Fostering Awareness of Big Data Through a Quantified-Self Approach: Designing for Critical Data Literacy (Poster 3)

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

Abstract

We live in a data-driven society (Pentland, 2013) in which traces of everyday digital activities
are inconspicuously collected, stored, and repurposed; this continuous production of data bears
ramifications on how educational systems should prepare students to thrive in such a world
(Wilkerson & Polman, 2020; Wise, 2019). Children’s online data contributions generate big
data sets which can be exploited by those who possess and manage them. A growing body of
research discusses possible threats to children’s physical and psychological safety and has
highlighted children’s low awareness of the inherent risks that come with the use of smart
technologies and online media. Scholars suggest that researchers and educational designers
should focus on how to help students develop an understanding of this complex reality using a
critical data literacies lens (Pangrazio & Selwyn, 2020; Stornaiuolo, 2020).
We see the process of students’ empowerment through critical data literacy as
challenging and multilevel, developed through the following steps: (1) Developing awareness of
data privacy and data mining issues; (2) Cultivating a deeper critical understanding of how
current business models and the data economy work; (3) Questioning data mining practices and
own data contributions to the data economy; and (4) Adopting informed behaviors and resilient
practices, including knowing how to change the data mining landscape.
In previous work (Authors, 2021) we examined the extent to which elementary school
students are aware of their data traces and the potential for exploitation. To support students in
becoming aware we designed a learning module, which adopted a quantified-self pedagogical
approach where students unobtrusively generated data with activity trackers, which then became
the springboard for reflecting on how the data economy might be using such data. In this work,
we first draw data from two design iterations with 84 students (n1:21, n2:63) and explain how
we personalized and contextualized the students’ interactions with the data using two problem-
solving contexts. The first iteration aimed to help students understand the multiple interests in
personal data traces through a stakeholder approach. Results showed that students had
difficulties in interpreting the data representations from their own physical activities and lacked
the motivation to discuss them. In the second iteration we designed three personally relevant
and meaningful scenarios for students to discuss and customized the data representations to
make them more age-appropriate. The result was a more meaningful context for students’
discussions of personal data. The analysis of students’ discourse also revealed persistent
challenges in understanding quantified data such as heart rate, in relation to one’s health
situation, raising the question whether the data representations were still difficult to interpret.
Recent research has found that children are often unable to interpret or contextualize the data
that wearables record (Oygür et al., 2021). We supplement our results with the presentation of a
focused data collection from 37 grades 4-8 students in which we explore data literacy as a
prerequisite of contextualization to develop a more critical interpretation of the shared
ownership of online data traces.

Authors

• Andria Agesilaou, Cyprus University of Technology

• Eleni A. Kyza, Cyprus University of Technology