety (cybernetics)Foreign languageMathematics educationMedical educationEducational technologyComputer science

Abstract

fetched live from OpenAlex

The present study intended to investigate the effect of utilizing Learning Management System (LMS), Blackboard® on enhancing English as a Foreign Language (EFL) female students’ satisfaction in the Saudi context. It is found that the effectiveness of utilizing the supplementary materials on Blackboard® is leading up to EFL students’ satisfaction. Since, Blended Learning (BL) model could stimulate a classroom setting with activities that are carried out under flexible and engaging manner. The sample consisted of ninety-eight students from proficiency level -104. The data of the study was collected using a questionnaire to identify students’ level of satisfaction. The results revealed that students’ satisfaction was apparent as their positive responses outweighed their negative responses mainly in terms of richness of learning resources, opportunity to interact in foreign language, appropriateness and variety of content, and ease of using Blackboard®. Based on the results, the study recommends considering the positive assets and challenges to plan the future of both teaching and learning English language effectively. The study suggested several areas to be investigated in the future such as examining the motivational behavior of both the teachers and the students and finding out the factors that will affect the environment of BL in EFL.

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)0.010
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.336
Teacher spread0.327 · 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

Citations25
Published2017
Admission routes1
Has abstractyes

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