Flipped Classroom: Students and Teachers Perceptions on the Impact and Challenges of Implementation at Tertiary Level in Bangladesh
Bibliographic record
Abstract
This study aimed to enhance the capacity of teachers at Jashore University of Science and Technology (JUST) to manage blended classes effectively by introducing them to the Flipped Classroom (FC) model. Through a series of workshops and training sessions, teachers and students explored FC concepts, methodologies, and best practices. Teachers who participated in the initial workshop began implementing FC in their classrooms, and after three months, they reconvened to discuss their experiences, challenges, and the overall impact of FC on their teaching. In addition, students from various university departments received training on FC usage. Afterward, both teachers and students completed separate questionnaires, sharing their perspectives on the FC approach. The analysis revealed mixed reactions from teachers, while students generally responded positively to FC. Teachers expressed hesitation to adopt FC, citing increased workload, limited technical knowledge, and inadequate technological support. Additionally, many teachers lacked formal pedagogical training, which compounded the challenges of transitioning to this model. Conversely, students displayed strong interest in flipped learning, with many expressing a desire for FC-based methods across all courses. In light of these findings, the study recommended providing teachers with pedagogical and technology-focused training and hiring additional staff to help reduce the current teaching workload. Overall, this study offers valuable insights for teachers, students, and educational authorities in Bangladesh, highlighting the readiness and potential benefits of FC for elevating tertiary education standards.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".