Deep learning physics and local wisdom strengthen mechanical wave literacy for earthquake risk reduction supporting SDGs 4 and 11

¹ Faculty of Mathematics and Natural Sciences, UNESA University, Surabaya, Indonesia
² Department for Higher Education Research, Faculty of Education, Arts, and Architecture, Universität für Weiterbildung Krems, Krems, Austria

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

This study examines students’ scientific literacy on mechanical waves in the context of earthquake mitigation and traditional construction, topics rarely emphasized in physics learning despite their real-life relevance. A deep learning approach was implemented to strengthen contextual and reflective understanding. Using a collaborative mixed-method design, the study involved 105 eleventh-grade science students and one physics teacher. Data were collected through PISA 2022–aligned scientific literacy tests, student questionnaires with 14 Likert items and 3 open-ended questions, and semi-structured teacher interviews. Learning activities integrated earthquake scenarios to promote critical thinking. Results showed 58.1% of students had low literacy, 34.3% moderate, and 7.6% high. The weakest indicators were data processing (mean 49.8) and evidence-based decision making (mean 36.9). Despite this, the deep learning approach improved engagement and contextual understanding, reflected by a mean perception score of 3.6 out of 4, along with increased motivation and awareness of local wisdom for disaster risk reduction. These outcomes assist achieve SDG 4 by enhancing the quality of education and scientific knowledge, and they contribute to SDG 11 by increasing community preparedness and resilience to disasters.