Tools

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010 a| 2022015180;

020 a| 9781003276579q| (ebook);

020 z| 9781032208503q| (hardback);

020 z| 9781032232829q| (paperback);

035 a| (WaSeSS)ssj0002612174;

040 a| DLCb| engc| DLCd| WaSeSS;

042 a| pcc;

049 a| EREEa| NEHH;

050 00 a| LB1028.38;

082 00 a| 371.32| 23/eng/20220606;

245 00 a| Learning engineering toolkith| [electronic resource] :b| evidence-based practices from the learning sciences, instructional design, and beyond /c| edited by Jim Goodell with Janet Kolodner ; Foreword by Chris Dede, Harvard University Preface by Bror Saxberg Contributions from Avron Barr, Michelle Barrett, Erin S. Barry, Laura Casey, Jesse Chuang, Scotty D. Craig, Erin Czerwinski, Brandt Dargue, Diana Delgado, Tanvi Domadia, Scott W. Greenwald, Andrew J. Hampton, Daniel Jacobs, Aaron Kessler, Dina Kurzweil, Jodi Lis, Prasad Ram, Brandt Redd, Steve Ritter, Sae Schatz, Jordan Richard Schoenherr, Robert Sottilare, Khanh-Phuong Thai, Richard Tong, and JJ Walcutt.;

260 a| New York :b| Routledge/Taylor & Francis Group,c| 2023.;

300 a| 1 online resource;

504 a| Includes bibliographical references.;

506 a| Available only to authorized users.;

520 a| "The Learning Engineering Toolkit is a practical guide to the rich and varied applications of learning engineering, a process and practice that synthesizes the learning sciences with human-centered engineering design methodologies and data-informed decision-making to support learners and their development. As learning engineering becomes an increasingly formalized discipline and practice, new insights and tools are needed to help education, training, design, and data analytics professionals iteratively develop, test, and improve complex systems for engaging and effective learning. Written in a colloquial style and full of collaborative, actionable strategies, this book explores the essential foundations, approaches, and real-world challenges inherent to ensuring participatory, data-driven, learning experiences across populations and contexts"--c| Provided by publisher.;

538 a| Mode of access: World Wide Web;

588 a| Description based on print version record and CIP data provided by publisher.;

650 0 a| Instructional systemsx| Design.=| ^A160177;

650 0 a| Individualized instruction.=| ^A1341;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Goodell, Jim.?| UNAUTHORIZED;

776 08 i| Print version:t| Learning engineering toolkitd| New York : Routledge, 2023z| 9781032208503w| (DLC) 2022015179;

856 40 z| Full text available from Taylor & Francis eBooksu| https://go.openathens.net/redirector/ecu.edu?url=https%3A%2F%2Fwww.taylorfrancis.com%2Fbooks%2F9781003276579;

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949 a| CLICK ON WEB ADDRESSw| ASISh| JMUSIC60;

596 a| 1 3 4;

998 a| 6674322;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6674322-1001l| JNETm| JOYNERr| Ys| Yt| JNESSBKu| 12/19/2025x| EBOOKz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6674322-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 12/19/2025x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6674322-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 12/19/2025x| EBOOKz| JERESOURCE;

Learning engineering toolkit evidence-based practices from the learning sciences, instructional design, and beyond / edited by Jim Goodell with Janet Kolodner ; Foreword by Chris Dede, Harvard University Preface by Bror Saxberg Contributions from Avron Barr, Michelle Barrett, Erin S. Barry, Laura Casey, Jesse Chuang, Scotty D. Craig, Erin Czerwinski, Brandt Dargue, Diana Delgado, Tanvi Domadia, Scott W. Greenwald, Andrew J. Hampton, Daniel Jacobs, Aaron Kessler, Dina Kurzweil, Jodi Lis, Prasad Ram, Brandt Redd, Steve Ritter, Sae Schatz, Jordan Richard Schoenherr, Robert Sottilare, Khanh-Phuong Thai, Richard Tong, and JJ Walcutt.

Other author	Goodell, Jim.
Format	Electronic
Publication Info	New York : Routledge/Taylor & Francis Group, 2023.
Description	1 online resource
Supplemental Content	Full text available from Taylor & Francis eBooks
Subjects	Instructional systems--Design. Individualized instruction.

Abstract	"The Learning Engineering Toolkit is a practical guide to the rich and varied applications of learning engineering, a process and practice that synthesizes the learning sciences with human-centered engineering design methodologies and data-informed decision-making to support learners and their development. As learning engineering becomes an increasingly formalized discipline and practice, new insights and tools are needed to help education, training, design, and data analytics professionals iteratively develop, test, and improve complex systems for engaging and effective learning. Written in a colloquial style and full of collaborative, actionable strategies, this book explores the essential foundations, approaches, and real-world challenges inherent to ensuring participatory, data-driven, learning experiences across populations and contexts"-- Provided by publisher.
Bibliography note	Includes bibliographical references.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Source of description	Description based on print version record and CIP data provided by publisher.
Issued in other form	Print version: Learning engineering toolkit New York : Routledge, 2023 9781032208503
Genre/form	Electronic books.
LCCN	2022015180
ISBN	9781003276579 (ebook)
ISBN	(hardback)
ISBN	(paperback)

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