Implementation and Challenges of Home-Based Teaching and Learning (PdPR) in Religious Schools in Kubang Pasu, Kedah
Bibliographic record
Abstract
Covid-19 has significantly impacted many sectors worldwide, including the education sector. This impact also affects educators in implementing home-based teaching and learning (PdPR). PdPR is implemented to ensure that students are not left behind even though the world is going through the Covid-19 pandemic. Therefore, research is carried out where the research questions and objectives are about PdPR implementation and challenges. Surveys are done in five religious schools in Kubang Pasu district, which involves 77 teachers. The research found that teachers still carry out PdPR even though they face many challenges such as limited interactions with students, lack of student attention, and limited internet access. To successfully implement PdPR, teachers need support from all parties, including parents, students, NGOs and the government. Thus, the research finding is crucial to the schools and Islamic education sector to study and improve PdPR to achieve educational objectives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".