Much attention has been devoted recently to improving teacher preparation and teachers’ skills and knowledge—in particular, their ability to use data to inform their practice. But in conversations around the topic, two key terms, data literacy and assessment literacy, are often mistakenly conflated. This blurriness is common in high-profile venues, particularly among the policymakers and professional organizations that publish documents outlining standards and recommendations for teacher knowledge and skills.
Conflating the two terms implies that the only data teachers should use are from assessments. That’s inaccurate.
Assessment literacy < data literacy
What’s the difference between the two skills? Data literacy is much more than assessment literacy. In fact, assessment literacy is a component of data literacy. Last year, WestEd convened a group of 55 experts in data-driven decision making, formative assessment and related fields. The objective of the meeting was to reach consensus on a definition of data literacy. We came close, with about 95 percent agreement and five percent noise.
We also asked the experts to depict the commonalities, differences and overlap between assessment and data literacy. The majority agreed that assessment literacy is subsumed within the broader construct of data literacy.
The call to action here is simple: Practitioners need a clear message. Schools of education need to understand what data-related skills teachers need.
Diverse data sources, diverse data skills
Granted that assessments are typically at the heart of teachers’ arsenals of data. But in order for teachers to form a comprehensive understanding of their students, they need more than just assessment data (a.k.a., test scores.) They need diverse sources of data, such as demographics, attendance, health, behavior, attitude, welfare, observations, classroom activities and even transportation data. These data are in addition to all the traditional student performance and assessment data.
Moreover, data literacy in the context of teaching is about more than looking at reports to analyze data and see patterns. It implies a broad set of skills and knowledge. It encompasses both pedagogical and content knowledge. Teachers must transform data into information and ultimately into actionable knowledge. They must put it to use in the ways they manage their classrooms, the ways they approach instruction and the ways they present content. In fact, through in-depth analyses of the experts’ definitions and state licensure requirements, we identified almost 60 skills that comprise data literacy.
Our efforts led to a concise definition of data literacy:
Pedagogical data literacy or data literacy for teaching is the ability to transform information into actionable instructional knowledge and practices by collecting, analyzing, and interpreting all types of data (assessment, school climate, behavioral, snapshot, etc.) to help determine instructional steps. It combines an understanding of data with standards, disciplinary knowledge and practices, curricular knowledge, pedagogical content knowledge, and an understanding of how children learn.
Confusion among policymakers and professional organizations
Now, back to the policymakers and professional organizations that use the terms interchangeably… policymakers’ rhetoric continues to stress that teachers must be able to use data. Professional organizations are likewise beginning to focus on the importance of data use, yet often say “assessment literacy” when they really mean “data literacy.”
- The Council of Chief State School Officers’ (CCSSO) 2012 report, “Our Responsibility Our Promise: Transforming Educator Preparation and Entry into the Profession,” refers to data use almost exclusively in the context of assessments.
- The new Interstate Teacher Assessment and Support Consortium (InTASC) standards and learning progressions also heavily reference the notion of assessment literacy. The broader concept of data literacy is woven throughout the document; however, the actual term and definition are not formally introduced until the glossary.
- Meanwhile, in a recent webinar, Council for the Accreditation of Educator Preparation (CAEP) president Jim Cibulka correctly used the term data literacy, emphasizing the concept that teachers and teacher preparation programs must use data-driven decision making for continuous improvement.
At certain points in time, having a united front matters. In education, as requirements around classroom data usage continue to evolve, now is one of those times. Why?
- Because we’re at a critical juncture in the history of teaching. New capacities around data use and analysis have the potential to radically alter educators’ abilities to reach students with the right information at the right time. We can’t miss this opportunity.
- Because changing professional standards of behavior is both complex and time consuming. If we lack a crisp definition of the skill we’re asking teachers to master (data literacy versus assessment literacy), confusion will abound, and the time horizons and effort need to shift practice will be magnified.
- Because students are much more than their test scores. Policy and teacher training standards around data must explicitly acknowledge that.
- Because instruction must not be usurped by assessment. Instead, instruction, assessment, and related data must form a tight feedback loop to inform the educational process.
The call to action here is simple: Practitioners need a clear message. Schools of education need to understand what data-related skills teachers need. As requirements around classroom data usage continue to evolve, it’s incumbent upon standards setting organizations to be clear and to establish a common language. Our definition offers a starting place.
Ellen Mandinach is a leading expert in data-driven decision making, focusing on understanding how educators are using data to inform practice. She serves as Senior Research Scientist and Director of the Data for Decisions Initiative at WestEd.
Read Ellen’s related posts, Licensure as a lever to drive education data literacy and The capacity gap: Grad schools & education data literacy.