The Effect of Blended Learning in Enhancing Female Students’ Satisfaction in the Saudi Context
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".