Clinical Practice Resources

This tool from CASEL, adapted from the ATLAS Looking at Data Protocol, presents a structured reflection process for school teams to observe trends and discuss ideas while looking at data.

"When it comes to people—and policy—numbers are both powerful and perilous." Though this New Yorker article isn't just about anti-racism, it talks about the limits of data, and one of those limits is that it can perpetuate bias.

"This book reminds us that data is anything but 'raw,' that we shouldn't think of data as a natural resource but as a cultural one that needs to be generated, protected, and interpreted. The book's essays describe eight episodes in the history of data from the predigital to the digital. Together they address such issues as the ways that different kinds of data and different domains of inquiry are mutually defining; how data are variously "cooked" in the processes of their collection and use; and conflicts over what can -- or can't -- be "reduced" to data."

This Harvard Business Review article argues against the idea that "with enough data, the numbers speak for themselves", stating that "data and data sets are not objective" and "hidden biases in both the collection and analysis stages present considerable risks."

Quantitative research enjoys heightened esteem among policy-makers, media, and the general public. Whereas qualitative research is frequently dismissed as subjective and impressionistic, statistics are often assumed to be objective and factual. We argue that these distinctions are wholly false; quantitative data is no less socially constructed than any other form of research material.

This resource makes the argument for collecting qualititative data in addition to quantitative data. While the intended audience is employers, teachers can consider how this applies to how they gather qualitative data about students.

This article argues why schools should collect and analyze multiple measures of data: demographics, school processes, perceptions, and student learning.

What data should teachers use, and how should they use these data to improve their teaching? In this review, a diverse set of literature that addresses this question is categorized according to three distinct approaches: the diagnostic approach, the methods approach, and the teacher approach. Within the diagnostic approach, teachers collect data to determine the students who need to learn specific content. Within the methods approach teachers collect data to determine the aspects of teaching that contribute to student learning. Within the teacher approach, teachers collect data to help the teacher develop beliefs and knowledge that support effective teaching. Benefits and drawbacks of these approaches are identified, and

EdInstruments is a developing library of educational measurement tools intended to be a resource for scholars, educators, schools, districts and the general public. It includes instruments related to academic knowledge and skills, student well-being, schooling, and home and community.

Education can be transformed if we eradicate our fixation on big data like standardized test scores as the supreme measure of equity and learning. Instead of the focus being on "fixing" and "filling" academic gaps, we must envision and rebuild the system from the student up--with classrooms, schools and systems built around students' brilliance, cultural wealth, and intellectual potential. Street data reminds us that what is measurable is not the same as what is valuable and that data can be humanizing, liberatory and healing. By breaking down street data fundamentals: what it is, how to gather it, and how it can complement other forms of data to guide a school or district's equity journey, Safir and Dugan offer an actionable framework for school transformation.

This paper "[identifies] key problems with the techno-rational logic of accountability policies and reflect[s] on the ways in which they influence teachers’ data-use practices."

This article examines the way in which teachers explain the causes of the outcomes observed in data. The authors' findings "yield implications for (a) the effectiveness of data use reforms and (b) equity in the education of ELLs and students in special education."

"Data-driven decision making continues to be a common feature of educational reform agendas across the globe. In many U.S. schools, the teacher team meeting is a key setting in which data use is intended to take place, with the aim of planning instruction to address students’ needs. However, most prior research has not examined how the use of data shapes teachers’ dialogue about their students’ ability and achievement. This study examines how teachers talk about student ability and achievement in the era of data-driven decision making and how their talk is shaped by the use of data within teams, their school contexts, and broader accountability systems."

This is a catalog of the various biases that affect health science. While this is related to health science rather than education, the definitions provided are still very helpful.

This article from the Atlantic discusses several cognitive biases that affect all humans (including confirmation bias), and the difficulties in combating those biases. This article discusses biases broadly, not just in the context of data analysis.

This paper proposes a system for mitigating bias: accept that we are biased, label the type of bias, and mitigate using the right process.

A review that categorizes literature on data driven instruction according to three distinct approaches: the diagnostic approach, the methods approach, and the teacher approach.

"Gathering data and using it to inform instruction is a requirement for many schools, yet educators are not necessarily formally trained in how to do it. This book helps bridge the gap between classroom practice and the principles of educational psychology."

This paper describes results of a qualitative study of teachers’ data use. The study looked at teachers’ data use for instruction to examine the extent to which instruction was aligned with established content standards and assessment.

This literature review identifies several misconceptions about data-based decision making, addresses the misconceptions, and makes recommendation for changes in policy and practice.