Localizing your learning: Using online census data in Introductory Sociology classes


Michael Follert, St Francis Xavier University

Despite being a scholar who leans more toward theoretical and interpretive approaches in sociology, I’ve found the online databases for the Canadian Census to be incredibly successful tools for getting introductory sociology students to translate key concepts to social reality on a week-to-week basis. Census profiles (detailed demographic details of every municipality in the country), census boundary files, and census tables can help students to answer highly relevant questions like: What does income inequality look like in my hometown when compared to the rest of my province or territory? How many people practice various religious faiths where I come from and how has that changed in the past 20 years? How does the quality of housing in my city compared with that in the nearest Reserve or Indigenous community (or vice versa)? What does the gendered division of labour look like in a part of the country I may not know much about (say, as an international student) and how has that changed over time? In addition to the benefits of applied understanding (e.g., concepts like social inequality, secularization, colonialism, and gender roles, respectively) and the development of data literacy, this approach offers highly localized learning that students can feel meaningfully connected to. As a learning framework that can be implemented in weekly online discussion forums, but also in classroom activities, it ensures additionally (i) originality in student responses, and (ii) limitations placed upon use of AI interfaces, in the interest of preserving academic integrity. (i) Students each select a different census subdivision (municipality) to start, to ensure responses are unique from each other; results can then be compared and contrasted in discussion posts in relation to the weekly concepts and readings, allowing students to learn from each other in a low-stakes environment; knowledge about their selected place can be built upon week-to-week in a cumulative fashion. (ii) AI is notoriously flawed at combing data sets like the census to find and interpret specific numbers, so this framework provides one measure for by-passing unauthorized AI usage. There are however potential openings for productive uses of AI here, like performing moderately complex mathematical or statistical calculations that can assist for the purposes of comparison, but whereby knowing how to perform such calculations is not itself part of the expected learning outcomes for first year sociology. Overall, a learning framework that incorporates census data can help students meaningfully connect what they are learning and reading about with lived realities.

This paper will be presented at the following session: