The Integration of Environmental Education in Science Materials by Using MOTORIC Learning Model
Bibliographic record
Abstract
The research of the integration of Environmental Education in science subject matter by application of MOTORIC Learning models has carried out on Junior High School Kupang Nusa Tenggara Timur Indonesia. MOTORIC learning model is a Environmental Education (EE) learning model that collaborate three learning approach i.e. character approach, contextual and multimedia approaches. MOTORIC consists of seven components which constitute the acronym namely: Motivation, Observation, Talking, Orientation, Reinforcement, Implementation and Confirmation. The purpose of this research is to improve the junior high school students’ knowledge about the environment. Futhermore, the study was carried out in February-May 2014, with a sample of class VII students of junior high school in Kupang Indonesia. Environmental education materials are integrated in this study include energy, living sustem, pollution, waste management and conservation. Data was measured by using multiple choice test of environmental knowledge. The data were analyzed using the Wilcoxon Signed Rank test. The results showed at 95% confidence level (? = 0.05), the integration of Environmental Education materials in science subject matter of junior high school through application of MOTORIC learning models effectively improve students’ knowledge of the environment by 64.15% in large groups of students meanwhile 68.07% in small group of students.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".