Learning Engineering Toolkit Evidence-Based Practices from the Learning Sciences, Instructional Design, and Beyond (Taylor & Francis, 2023)
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.
English
Keywords: Education; Educational equipment & technology, computer-aided learning (CAL); Higher & further education, tertiary education; Open learning, home learning, distance education; instructional design; user experience design; data analysis; ISLS; International Society of the Learning Sciences; technology-enhanced learning; artificial intelligence; participatory research design; learning design; engineering design; Janet Kolodner; human-centered learning; Association for the Advancement of Computing in Education; IEDMS; online learning; reskilling; E-Learning; learning sciences; Evidence-Based Practices; computer science; learning analytics; big data; Society for Learning Analytics Research; International Educational Data Mining Society; AECT; Association for Educational Communications and Technologies; ICICLE; design-based research; Learning Engineering Toolkit; lean-agile development; SoLAR; IEEE IC Industry Consortium on Learning Engineering; digital learning; course design; upskilling; massive open online courses; human computer interaction; HCI; Jim Goodell; data science; educational data mining; AACE; educational technologies; MOOC