Moving Home Learning Program (MHLP) as an Adaptive Learning Strategy in Emergency Remote Teaching during the Covid-19 Pandemic
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Bibliographic record
Abstract
The Covid-19 pandemic had a dangerous impact on early-childhood education, lost learning in almost all aspects of child development. The house-to-house learning, with the name Moving Home Learning Program (MHLP), is an attractive offer as an emergency remote teaching solution. This study aims to describe the application of MHLP designed by early-childhood education institutions during the learning process at home. This study used a qualitative approach with data collection using interviews, observation, and documentation. The respondents involved in the interview were a kindergarten principal and four teachers. The research data were analyzed using the data content analysis. The Findings show that the MHLP has proven to be sufficiently in line with the learning needs of early childhood during the Covid-19 pandemic. Although, the application of the MHLP learning model has limitations such as the distance from the house that is far away, the number of meetings that are only once a week, the number of food and toy sellers passing by, disturbing children's concentration, and the risk of damage to goods at home. The implication of this research can be the basis for evaluating MHLP as an adaptive strategy that requires the attention of related parties, including policy makers, school principals, and teachers for the development of new, more effective online learning models.
 Keywords: Moving Home Learning Program (MHLP), Children Remote Teaching
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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 | 0.003 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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