Open AccessScience environment technology and society approach learning to improve natural disaster mitigation literacy
Author(s) -
Supriyadi Supriyadi,
Ani Rusilowati,
S. Linuwih,
Achmad Binadja,
Cherly Salawane
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1387/1/012119
Subject(s) - syllabus , natural disaster , test (biology) , mathematics education , natural (archaeology) , event (particle physics) , literacy , psychology , scientific literacy , computer science , science education , pedagogy , geography , paleontology , physics , archaeology , quantum mechanics , meteorology , biology
This research is motivated by the location of Indonesia in disaster areas. But the readiness of citizens to deal with disasters is still low. This study aims to (1) develop natural disaster learning packages with the vision of the Science Environment Technique Society (SETS) which is integrated in science subjects, (2) implement disaster prepared teaching materials, (3) improve teacher understanding and skills, students about concepts, principle, the practice of saving oneself in the event of a natural disaster. This research was conducted in collaboration with teachers in primary and secondary education. Theoretical exploration and expert evaluation are carried out on the features of themes and sub-themes of disaster learning models that are integrated in SETS vision science subjects. Data analysis technique used (1) descriptive percentage, (2) normalized gain test, and (3) t-test. The results of this study in the form of five features of learning models such as (1) syllabus, (2) lesson plans, (3) learning methods, (4) teaching materials, and (5) assessment instruments. Desimination of the five features was declared feasible to increase students’ understanding of disasters. This is seen from the test results obtained by students experiencing an increase (gain) from pretest to posttest starting from 53.5% to 80.3%.