The Role of Data Literacy in Convergence and Divergence of Reasoning Criteria Between Social Groups

• In Event: Data Literacy in Context: Culturally Oriented and Place-Based Learning Through Data

Abstract

Do, and how do sociocultural interpretive frameworks influence data-based reasoning around everyday functional issues? We explore how individuals from two social groups who differ in their educational background, worldview, and daily life practices reason through functional questions concerning policy and personal practices related to COVID-19: What criteria do they consider? Do they incorporate publicly available data in their reasoning?
We compare two poles of religious identification—secular and ultra-orthodox—in Israel, where, among the Jewish residents, religiosity is a central dimension of identification, as well as a dimension of social and political divisiveness (Peri et al., 2012). There is ongoing controversy over whether the government should mandate a core-curriculum in K-12 education in the ultra-orthodox community (Katzir & Perry-Hazan, 2019). Data literacy is implicated in this controversy as one of the topics that the secular public claims is neglected in typical ultra- orthodox education.
We draw on data from a questionnaire (Tabak & Dubovi, 2021) distributed to a large sample (N=500) of the Jewish public in Israel (individuals who self-identify as traditional or religious, in between the two poles, are not included here). Participants reported on their information seeking habits and beliefs prior-to and during COVID-19. Participants answered a series of questions assessing their proficiency in interpreting quantitative data representations, as well as a series of functional COVID-19 related questions (e.g., should schools have reopened), which presented participants with a data set and/or graphical representation like those that appear in news media. Our goal was to investigate the criteria that people consider and whether the public incorporates data in reasoning about functional and consequential issues (Feinstein et al., 2013; Lee & Dubovi, 2020).
At the backdrop of our analysis are two scales of learning: the data literacy readiness with which individuals are equipped following formal schooling, and the take-up of information-seeking and data-interpretation practices brought about and shaped by the COVID- 19 information ecology. We focus primarily on two open-ended questions that asked participants to consider, based on data provided, whether schools should have reopened following a second large-scale lockdown, and what face covering they should choose to use. We examine similarities and differences in criteria used by the two groups of focus, and how they associate with data literacy. We combine a quantitative analysis of open-ended responses that gauges whether people incorporated data in their reasoning, with a qualitative analysis of these responses that distills the themes embedded in participants’ criteria and reasoning (e.g., lack of control: “things will happen no matter what measures we take”).
We found both similarities and differences across questions. Overlaps in data interpretation practices seem to explain these patterns of convergence and divergence. In this poster, we present our analysis and findings, and consider implications for education that can cultivate shared data practices, while also allowing for plural world views (Tabak & Weinstock, 2008).

Authors

• Ilana Dubovi, Tel Aviv University

• Iris Tabak, Ben-Gurion University of the Negev