Understanding Students’ Perception of Sustainability: Educational NLP in the Analysis of Free Answers

Hiroko Yamano (), John Jongho Park, Nathan Hyungsok Choe and Ichiro Sakata
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Hiroko Yamano: Institute for Future Initiatives, The University of Tokyo, Tokyo 113-0033, Japan
John Jongho Park: School of Engineering Design, Technology, and Professional Programs, The Pennsylvania State University, University Park, PA 16802, USA
Nathan Hyungsok Choe: Department of Biomedical Engineering, The George Washington University, Washington, DC 20052, USA
Ichiro Sakata: Institute for Future Initiatives, The University of Tokyo, Tokyo 113-0033, Japan

Sustainability, 2022, vol. 14, issue 21, 1-16

Abstract: This study explored undergraduate students’ conceptions of sustainable development by asking about their definition of a sustainable world, current issues of sustainable development, and the necessary mindset and skillsets to build a sustainable world. We derived data from 107 participants’ open-ended answers that we collected through an online survey at the beginning and the end of the sustainability class. Text mining with Natural Language Processing (NLP), principal component analysis (PCA), and co-occurrence network analysis were conducted to understand the changes in students’ conception of sustainable development. In addition, we also conducted the Linguistic Inquiry and Word Count (LIWC) dictionary to investigate the psychometric properties of students’ awareness and understanding related to sustainable development. This advanced analysis technique provided a rich understanding of university students’ perceptions of sustainable development compared to what the UN initially defined as sustainable development goals (SDGs). The results showed imperative insights into the benefits of sustainability experiences and knowledge that generate motivation to develop students’ competencies as change agents.

Keywords: natural language processing (NLP); sustainability education; sustainable development goals (SDGs); higher education; co-occurrence network; linguistic analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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