How can researchers, practitioners, and technology developers best use the wealth of data generated by the increasing use of technology in schools? We’ve been among the many people wrestling with this question over the last five years, and our efforts have led us to a surprisingly simple answer: The best way to use data from digital learning environments for the purposes of educational improvement is in collaboration with the practitioners who will take action based on the data.
Collaborations among researchers and practitioners have long been framed as an ideal, but the elements of what makes for an effective collaboration are often not well understood, and the funding for such work has been scarce. Recently, however, both government and private research funders have become interested in collaborations under the label of research-practice partnerships. As partnerships have proliferated, collective knowledge and wisdom about how to implement successful partnerships has grown. Our new book Learning Analytics Goes to School (see preview here) describes a particular kind of partnership that is focused on leveraging complex data from various technologies to improve teaching and learning.
The book distills insights gathered over the course of dozens of cycles of inquiry involving work with K-12 schools, community colleges, and youth-serving organizations. Learning Analytics Goes to School offers an account of the supporting conditions and key phases involved in what we refer to as collaborative data-intensive improvement. We begin the book with an introduction to the developing fields of learning analytics and educational data mining and end it with calls to action for new as well as experienced researchers, practitioners, and technology developers. In between, we, along with our co-author Marie Bienkowski from the SRI Center for Technology in Learning, provide accessible descriptions of the new sources of data fueling data-intensive research in education, the application of new analytical techniques, and the importance of students’ privacy in using data for research and improvement. We’ve worked to avoid pat exhortations to be “data driven” and instead to provide a historical account of data use in schools to ground our approach for working directly with practitioners to analyze, make sense of, and take action on data spread across various learning technologies and databases.
Along with useful background information and historical framings, we offer advice for launching data-intensive research-practice partnerships. Based on first-hand experiences leading multiple partnerships, Learning Analytics Goes to School describes the importance of cultivating trust between researchers and practitioners, using explicit improvement methods, creating learning events for partnership members, and developing common workflows for sharing and analyzing data.
The core lesson of Learning Analytics Goes to School is that data is a necessary but not sufficient condition for creating more equitable and effective learning environments. We hope researchers and practitioners alike can use the book as an on-ramp to the exciting possibilities of data-intensive research in education.