Virtual Classroom: To Create A Digital Education System in Bangladesh
Bibliographic record
Abstract
The use of web-based tools for educational purposes is a rapidly growing trend in Bangladesh. Most of the academic institutions in Bangladesh have decided to develop academic portals where teachers can create online versions of their courses. This paper addresses students’ opinions on the use of virtual classroom from their own personal experiences and identifies features of virtual classroom that are vital to create an interactive student-centered learning environment. We try to understand whether the use of virtual classroom can bring improvement in students’ learning and performance in the class. The results from binary logistic regression indicate that most of the participants have positive opinions regarding the usage of virtual classroom for learning purposes. As the present Government of Bangladesh (GOB) has urged all higher education institutions (HEIs) to take effective measures in order to implement ‘Integrated University Information Management Platform’, the findings from this study will help educators and administrators to understand how to construct an interactive student-friendly academic portal that will fulfill all the needs of the customers and to assess whether the features of the existing portals that they are currently using need any further improvements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".