Integrating interdisciplinary thinking and education in graduate disaster research curricula: Insights from statistical, sentiment, and thematic analysis

Abstract

Natural hazard and disaster resilience research requires a multidisciplinary approach to address the complexities of risk mitigation and recovery for vulnerable communities. To this end, an interdisciplinary course was developed, with faculty from civil engineering, urban planning, and anthropology. The course aimed to deepen students' understanding of coastal hazard risks and to equip them with an understanding of interdisciplinary and community-engaged research. This article analyzes course evaluation data using statistical, sentiment, and thematic analysis to examine benefits, challenges, and pedagogical implications from students' perspectives. Statistical and sentiment analysis provided insight into general themes, polarizing thoughts, and key challenges and benefits. In contrast, thematic analysis offered a deeper understanding of students' nuanced perspectives and the pedagogies employed in the course. Results indicated participants held a generally positive perception of the course's ability to foster interdisciplinary competencies, despite some challenges. Students also reported favorable views on collaboration and personal development, though opinions varied across participants. Challenges included difficulties with other fields' foundational knowledge, terminology, and acronyms. Technical problems, particularly with online communication tools, were also noted as obstacles to collaborating with partners from other universities. Student recommendations to alleviate these challenges included breaking down complex concepts into smaller, more digestible components and incorporating peer-to-peer learning and real-world problem-solving activities. In summary, this study underscores how interdisciplinary education can broaden students' perspectives and prepare them to address complex issues in natural hazard resilience. Furthermore, it demonstrates that statistical, sentiment, and thematic analysis can complement each other, providing both high-level insights and deeper contextual understanding.