Building a Conceptual Framework for Data Literacy

• Published in: Teachers College record (1970) Vol. 117; no. 4; pp. 1 - 22
• Main Authors: Gummer, Edith, Mandinach, Ellen
• Format: Journal Article
• Language: English
• Published: New York Teachers College, Columbia University 01-04-2015
• Subjects: Best Practices; Capacity Building; Data Analysis; Data Collection; Data Use; Decision Making; Definitions; Educational Facilities Improvement; Evaluation Methods; Evidence; Evidence Based Practice; Formative Evaluation; Information; Information Literacy; Information Utilization; Knowledge; Knowledge Level; Literacy; Pedagogical Content Knowledge; Pedagogy; Preservice Teachers; Qualitative Research; Research Skills; Skills; Teacher Certification; Teacher Competencies; Teaching methods; Teaching Skills
• ISSN: 0161-4681; 1467-9620
• DOI: 10.1177/016146811511700401

Background: The increasing focus on education as an evidence-based practice requires that educators can effectively use data to inform their practice. At the level of classroom instructional decision making, the nature of the specific knowledge and skills teachers need to use data effectively is complex and not well characterized. Being able to characterize this requisite knowledge and skills supports definition and measurement of data literacy. Evolving from empirical analyses, an emergent conceptual framework of knowledge and skills is proposed for the construct, data literacy for teaching. The framework is based on a domain analysis, which is the first step of an evidence-centered design process for data literacy. The framework is contextualized in existing research, with an objective of having it ground future work in the development of instruments to measure data literacy. Purpose: This article reports on work to develop a conceptual framework to undergird research, development, and capacity building around data literacy for teaching. The emergent nature of the framework is intended to inform the discussions around data literacy so that continued refinement of operational definitions of the construct will emerge. Without such operational definitions, measurement of progress toward teacher data literacy is not possible. Research Design: The conceptual framework is based on a sequence of qualitative studies that sought to determine the nature of knowledge and skills that are required for teachers to be considered data literate. A first study examined the ways that the knowledge and skills around the use of data were characterized in practical guides, books, and manuals on data use, formative assessment, and related topics. These characteristics were integrated with definitions of data literacy elicited from experts. A second study examined the licensure and certification documents required by states for teacher candidates for their treatment of data- and assessment-related knowledge and skills. The synthesis of these studies and their components have yielded an evolving conceptual framework for a new construct: data literacy for teachers. Conclusions: The conceptual framework described in this article reflects an evolving effort to understand what it means for teachers to be data literate--that is, what knowledge and skills are required for teachers to use data effectively and responsibly set within an iterative inquiry cycle. The work posits that the construct comprises three interacting domains (data use for teaching, content knowledge, and pedagogical content knowledge), six components of the inquiry cycle (identify problems, frame questions, use data, transform data into information, transform information into a decision, and evaluate outcomes), and, finally, 59 elements of knowledge and skills embedded within those components. However, the complex construct requires additional discussion from policy, research, and practitioners to refine and reorganize it and to expand it beyond a cognitive focus on knowledge and skills to include beliefs/values, identity, and epistemic elements. Next steps will include structuring an ongoing discussion about the nature of the framework and expansion beyond domain analysis through the evidence-centered design process to the development of a suite of instruments to measure the construct.