Karen Cator, Director, Office of Educational Technology, U.S. Department of Education
Update, October 12, 2012 Final Version Now Available
We are pleased to announce that the final version of this issue brief on improving teaching and learning using new big data methods is now available. Back in April, we released a draft of the report for public comment and had excellent and thoughtful input. The final report, now available on the Department’s website, was able to address some of this input and, for the rest, the Department is taking note for future projects. The report recommends that educators continue to become smart consumers and be more “data curious,” that researchers and developers balance automated decision-making against “human in the loop,” and that meaningful collaborations across sectors be created and sustained. A common thread across all recommendations is careful consideration of educator and student privacy and an overall effort to increase institutional capacity to gather, analyze, research, and act upon big data to improve teaching and learning.
Commerce, entertainment, and social life are amplified more and more across the Web and, as a result, the amount of data generated is skyrocketing. Commercial entities are harvesting this data stream to provide personalized advertisements. Public discourse is trending toward questions such as “What data am I creating, where is it going, and what are we getting from it?”
Big data, it seems, is everywhere—even in education. Researchers and developers of online learning systems, intelligent tutoring systems, virtual labs, simulations, games and learning management systems are exploring ways to better understand and use data from learners’ activities online to improve teaching and learning.
The Office of Educational Technology at the U.S. Department of Education asked SRI to talk to industry experts and convene a panel of researchers to understand the state of the art, the state of the practice, and the emerging field of learning analytics and educational data mining.
We did our best to cover learning at all levels, from early learning to adult; we tried to tease apart educational data mining from learning analytics, and we drew on industry applications to understand what was possible—and promising—in education. Along the way, we also encountered challenges such as, who owns the data? How can data mash-ups support improved understanding? Who does this work and what is needed to succeed? And we present those along with recommendations for work going forward—not only how to collect, analyze, and visualize data, but also how to help people become smarter consumers of data and how to ensure integrity regarding privacy and ethics issues.
We’ve tried to cook the technical into something palatable without watering it down and we think the report will be interesting to many stakeholders. We’re interested in hearing what you think of the result.
We welcome your input as we continue the dialog on what is likely to be a game-changing approach to providing today’s learners with more personalized and effective learning opportunities.
Thanking Our Deliberators
We’d like to thank the experts interviewed for this report, Shelby Sanders (Onsophic Inc.), Linda Chaput (Agile Minds Inc.), Michael Freed and Dror Oren (SRI International), Dave Gutelius (Jive Software), Michael Jahrer and Andreas Toescher (Commendo Inc., Austria), Jeff Murphy (Florida Virtual School), Peter Norvig (Google Inc.), Sunil Noronha (Yahoo! Research Labs), Ken Rudin (Zynga Inc.), Steve Ritter (Carnegie Learning, Inc.), Bror Saxberg and David Niemi (Kaplan Inc.), and Chuck Severance (University of Michigan and Sakai Inc.).
Our technical working group of academic experts in educational data mining and learning analytics also deserves thanks: Ryan S. J. d. Baker (Worcester Polytechnic Institute), Gautam Biswas (Vanderbilt University), John Campbell (Purdue University), Greg Chung (National Center for Research on Evaluation, Standards, and Student Testing, University of California, Los Angeles), Alfred Kobsa (University of California, Irvine), Kenneth Koedinger (Carnegie Mellon University), George Siemens (Technology Enhanced Knowledge Research Institute, Athabasca University, Canada), and Stephanie Teasley (University of Michigan, Ann Arbor).