Implementation of Early Childhood Learning during the Covid-19 Pandemic in Kindergarten Children Mamba'ul Bajubang, Bajubang District
Bibliographic record
Abstract
The condition of the corona virus outbreak makes children less than optimal in gaining knowledge, because learning that is usually done at school must be done online. The purpose of this study is to find out how the obstacles are and how to overcome obstacles in the implementation of early children’s learning during the covid 19 pandemic at Mamba'ul Ulum Kindergarten Bajubang. This research uses qualitative method, with data collection techniques, observation, interviews and documentation. The respondents in this study are principals, teachers and parents of students. The result of this study is that the implementation of early children’s learning during the COVID-19 pandemic at Mamba'ul Ulum Kindergarten Bajubang is carried out in accordance with the RPPH (Daily Lesson Plan) which is made simpler containing identities, including: theme, sub-theme, day, date and activities to be carried out. Then in the implementation of learning all teachers take turns every day making learning videos that are in accordance with the RPPH (Daily Lesson Plan). The video is a guide for parents at home. Obstacles faced by parents who do not have android mobile phones, lack of parental understanding of early children’s learning, learning outcomes are less than optimal because teachers cannot see directly the process of activities carried out by children. Efforts to overcome problems in early children’s learning during the COVID-19 pandemic at Mamba'ul Ulum Kindergarten Bajubang are by providing additional explanations for learning activities by giving understanding continuously and providing reinforcement to parents.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".