Transition to online education in Sri Lanka during COVID-19: A descriptive phenomenological study
Bibliographic record
Abstract
The novel coronavirus (COVID-19) that originated in China was declared a global pandemic by the World Health Organization in March 2020. To limit the spread of the disease, the Sri Lankan government announced the closure of all educational institutes. The school closure encouraged the use of home-based learning, and this transition was a critical period for both teachers and students. Because most Sri Lankan children reside in rural areas, our study examined how teachers in rural schools experienced the lockdown in relation to their teaching role during the pandemic. We used a descriptive phenomenological study design with purposive sampling until we achieved the saturation point. In-depth interviews were conducted via the Zoom platform, using a validated guideline that was piloted before the study and analyzed based on the thematic analysis approach. We identified five major themes: (1) adjustment to the online teaching/learning process; (2) experience of challenges; (3) experience of new opportunities; (4) impact of parental involvement; and (5) impact of teachers’ and children’s physical, psycho-social well-being. The study identified lack of access to technology and lack of proper guidance from the family as challenges during home-based learning. Parental involvement during teaching sessions decreased teachers’ autonomy. Inadequate and inaccurate evaluation of the students disrupts the learning process. Teachers perceived that children’s distance from classroom learning and their peer groups will affect the children’s development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".