Exploring Diverse Teaching Models for Enhancing Nursing Students' English Language Proficiency: A Blended Learning Perspective
Bibliographic record
Abstract
The present study examines the efficacy of blended learning models in enhancing nursing students’ proficiency in the English language. It is challenging to come across comparative studies that assess the effectiveness of blended learning models in advancing the English language skills of nurses. However, the experimental study includes five groups, and each group has gone through five different instructional models of teaching. The first experimental group received instruction through the rotation model; the second experimental group received instruction through the flex model; the third experimental group received instruction through the self-blend model; the fourth experimental group received instruction through the enriched-virtual model; and the fifth group which served as control group received instruction through the communicative language teaching approach. A total of one hundred and fifty participants were selected through random sampling from Aligarh Muslim University, India. The data were gathered using pre- and post-tests administered before and after the intervention of these models using the standardized general English test (TOFEL). The repeated measure ANOVA revealed that each group manifested significant advancement in nurses' English language skills. Nevertheless, the rotation model demonstrated superior performance in enhancing nurses' English language skills in comparison to the other groups. The findings of the study hold pedagogical significance for those who are responsible for designing curricula, developing training programs for future nurses, producing materials, and all other persons who are involved in nursing education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".