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

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.