Although elementary school-aged children use and have interest in technologies with integrated Artificial Intelligence applications, they rarely critically investigate the sociopolitical contexts in which people consume and produce AI technology. Without engaging in this form of critical computing, elementary school students will not be prepared to participate ethically in a digitally reliant society and tackle the increasingly discriminatory affects of algorithmic decision-making as they continue their schooling and careers.

For examples of how AI applications can harm marginalized population and what advocacy groups are doing about it, visit the Algorithmic Justice League.

In S.P.O.T, learners interact with ML within real-life sociopolitical contexts and examine how ML predictions impact their daily lives and communities. Through the immersion of stories that mirror children’s lived experiences, S.P.O.T. provides elementary school aged children with opportunities to learn how machine learning applications function and develop children’s abilities to critically examine, question, and reimagine the consequences of ML decisions in the real world.

Computational Data Science for Children

Data Science PopUps

To make data science accessible and flexible for elementary school teachers, we co-created Data Science Pop-Ups with our teacher partners. The project goals were to make data science education accessible and of interest to rural, elementary students in South Carolina, including students with high-incidence disabilities, to increase participation in computer science education, and broaden ways to hone computational thinking skills. See more information on the collaboration with Clemson University and University of Georgia on the project website.

Using R to Visualize and Make Claims About Data

In this project, we developed an 8-session introductory data science course for middle grades (ages 11 – 13), and observed how participants learned data science practices and processes through the combination of non-programming activities and programming activities using the language R. We designed activities that were learner-centered and allowed students to create “objects-to-think-with” such as R code or visual data representations. The curriculum was designed as an interdisciplinary approach between statistics and computer programming.

The first half of the course was an introduction to data science that aimed to enhance statistical thinking and help students understand how data can be used to gain information. The last half of the course was dedicated to a final project in which students identified existing, publicly available data sets and analyzed their data using R. Students then presented their projects in an expo-style format on the last day in which their parents and family were invited to attend.

Participatory Quantitative Ethnography

Broadening Participation in Data Analytics

Participatory quantitative ethnography has emerged as QE scholars have recognized the need to support equitable researcher-participant relations in the co-construction of knowledge. Calls for imagining and designing possibilities for such collaboration agree on the potential of already existing tools, such as Epistemic Network Analysis (ENA), within QE.

Traditionally, ENA has been used by researchers as it was created for data analysis tasks, typically conducted by research experts. In recognizing that ENA can be expanded to accommodate researcher and participants’ interactions, we have developed a tool for this purpose.

Connect supports researcher-participant co-interpretation of discourse data. The online tool is a collaborative space hosted in R Shiny App, where researchers and participants can analyze data together by creating networks based on themes or codes from the data. Researchers and participants can work together to discuss and debate how to create data visualizations and interpretations that are supported by evidence in the discourse data.