Assessing Data Literacy in K--12 Education: Challenges and Opportunities

2026-03-22

AI Summary

This study addresses the ambiguity in K–12 teachers’ understanding of data literacy, which hinders their ability to design assessments that effectively capture its core components—particularly in the application of data visualization. Through interviews with 13 teachers and drawing on theories from data visualization, human-computer interaction, and the learning sciences, the research systematically identifies four central challenges in assessing data literacy: conceptual confusion, lack of authentic context, misalignment of tools, and insufficient disciplinary integration. Building on these findings, the study proposes actionable assessment design strategies that integrate real-world contexts with subject-specific learning goals. This work offers interdisciplinary theoretical grounding and practical guidance to support educators in implementing effective data literacy instruction.

Technology Category

• Data Mining & Knowledge Management: Data Visualization & Summarization
• Knowledge Representation and Reasoning: Description Logics
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Web data visualization
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Data literacy has become a key learning objective in K--12 education, but it remains an ambiguous concept as teachers interpret it differently. When creating assessments, teachers turn broad ideas about "working with data" into concrete decisions about what materials to include. Since working with data visualizations is a core component of data literacy, teachers' decisions about how to include them on assessments offer insight into how they interpret data literacy more broadly. Drawing on interviews with 13 teachers, we identify four challenges in enacting data literacy in assessments: (1) conceptual ambiguity between data visualization and data literacy, (2) tradeoffs between using real-world or synthetic data, (3) difficulty finding and adapting domain-appropriate visual representations and data visualizations, and (4) balancing assessing data literacy and domain-specific learning goals. Drawing on lessons from data visualization, human-computer interaction, and the learning sciences, we discuss opportunities to better support teachers in assessing data literacy.

Problem

• data literacy
• K–12 education
• assessment
• data visualization
• teacher interpretation

Innovation

• data literacy
• K–12 education
• data visualization
• assessment design
• teacher challenges