Online Learning in Low-tech environments – What works, what doesn’t?
Bibliographic record
Abstract
The COVID-19-induced pandemic compelled all schools and colleges to shift abruptly to online learning to continue education for students. Set in the context of a low-technological online learning environment in public schools in India, this study examined the contextual factors that supported or impeded online learning for middle school students drawn from 11 states of India. The Community of Inquiry (COI) Framework (Garrison et al., 2000) served as a theoretical lens to examine teachers’ online practices and how contextual factors affected teachers’ practices and students’ experience of the three presences of the COI framework. A qualitative study using interviews, and focus group discussions were conducted with students, teachers, education department officials and parents. Students’ interviews were conducted using Participatory Learning and Action (PLA) tools and group discussions. Device accessibility, poor networks, low competency and familiarity with online teaching and learning technology not only inhibited students’ from accessing online learning but also inhibited teachers from establishing effective online teaching practices in alignment with the COI framework. Drawing from the insights generated in the study, the paper proposes ways for creating more effective learning experiences in a technology-deficient online learning environment.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".