Implementation of Hybrid Learning to Maintain the Quality of Learning in Fostered MTs During the Covid-19 Pandemic
Bibliographic record
Abstract
During the Covid-19 pandemic, the education sector was also greatly affected, because in order to stop the spread of this corona all students and teachers studied from home, which was suddenly carried out without any preparation at all. The unpreparedness of all elements in education is a big obstacle, changing the way of teaching and learning from face to face or offline (outside the network) to online (in the network) requires readiness from all elements, starting from the government, madrasas, teachers, students and parents. The government relaxed the education assessment system according to emergencies as long as learning can continue without having to be burdened with achieving competence. Many teachers teach by utilizing existing technology. The purpose of this study is to describe the implementation of the implementation of learning strategies through the collaboration of WAG (Whatsapp Group) and Offline during the Covid-19 pandemic emergency. This research is divided into two stages, each stage has different characteristics from one another. From data collection, data analysis, and discussion results, it is known that the implementation of learning strategies through WAG (Whatsapp Group) collaboration and offline was carried out to maintain the quality of the teaching and learning process during the Covid-19 Pandemic at MTs assisted by Malang Regency. The implementation of Cycle I focused on the necessary administrative preparations while the implementation of Cycle II focused on formulating decrees on the administration of learning activities, circulars for meetings with parents/guardians of students to socialize the WAG (Whatsapp Group) and offline collaboration models.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".