Strengths weaknesses opportunities and threats of blended learning: Students′ perceptions
Bibliographic record
Abstract
BACKGROUND: Blended learning (BL) in a cell biology course of the premedical program at the Kasturba Medical College International Centre, Manipal, India, commenced in 2006. The program provides training in basic sciences to students, especially from the United States and Canada. The approach to the study was phenomenographic, with a qualitative study design using an open-ended questionnaire, focused interviews and empirical observations. AIM: The aim of this study was to identify the strengths, weaknesses, opportunities and threats (SWOT) of BL in a premedical class. SUBJECTS AND METHODS: It was a cross-sectional study. Ninety six students in a premedical cell biology class participated in the study. SWOT analysis of students' perceptions was conducted manually. Statistical analysis included content analysis of qualitative data to classify data and aligning them into the SWOT analysis matrix. RESULTS: The outcomes of the study revealed student perceptions in terms of SWOT of BL and the potential uses of this strategy. CONCLUSIONS: The study provides background for educators and curriculum experts to plan their modules while incorporating a BL approach.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".