THE INFLUENCE OF MEDIA VIDEO CD TUTORIAL ON SCIENCE-BASED LEARNING MULTISENSORY-ECOLOGY TO IMPROVE COGNITIVE ABILITY OF EARLY CHILDHOOD
Bibliographic record
Abstract
THE INFLUENCE OF MEDIA VIDEO CD TUTORIAL ON SCIENCE-BASED LEARNING MULTISENSORY-ECOLOGY TO IMPROVE COGNITIVE ABILITY OF EARLY CHILDHOOD Eka Nilawati 1 , Yaswinda 2 Graduate School of Early Childhood Education (PAUD), Padang State University ekanilawati06@gmail.com Abstract Science learning provided in early childhood can stimulate and improve children's thinking, and bring out the child's interest and curiosity. Through learning science children can have various opportunities to understand the universe, the surrounding environment through observation activities, experiments and various other science playing activities. The use of technology-based learning media is very helpful and useful in the science learning process. Various strategies and approaches can also be used by early childhood educators in presenting science learning. The implementation of a multisensory-ecological-based science learning model supported by the use of video tutorial media is assessed to improve cognitive abilities as well as various other abilities in early childhood. Multisensory-Ecological-based science learning is one of the models of benchmarking that prioritizes the process skills and science content through the experience of multiple senses. The use of media learning video CD Tutorial can be an alternative guide for educators early Childhood in delivering materials related to the learning of science-based multisensory ecology, so that with the video Tutorial Teachers can develop various activities that can spur and stimulate the cognitive development of children. Keywords: science, multisensory-ecology, video tutorial, early childhood
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".