Making Meaning with Data: Integrating Data Literacy in ELA

• In Event: How Data Science Education Can Enable Exploration of Real World Phenomena

Abstract

Introduction
The rise of data in everyday life has made data literacy—encompassing the ability to access, interpret, create, and use data—essential for all learners (Gummer & Mandinach, 2015). While often associated with STEM, data literacy can also enrich English Language Arts (ELA) by deepening analysis and evidence-based reasoning (Jiang et al., 2022; Lee & Nomura, 2025). In ELA, data can include textual and relational information, positioning students as both producers and interpreters of data (Zhou et al., 2024; Acker & Bowler, 2018). This study examines how student-based texts, learning resources, and teacher facilitation serve as entry points into data literacy in ELA.

Methods & Data Sources
This study took place in two sixth-grade ELA classes (taught by the same teacher; n=29 students), where students engaged in a detective-style activity exploring character traits. Students wrote anonymized paragraphs describing at least three personal characteristics. They then created a network visualization that linked themselves to those traits. Finally, they used both the network and a confidential dossier worksheet to infer the identity behind an assigned anonymous paragraph written by a classmate. Video data were analyzed using interaction analysis (Jordan & Henderson, 1995) to examine how these activities supported students’ reasoning and argumentation.

Results
Three elements supported student engagement with data literacy in ELA: (1) student-generated writing, (2) curricular resources, and (3) teacher facilitation. Students’ own writing became an authentic dataset as they described personal traits, hobbies, and other characteristics. These paragraphs were central to the activity, positioning students as both producers and interpreters of data. The teacher also introduced a network visualization tool, Net.Create (Authors, 2018), which enabled students to map connections between peers and their traits, providing a visual dataset that aided in identifying the authors of paragraphs. Curricular resources, such as the dossier confidential worksheet and Net.Create network, scaffolded students’ reasoning. Students analyzed anonymous paragraphs, completed prompt boxes, and used the visualization to make data-based inferences about their peers. The focus filter in Net.Create allowed students to isolate specific traits (e.g., creativity) and apply process-of-elimination strategies. Lastly, teacher facilitation proved critical; explicit modeling and open-ended questioning encouraged deeper analysis. For instance, the teacher posed real-world analogies to emphasize the importance of evidence-based reasoning over guessing. Modeling Net.Create features on the board demonstrated how to narrow possibilities and make accurate inferences, supporting a robust engagement with data literacy (e.g., combining trait filters, such as “funny” and “short-tempered”).

Significance
This study demonstrates that data literacy can be embedded in ELA by leveraging student-authored texts, curricular resources, and teacher facilitation. Positioning students as both data creators and interpreters draws on their contextual knowledge and agency (Esteban-Guitart & Moll, 2014; Philip et al., 2016). Mediational resources (Vygotsky, 1978) such as worksheets and networks help students recognize patterns and draw inferences from authentic data (Gummer & Mandinach, 2015; Zhou et al., 2024). Teacher mediation—through modeling and strategic questioning—supports evidence-based reasoning. These practices broaden data literacy as a cross-curricular competency, deepening core ELA practices (Kuhn, 2023; Digital Promise, 2025).

Authors

• Chris Cruz-Gonzalez, Indiana University

• Merijke Coenraad, Digital Promise Global

• Selena Steinberg, Indiana University

• Mengxi Zhou, Indiana University

• Joshua Adam Danish, University of Illinois at Urbana-Champaign