Learners’ Perception of the Transition to Instructor-Led Online Learning Environments: Facilitators and Barriers During the COVID-19 Pandemic
Bibliographic record
Abstract
Online learning environments (OLE) continue to expand due to the COVID-19 pandemic and the transition of a majority of educational institutions and universities worldwide from traditional classroom settings to online learning methods. The purpose of this study was to understand the perceptions of learners at a university in India toward the sudden transition from traditional face-to-face learning to an instructor-led OLE due to the pandemic-induced lockdown enforced across India in March 2020. Using a qualitative case study approach, structured interviews were conducted via Microsoft Teams with 35 learners from Savitribai Phule Pune University, a large public university in India. Interviews comprised eight open-ended questions, which were validated by experts. Results indicate that learners accepted the transition toward the OLE. Five key themes arose from the interview data: accessibility and comfort, Internet connectivity, OLE effectiveness, course content, and interactions between students and instructors. The study provides insights to the researchers with the emergent themes from the research. Also, it carries practical implications concerning implications regarding infrastructure readiness for remote learners, acceptance, and adoption of OLEs by faculty instructors, organizational support, and facilitating conditions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".