The Effectiveness of the Blended Learning in Enhancing EFL Learning and Collaboration
Bibliographic record
Abstract
This descriptive-analytical study investigates undergraduates' perceptions and reflections toward adopting the Blended-Learning system in the university instructions. For this goal, the study employed two tools to collect data. Firstly, a) (n=38) pre-service teachers wrote reflection essays, and b) Google forms closed-ended five scales' questionnaire investigated the academic and interaction indicators, each consisting of thirty items, distributed among (n=110) pre-service teachers. The participants were majoring in English as a Foreign language at multi-stages, Saudi Arabia, 2020/2021. The study employed the content and the SPSS analysis. The questionnaire's results showed the undergraduates' positive perceptions toward combining online and face-to-face learning and how this environment improved their learning outcomes, created a collaborative community, fostered openness for sharing, asking, expressing, and getting talk-worthy ideas. The essays' content analysis reflected the undergraduates' experiences and how they enjoyed transitioning between online and face-to-face learning; they were satisfied by the ability to track their scores. These results created a continuous feedback loop correlated with their academic progress through various activities. However, they encountered a few challenges in online classes like a) missed face-to-face warm environment, b) lost attention, and c) missed instructors' nonverbal cues. Generalized, blended learning facilitated learners' knowledge by reducing education costs, distance, efforts, and time without reducing the students' benefits. The study recommends that the BL instructors have to show some enthusiasm and inspiration. The study proposes future research.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".