Learning engineering: Difference between revisions

Revision as of 23:24, 13 March 2022

Revision as of 14:33, 7 July 2022

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Supporting learners as they learn is complex, and design of learning experiences and support for learners usually requires interdisciplinary teams. [[File:Learning Engineering is Multidisciplinary.png|thumb|Supporting learners as they learn is complex, and design of learning experiences and support for learners usually requires interdisciplinary teams.]] Learning engineers themselves might specialize in designing learning experiences that unfold over time, engage the population of learners, and support their learning; automated data collection and analysis; design of learning technologies; design of learning platforms; improve environments or conditions that support learning; or some combination. The products of learning engineering teams include on-line courses (e.g., a particular MOOC), software platforms for offering online courses, learning technologies (e.g., ranging from physical manipulatives to electronically-enhanced physical manipulatives to technologies for simulation or modeling to technologies for allowing immersion), after-school programs, community learning experiences, formal curricula, and more. Learning engineering teams require expertise associated with the content that learners will learn, the targeted learners themselves, the venues in which learning is expected to happen, educational practice, software engineering, and sometimes even more.

Learning engineering teams employ an iterative design process for supporting and improving learning. Initial designs are informed by findings from the [[learning sciences]]. Refinements are informed by analysis of data collected as designs are carried out in the world. Methods from [[learning analytics]], [[design-based research]], and rapid large-scale experimentation are used to evaluate designs, inform refinements, and keep track of iterations.<ref>{{Cite web|last1=Dede|first1=Chris|last2=Richards|first2=John|last3=Saxberg|first3=Bror|date=2018|title=Learning Engineering for Online Education: Theoretical Contexts and Design-Based Examples|url=https://www.routledge.com/Learning-Engineering-for-Online-Education-Theoretical-Contexts-and-Design-Based/Dede-Richards-Saxberg/p/book/9780815394426|access-date=2020-07-21|website=Routledge & CRC Press|language=en}}</ref><ref>{{Cite documentjournal|last=Saxberg|first=Bror|s2cid=12156278|date=April 2017|title=Learning Engineering: Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale|language=EN|doi=10.1145/3051457.3054019}}</ref><ref>{{Cite documentjournal|last1=Koedinger|first1=Ken|s2cid=29186611|date=April 2016|title=Learning Engineering: Proceedings of the Third (2016) ACM Conference on Learning @ Scale|language=EN|doi=10.1145/2876034.2876054}}</ref> According to the [[IEEE Standards Association]]'s IC Industry Consortium on Learning Engineering, "Learning Engineering is a process and practice that applies the learning sciences using human-centered engineering design methodologies and data-informed decision making to support learners and their development."<ref>{{Cite web|title=IEEE ICICLE: A volunteer professional organization committed to the development of Learning Engineering as a profession and as an academic discipline.|url=https://sagroups.ieee.org/icicle/}}</ref>

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== Overview ==

Learning Engineering is aimed at addressing a deficit in the application of science and engineering methodologies to education and training. Its advocates emphasize the need to connect computing technology and generated data with the overall goal of optimizing learning environments.<ref>{{Cite documentjournal|last=Saxberg|first=Bror|s2cid=12156278|date=April 2017|title=Learning Engineering: Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale|language=EN|doi=10.1145/3051457.3054019}}</ref>

Learning Engineering initiatives aim to improve educational outcomes by leveraging computing to dramatically increase the applications and effectiveness of learning science as a discipline. Digital learning platforms have generated large amounts of data which can reveal immediately actionable insights.<ref>{{Cite book|last1=Koedinger|first1=Kenneth|last2=Cunningham|first2=Kyle|last3=Skogsholm|first3=Alida|last4=Leber|first4=Brett|last5=Stamper|first5=John|title=Handbook of Educational Data Mining|date=2010-10-25|chapter=A Data Repository for the EDM Community|series=Chapman & Hall/CRC Data Mining and Knowledge Discovery Series|volume=20103384|pages=43–55|chapter-url=https://www.researchgate.net/publication/254199600|doi=10.1201/b10274-6|isbn=978-1-4398-0457-5}}</ref>