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      Learning Engineering: A View on Where the Field Is at, Where It’s Going, and the Research Needed


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          Although the idea of learning engineering dates back to the 1960s, there has been an explosion of interest in the area in the last decade. This interest has been driven by an expansion in the computational methods available both for scaled data analysis and for much faster experimentation and iteration on student learning experiences. This article describes the findings of a virtual convening brought together to discuss the potential of learning engineering and the key opportunities available for learning engineering over the next decades. We focus the many possibilities into ten key opportunities for the field, which in turn group into three broad areas of opportunity. We discuss the state of the current art in these ten opportunities and key points of leverage. In these cases, a relatively modest shift in the field’s priorities and work may have an outsized impact.

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              Knowledge tracing: Modeling the acquisition of procedural knowledge


                Author and article information

                Technology, Mind, and Behavior
                American Psychological Association
                March 31, 2021
                : 3
                : 1
                [1]Graduate School of Education, University of Pennsylvania
                [2]The Learning Agency, Washington, DC, United States
                [3]Independent Consultant, Wayne, Pennsylvania, United States
                Author notes
                Action Editor: Danielle S. McNamara was the action editor for this article.
                Acknowledgments: The authors also wish to acknowledge the support of Nidhi Nasiar, Kayla Meyers, and Rina Madhani. The authors would like to thank Tom Kalil and Kumar Garg, who provided invaluable help and support. Many colleagues gave thoughtful suggestions and feedback including David Porcaro, Bror Saxberg, Gouri Gupta, Caitlin Mills, Chris Dede, Scott Crossley, Ben Motz, John Whitmer, Pior Mitros, and all of the participants in the 2020 Asynchronous Virtual Convening on Learning Engineering.
                Funding: The preparation of this document and the asynchronous virtual convening were supported by a grant from Schmidt Futures, a philanthropic effort founded by Eric and Wendy Schmidt.
                Disclosures: The authors declare no conflicts of interest in this work.
                [*] Ryan S. Baker, Graduate School of Education, University of Pennsylvania, 3700 Walnut Street, Philadelphia, PA 19104, United States ryanshaunbaker@gmail.com
                [*] Ulrich Boser, The Learning Agency, Washington, DC 20001, United States ulrichboser@gmail.com
                Author information
                © 2022 The Author(s)

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY-NC-ND). This license permits copying and redistributing the work in any medium or format for noncommercial use provided the original authors and source are credited and a link to the license is included in attribution. No derivative works are permitted under this license.


                Education,Psychology,Vocational technology,Engineering,Clinical Psychology & Psychiatry
                feedback,learning engineering,learning,mastery


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