Teaching English Online to Learners of Bangladesh Open University: Expectations and Outcomes
Bibliographic record
Abstract
This study investigated the anxieties and viewpoints associated with teaching English online to the students at Bangladesh Open University in the pandemic situation.The study analyzed the variables that impacted their adjustment to online education.An email-based survey was conducted among English course participants, using a questionnaire that included both open-ended and closed items.The poll focused on students' comprehension of online education, their perspectives and responses, the benefits and drawbacks of utilizing Zoom, learner engagement, and matters pertaining to learner independence.A focus group interview (FGI) was also conducted with four students from the sample.The study's findings indicated that online English education could be advantageous when thoroughly investigated, despite the initial difficulties.The utilization of online lectures, monitoring, and scaffolding had a significant role in fostering the development of learner autonomy and reflective practice.At first, learners encountered difficulties like as limitations in technology, inadequate data plans, unreliable networks, and fear or resistance towards technology.Nevertheless, they ultimately discovered the experience to be fulfilling and successfully adjusted to the online format.The findings also revealed the necessity of realizing the term 'ODL' (Open and Distance Learning) and its necessity and implementation resulting in positive outcomes on learners.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".