Assessing The Viability Of Online Learning During Covid-19 Pandemic In Rural Areas
Bibliographic record
Abstract
In the first quarter of 2019, the pandemic of Covid-19 struck globally which resulted in the unprecedented lockdown schooling in most affected countries. Unfortunately, online learning is a big challenge in certain localities especially among the rural learners with limited internet access and facilities. This study is assessing the impact and challenges faced by the teachers and students in these areas in this period of online learning during lockdown schooling. An online survey was distributed among rural schools in Selangor, Malaysia and the data was analysed by using descriptive analysis. The results revealed that 82.6% of rural respondents experienced psychological difficulties with 77.4% of respondents agreeing that time management is an issue during online learning. This study observed a high level of gratitude among respondents as they are satisfied with online learning during lockdown schooling mainly with the online materials, student-teacher interaction and online assessment. The main challenges of rural students and teachers include the internet connection and being easily distracted during class whereas lack of support is not an issue. Meanwhile, loneliness and mental health are not considered as main learning issues. This study is important for the respected figures to strategize their priority in facilitating the rural learners and navigate the situation in the future. Such action will be able to reinforce goal 4 in Sustainable Development Goals (SDG) in providing a higher quality education for everyone.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".