Employment of Active Learning at HEIs in Bangladesh to Improve Education Quality
Bibliographic record
Abstract
In recent years, education quality and quality assessment have received a great deal of attention at Higher Education Institutions (HEIs) in Bangladesh. Most of the HEIs in Bangladesh face severe resource constraints and find it difficult to improve education quality by improving inputs, such as better infrastructure and modernized classroom facilities. Thus, in response to the present government’s demand to improve the quality of education at HEIs in Bangladesh, it is imperative to formulate plans that are more cost-effective. According to some previous studies, the quality of education depends largely on the teaching-learning process. These studies affirm that, with limited resources at hand, the employment of active learning in the classroom is one of the most effective ways to improve education quality. To conduct this qualitative research, we utilized multiple sources of data, including semi-structured and in-depth interviews, descriptive observations and self-administered questionnaires. This paper aims to explore three related issues: What are the various active learning strategies that can be employed by the instructors at HEIs in Bangladesh? What are the potential factors that can hinder the implementation process? Finally, what recommendations can be provided on how to successfully implement active learning strategies in the classroom? The findings suggest that a lack of teacher training and student prior experience in an active learning environment, large class sizes, excessive curriculum loads and students’ academic backgrounds are some common factors that can hinder the implementation of active learning in Bangladesh. The findings of this study can be instrumental for HEIs in Bangladesh as they aspire to improve their education quality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".