Learning engineering: Difference between revisions

Revision as of 14:49, 2 November 2020

Revision as of 15:22, 2 November 2020

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Subsequently, the term “learning engineering” has come to emphasize a focus on applied research (rather than foundational or theoretical research), as well as incorporating learning science research in order to improve real-life learning outcomes.<ref>{{Cite web|last=Lieberman|first=Mark|date=|title=Learning Inch Toward the Spotlight|url=https://www.insidehighered.com/digital-learning/article/2018/09/26/learning-engineers-pose-challenges-and-opportunities-improving|url-status=live|archive-url=|archive-date=|access-date=2020-07-21|website=Inside Higher Education|language=en}}</ref>

== Overview ==

Learning Engineering is aimed at addressing a deficit in the application of science and technologyengineering 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 document|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>