Integrating ‘education for sustainable development’ in statistics classes: visual analysis of social and economic data with gapminder

Abstract

Sustainable development goals (SDGs) address various aspects of future human development such as poverty, pollution, or climate change. Education for Sustainable Development (ESD) seeks to encourage students to actively participate and consider these issues in sustainable development. Following design-based research approaches, our study aims to identify opportunities for integrating ESD into statistics education. We describe the main features of an ESD-integrated learning trajectory for middle school students exploring sets of ESD related data visually with the software Gapminder. The outcomes of our study suggest that (1) Students’ engagement in forming statistical models of various countries’ sustainable development developed their abilities to generate statistical questions (2) Their intuitive knowledge of statistical concepts was further formalized during their subsequent analyses.

Cross-Curricular Approaches to Promoting Statistical and Data Literacy in STEM School Education: A Systematic Review

“…Several of these studies used interdisciplinary learning environments addressing authentic problems and including real-world datasets. This approach has been shown to be effective in developing students' knowledge of statistical concepts (Andre et al, 2020) and their ability to ask and answer databased questions (Swan et al, 2013;Vahey et al, 2012). Furthermore, increases in students' confidence in data analysis and statistical skills (such as data management and data analysis; Dierker et al, 2017) as well as positive effects on students' interest in data (Kochevar et al, 2015) have been found.…”

“…A common feature of most of the approaches listed in Table 1 is the integration of digital media. The usage of specific software (e.g., Gapminder in Andre et al, 2020;Jiang et al, 2020) and novicefriendly web-based interfaces (Kochevar et al, 2015;Mosquera et al, 2020;Wolff et al, 2019) has been demonstrated to be effective in helping students deal with large and complex real-world data sets.…”