|Expanding Evidence Approaches for Learning in a Digital World Download the report||The report discusses the promise of sophisticated digital learning systems for collecting and analyzing very large amounts of fine-grained data (“big data”) as users interact with the systems. It proposes that this data can be used by developers and researchers to improve these learning systems and strive to discover more about how people learn. It discusses the potential of developing more sophisticated ways of measuring what learners know and adaptive systems that can personalize learners’ experiences.|
The report describes an iterative R&D process, with rapid design cycles and built-in feedback loops—one familiar in industry but less so in education (however, the report provides numerous examples of applications in education). An iterative R&D process enables early-stage innovations to be rapidly deployed, widely adopted, and—through continuous improvement processes—refined and enhanced over time. This means that data collection and analysis can occur continuously and that users are integral to the improvement process.
The report encourages learning technology developers, researchers, and educators to collaborate with and learn from one another as a means of accelerating progress and ensuring innovation in education.
This report was developed collaboratively, in partnership with a Technical Working Group of learning technologies experts. We wish to thank Eva L. Baker (University of California, Los Angeles), Allan Collins (Northwestern University), Chris Dede (Harvard University), Adam Gamoran (University of Wisconsin), Kenji Hakuta (StanfordUniversity), Anthony E. Kelly (George Mason University), Kenneth R. Koedinger (Carnegie Mellon University), David Niemi (Kaplan, Inc. ), James Pellegrino(University of Illinois, Chicago), William R. Penuel (University of Colorado, Boulder), Zoran Popović (University of Washington), Steve Ritter (Carnegie Learning), Russell W. Rumberger (University of California, Santa Barbara), Russell Shilling (Department of Defense, United States Navy), Marshall S. Smith (The Carnegie Foundation for the Advancement of Teaching) and Phoenix Wang (William Penn Foundation).
The public comment period for this draft report has closed. Please review submitted comments here.