Implementation of Hybrid Learning to Maintain the Quality of Learning in Fostered MTs During the Covid-19 Pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it