Environmental Education for Prevent Disaster: A Survey of Students Knowledge in Beginning New Normal of COVID-19
Bibliographic record
Abstract
The purpose of this study was to determine the importance of implementing disaster mitigation education in schools during the new normal era of coronavirus disease 2019 (COVID-19). The method in this research used descriptive research design using a survey study approach. The research instrument used was a knowledge test with the number of questions as many as 15 items. The sample used was senior high school students who were randomly selected. The level of good and lack of knowledge is determined based on the average total score. A good level of knowledge has a condition> 111.76, while a level of knowledge that lacks a condition <111.76. The results of this study are that the majority of respondents have a total score that is less with a mean score of 101.94, while respondents who have a good number of scores have an average score of 127. Overall respondents have an average of 111.76. Then conducted an Independent Sample t-test with the result that there are differences in the number of good disaster mitigation knowledge scores on respondents with the number of disaster mitigation knowledge scores that are less on respondents with a p-value of 0,000 with a degree of freedom is 95%. The implementation of disaster education in the new normal era is important to continue to prevent transmission of COVID-19. The conclusion of this study is the score of students' knowledge about disaster needs to be improved in the new normal era. Keywords: Disaster Mitigation Education, Disaster Mitigation Knowledge, COVID-19
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".