TY  - GEN
AU  - Goodell, Jim (Ed.)
AU  - Barr, Avron
AU  - Barrett, Michelle
AU  - Barry, Erin S.
AU  - Casey, Laura
AU  - Chuang, Jesse
AU  - Craig, Scotty D.
AU  - Czerwinski, Erin
AU  - Dargue, Brandt
AU  - Delgado, Diana
AU  - Domadia, Tanvi
AU  - Greenwald, Scott W.
AU  - Hampton, Andrew J.
AU  - Jacobs, Daniel
AU  - Kessler, Aaron
AU  - Kurzweil, Dina
AU  - Lis, Jodi
AU  - Redd, Brandt
AU  - Ram, Prasad
AU  - Ritter, Steve
AU  - Schatz, Sae
AU  - Schoenherr, Jordan Richard
AU  - Sottilare, Robert
AU  - Thai, Khanh-Phuong
AU  - Tong, Richard
AU  - Walcutt, JJ
TI  - Learning engineering toolkit: evidence-based practices from the learning sciences, instructional design, and beyond
SN  - 9781032232829
U1  - 371.33 GOO 
PY  - 2023///
CY  - New York
PB  - Routledge
KW  - Learning Engineering
KW  - Instructional Design
KW  - Engineering Design
KW  - Education
KW  - Data Analytics
N2  - The Learning Engineering Toolkit is a practical guide to the rich and varied applications of learning engineering, a rigorous and fast-emerging discipline that synthesizes the learning sciences, instructional design, engineering design, and other methodologies to support learners. 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.

https://www.routledge.com/Learning-Engineering-Toolkit-Evidence-Based-Practices-from-the-Learning/Goodell-Kolodner/p/book/9781032232829
ER  -