Skip to main content
Usable Knowledge

What Big Data Means

The rise of data science could dramatically expand what we know about learning, teaching, and schooling, a new report finds.
What Big Data Means

For HGSE Professor Chris Dede, the rise of data science in education research is a potentially transformative development in our understanding of how people learn — and how best to teach them.

Dede compares what’s happening now with data-intensive research in education to the inventions of the microscope and the telescope. “Both of these devices revealed new types of data that were always present, but never before accessible,” he says. “We now have the equivalent of the microscope and the telescope for understanding learning, teaching, and schooling in powerful ways. What was previously invisible can now be studied and shaped.”

We now have the equivalent of the microscope and the telescope for understanding learning, teaching, and schooling in powerful ways. — Chris Dede Harvard Graduate School of Education
In a significant new report compiled and edited for the Computing Research Association (CRA), Dede shows that — just as science and engineering have used technology-enriched, data-intensive research to probe fundamental questions — education is primed to grapple with its own driving questions. The STEM fields, replete with examples of successful research strategies, can stand as a model for how education can do it — and how federal support could help.

In the report, Data-Intensive Research in Education: Current Work and Next Steps [PDF], Dede summarizes case studies presented earlier this year at two workshops hosted by the CRA for the National Science Foundation. Those workshops helped to inventory the resources necessary for successful data-driven initiatives, describing models of effective partnerships between producers of big data and its consumers, inside and outside of education. (HGSE Professor Andrew Ho was a key speaker at the second workshop.)

Dede outlines seven broad themes — criteria necessary for success — for scholars, funders, policymakers, practitioners, and other stakeholders in education research:

  • Mobilize communities around opportunities based on new forms of evidence. 
    Before launching a new research effort, stakeholders should identify important educational issues for which richer evidence would lead to better decision-making. Establishing common goals and a shared vocabulary will ensure that projects yield more useful results. 
  • Infuse evidence-based decision-making throughout a system.
    Data do not exist in a vacuum. Each type of data is part of a complex system, and each can lead to improved decision-making along the way. For example, data analytics about instruction can be used on a small scale, providing real-time feedback in a single classroom, or on a large scale, involving many different institutions. To increase the impact of evidence-based education, a common set of assessments is necessary for straightforward aggregation and comparison.
  • Develop novel forms of educational assessment.
    New ways of measuring learning — in new kinds of digital environments — can dramatically change both learning and assessment, providing easily aggregated evidence for decision-making.  
  • Work to reconceptualize how data is generated, collected, stored, and framed for various types of users.
  • Develop new types of analytic methods to enable rich findings from complex forms of educational data.
    As one workshop leader wrote, “Integrating different forms of data—from peer grading, to mastery-based assessments, to ungraded formative assessments, to participation in social forums—gives an unprecedented level of diversity to the data.” Breakthroughs in analytic methods will help us understand it.
  • Build human capacity to do data science and to use its products.
    Education needs more people with expertise in data science and data engineering, and all stakeholders must become sophisticated consumers of data-intensive research in education. Few data science education programs now exist, and most educational research programs don’t require data literacy beyond a graduate statistics course.
  • Develop advances in privacy, security, and ethics.
    It is clearly essential to reassure stakeholders about how educational data is collected, safeguarded, and used. A risk-based approach, similar to the approach taken by the National Institute of Standards and Technologies in guidelines for federal agencies, would address confidentiality, consent, and security concerns.

The report aims to provide a sensible blueprint for managing research projects and deploying their results, Dede says. “As always, findings and evidence from research may or may not be used well in practice and policy,” he cautions. “Psychological, political, and cultural issues always shape and influence which evidence is valued, which is ignored. Hopefully, we can find ways to use data-intensive research wisely for educational decision making.”


Get Usable Knowledge — Delivered
Our free monthly newsletter sends you tips, tools, and ideas from research and practice leaders at the Harvard Graduate School of Education. Sign up now.

Usable Knowledge

Connecting education research to practice — with timely insights for educators, families, and communities

Related Articles