Online Learning Challenges in Schools During the Pandemic COVID-19 in Indonesia
Bibliographic record
Abstract
The purpose of this research is to provide information to the Government of Indonesia in particular and observers of education in general regarding the challenges of online learning in schools in remote areas in Indonesia so that there is mutual attention from educational stakeholders to pay attention to students in remote areas to retain their rights in education. This research methodology is qualitative, using the result of previous relevant researchs that support in writing of this research. The research findings include: 1) the importance of the role of school principals as implementers of government policies; 2) teachers' strategies in implementing online learning that are easily understood by students; 3) the importance of the role of parental assistance during online learning; and 4) Regional Government policy strategies for the success of online learning, especially in remote areas. The recommendations of this research are: 1) adjusting online learning based on local conditions; 2) the existence of task forces in the regions to help the online learning process run smoothly; 3) monitoring and evaluation; 4) broad autonomy for school principals to innovate; 5) teacher training program to support the online learning process; 6) the existence of cooperation between the government and the private sector in the telecommunications sector; 7) face-to-face learning for students who do not have telecommunication equipment.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".