Online English Classes for Bangladeshi Young Learners during the COVID-19 Pandemic: Voices of the Teachers and Parents
Bibliographic record
Abstract
The recent COVID-19 pandemic brought a dramatic change in teaching and learning around the world. Almost all educational institutions shifted to the online mode of teaching so that students do not miss any academic year. In Bangladesh, such a mode of teaching was never introduced at the mass level earlier, and that is why it was quite challenging for teachers to conduct online classes initially. In some cases, teachers who did not have a certain level of technological expertise in using online teaching platforms experienced more issues in conducting classes. This qualitative study highlights the problems that teachers and parents of nine young learners (YLs) faced during their online classes. The focused group discussion among seven English teachers and nine parents revealed that many YLs could not follow the technical instructions of the teachers well, which compelled the parents to sit beside their children constantly. Moreover, this study reports on the experience of teachers in communicating and coordinating with students on online platforms in a developing country where a full-fledged online teaching mode has not been implemented before. Such a reciprocal interaction among teachers, students, and parents in an online platform provides the scope for rethinking the “parental involvement framework of online teaching” in an English for speakers of other languages (ESOL) context, where parents’ digital skills, as well as involvement with the learners’ the learning process, affect students’ academic achievement to a great extent. The findings of the study have implications for planning online English courses for YLs in ESOL contexts such as Bangladesh, where the idea of online teaching is quite new.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".