Seeing Ourselves in the Data: Situating Data Literacy in Theory-Building and Action-Taking by Youth (Poster 10)

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

Abstract

In this study, we explored how open data could be used to bootstrap theory development and collective understanding around world issues in a Grade 6 class from Canada. Over the span of four months, 22 students conducted data investigations as a knowledge-building community (Scardamalia & Bereiter, 2014) using Knowledge Forum (KF) and the Common Online Data Analysis Platform (CODAP). Students self-organized around global issues, including sexism, poverty, climate change, income inequality, governments, wars, and weapons. Students investigated Canada’s role for each world issue and discussed implications for their personal identities. Our research questions were: (a) To what extent were students able to analyze data using CODAP? (b) How did students’ data investigations facilitate their theory building and epistemic agency?
Data sources included 21 student CODAP notebooks, 152 KF posts, and transcripts of classroom dialogues. To answer the research questions, we first coded all CODAP notebooks for structural complexity and level of graph comprehension (Curcio, 1987; Friel et al., 2001). Students demonstrated a command of structural components of statistical plots (such as regression lines and variable legends) and showed their capability in reading between and even beyond the data.
Second, we examined KF posts related to CODAP notebooks with a focus on the question–data–theory dynamics in the data cycle (Gould et al., 2016). Drawing on their CODAP work, students used KF to share interpretations, ask new questions, and seek ways to raise awareness of world issues. For example, in one data cycle, students sought to understand which countries polluted the most and were surprised to find that North America had the highest CO2 emission per capita. This unexpected finding prompted some students to start a second data cycle on the lack of action taking on climate change, while others sought to organize a social media campaign to inform the public.
Finally, we coded classroom dialogues and found sophisticated transitions among various modes of data investigations such as grasping data basics, theorizing and extending from data, and taking civic actions. Take the Climate Change group for example. This group analyzed a multivariate data set about different world regions. Students discussed basic
concepts such as “per capita” and “kilotons” when necessary. After plotting CO2 emission per capita by regions, they were surprised by North America having the highest number. “It’s not Asia destroying the ice caps. It’s us!” After spending time discussing their cognitive dissonance, students identified possible actions: While one student believed “[adults] don’t take us seriously,” another student suggested “kids are really responsible, even more than adults,” who “have all these groups [with hidden agendas].”
Our study demonstrated the promise of situating data science in students’ knowledge building and civic engagement. Under supportive conditions, 12-year-olds were capable of engaging in productive analysis of authentic data sets, sophisticated interpretations of multivariate graphs, and collaborative discourse around evidence-based claims – making local
and global connections in the process. Their relationship with data ultimately deepened their understanding of world issues and empowered them to dispel political rhetoric and take action for public good.

Authors

• Bodong Chen, University of Pennsylvania

• Leanne Ma, University of Toronto

• Ben Peebles, University of Toronto - OISE