Skotnicka, M.
Program Studi Pendidikan IPA Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA)

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Journal : Jurnal Pendidikan IPA Indonesia

The Reconstruction of Disaster Knowledge through Thematic Learning of Science, Environment, Technology, and Society Integrated with Local Wisdom Atmojo, S. E.; Rusilowati, A.; Dwiningrum, S. I. A.; Skotnicka, M.
Jurnal Pendidikan IPA Indonesia Vol 7, No 2 (2018): June 2018
Publisher : Program Studi Pendidikan IPA Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpii.v7i2.14273

Abstract

This study aimed to improve the mastery of knowledge, attitudes, and skills of students in disaster mitigation in prone areas of volcanic eruptions. The subjects of this research were fifth (V) graders in three elementary schools situated in prone areas of Merapi volcano eruption in Srumbung subdistrict, Magelang regency, Central Java. The data collected in this study consisted of disaster knowledge, attitude, and mitigation practice. The data of disaster knowledge were obtained through a test and analyzed using t-test and N-gain. The attitude data were collected using questionnaire with an improvement analysis using N-gain. The mitigation practice data were collected through observation and analyzed by descriptive percentage for attitudes and skills. The research results showed that the implementation of thematic learning of Science, Environment, Technology, and Society (SETS) integrated with local wisdom was able to reconstruct and increase the disaster management knowledge. This can be seen from the N-gain score obtained by each elementary school which the highest Gain Score was 0.46 and included in the medium category. There was an enhancement attitude toward disaster having the highest Gain Score was 0.77. All disaster mitigation indicators were successfully mastered by more than 70 percent of the students, who were in the good category (70≥X≤85) in all three elementary schools implementing the thematic learning of SETS integrated with local wisdom.