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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>
The Learning Engineering field has the further potential to communicate educational insights automatically available to educators. For example, learning engineering techniques have been applied to the issue of [[Dropping out|drop-out]] or high failure rates. Traditionally, educators and administrators have to wait until students actually withdraw from school or nearly fail their courses to accurately predict when the drop out will occur. Learning engineers are now able to use data on ''off-task behavior''<ref>{{Cite web|last1=Cocea|first1=Mihaela|last2=Hershkovitz|first2=Arnon|last3=Baker|first3=Ryan S.J.d.|title=The Impact of Off-task and Gaming Behaviors on Learning: Immediate or Aggregate?|url=https://www.upenn.edu/learninganalytics/ryanbaker/CoceaHershkovitzBakerFinal.pdf|website=Penn Center for Learning Analytics}}</ref> or ''wheel spinning''<ref>{{Cite
This data enables educators to spot struggling students weeks or months prior to being in danger of dropping out. Proponents of Learning Engineering posit that data analytics will contribute to higher success rates and lower drop-out rates.<ref>{{Cite journal|last1=Milliron|first1=Mark David|last2=Malcolm|first2=Laura|last3=Kil|first3=David|date=Winter 2014|title=Insight and Action Analytics: Three Case Studies to Consider|url=https://eric.ed.gov/?id=EJ1062814|journal=Research & Practice in Assessment|language=en|volume=9|pages=70–89|issn=2161-4210}}</ref>
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== Criticisms of Learning Engineering ==
Researchers and educational technology commentators have published critiques of learning engineering. <ref name=":0" /><ref>{{Cite journal |last=Lee |first=Victor R.|author-link = Victor R. Lee|date=2022-08-12 |title=Learning sciences and learning engineering: A natural or artificial distinction?
Still others have commented critically on learning engineering's use of metaphors and figurative language. Often a term or metaphor carries a different meaning for professionals or academics from different domains. At times a term that is used positively in one ___domain carries a strong negative perception in another ___domain.<ref>{{Cite book |last1=Chandler |first1=Chelsea |url=https://sagroups.ieee.org/icicle/wp-content/uploads/sites/148/2020/07/ICICLE_Proceedings_Learning-Engineering.pdf |title=Proceedings of the 2019 Conference on Learning Engineering |last2=Kessler |first2=Aaron |last3=Fortman |first3=Jacob |date=2020 |publisher=IEEE IC Consortium on Learning Engineering |chapter=Language Matters: Exploring the Use of Figurative Language at ICICLE 2019}}</ref>
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