Marginalization, Technology Access and Study Approaches of Undergraduate Distance Learners during Covid-19 Pandemic in India
Bibliographic record
Abstract
The Covid-19 pandemic, for the past years, had led to disruption of classroom activities and adoption of online teaching-learning in almost all parts of the globe, including India. Sudden switch over from the classroom blackboard to the laptop screen may have influenced study approaches of students especially when there were challenges for access to technology and non-readiness for online learning among the Indian students. Since different social and economic factors bring differences in students’ learning, an online survey was conducted with 296 randomly selected undergraduate distance learning (DL) students of Indira Gandhi National Open University (IGNOU) to examine how technology access during the pandemic has influenced study approach of Indian DL students belonging to different marginalized and non-marginalized groups. The research results showed that marginalized students had lower access to technology than their non-marginalized counterparts, although no gender differences were found in access to technology in both the groups. Lower access to technology was found associated with more surface approach to study in the DL students in general and the marginalized students in particular. The marginalized females were found at risk in terms of both, access to technology and approaches to study. The findings, as discussed, are intended to further enrich our understanding of the role of technology vis-à-vis study approach of distance learning students during the pandemic, and formulate appropriate teaching-learning strategies for the future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".