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{{distinguish|Engineering education}}
'''Learning Engineering''' is the systematic application of evidence-based principles and methods from educational technology and the learning sciences to create engaging and effective learning experiences, support the difficulties and challenges of learners as they learn, and come to better understand learners and learning. It emphasizes the use of a human-centered design approach in conjunction with analyses of rich data sets to iteratively develop and improve those designs to address specific learning needs, opportunities, and problems, often with the help of technology. Working with subject-matter and other experts, the Learning Engineer deftly combines knowledge, tools, and techniques from a variety of technical, pedagogical, empirical, and design-based disciplines to create effective and engaging learning experiences and environments and to evaluate the resulting outcomes. While doing so, the Learning Engineer strives to generate processes and theories that afford generalization of best practices, along with new tools and infrastructures that empower others to create their own learning designs based on those best practices.
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; 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 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><ref>{{Cite document|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,
== History ==
[[Herbert A. Simon|Herbert Simon]], a [[Cognitive psychology|cognitive psychologist]] and [[economist]], first coined the term
Simon’s ideas about learning engineering continued to reverberate at Carnegie Mellon University, but the term did not catch on until Bror Saxberg began using it in 2014
.<ref>{{Cite book|last1=Hess|first1=Frederik|last2=Saxberg|first2=Bror|date=2014|title= Breakthrough Leadership in the Digital Age: Using Learning Science to Reboot Schooling |publisher= Corwin Press |isbn= 9781452255491}}</ref> A clear line can be drawn from Simon to Saxberg. In 1978, Herb Simon helped bring [[John Robert Anderson (psychologist)|John Anderson]] to Carnegie Mellon and Anderson soon began to test his theory of cognition within intelligent tutoring systems. In 1998, [[Carnegie Learning]] was spun off producing the first widespread use of intelligent tutoring systems in K12 schools. In 2004, [[Kenneth Koedinger]] and [[Kurt Vanlehn]] started the [[Pittsburgh Science of Learning Center]], or LearnLab for short. Bror Saxberg brought his team from Kaplan to visit CMU. The team went back to Kaplan, armed with LearnLab’s KLI framework,<ref>{{cite journal |last1=Koedinger|first1=Ken|last2=Corbett |first2=Albert|last3=Perfetti|first3=Charles|date=2012 |title=Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning |url=http://pact.cs.cmu.edu/pubs/Koedinger,%20Corbett,%20Perfetti%202012-KLI.pdf|journal=Cognitive Science |volume=36 |issue=5 |pages=757–798|doi=10.1037/a0031955}}</ref> a theoretical framework linking cognition and instruction. They began executing what we now call learning engineering to enhance, optimize, and test their educational products. Bror Saxberg would later co-write the 2014 book using the term
Subsequently, the term
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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
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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=== Learning Engineering in Practice ===
Combining education theory with data analytics has contributed to the development of tools that differentiate between when a student is
Studies have found that Learning Engineering may help students and educators to plan their studies before courses begin. For example, UC Berkeley Professor Zach Pardos uses Learning Engineering to help reduce stress for community college students matriculating into four-year institutions.<ref>{{Cite web|date=2019-09-30|title=Zach Pardos is Using Machine Learning to Broaden Pathways from Community College|url=https://www.ischool.berkeley.edu/news/2019/zach-pardos-using-machine-learning-broaden-pathways-community-college|access-date=2020-07-21|website=UC Berkeley School of Information|language=en}}</ref> Their predictive model analyzes course descriptions and offers recommendations regarding transfer credits and courses that would align with previous directions of study.<ref>{{Cite web|last=Hodges|first=Jill|date=2019-09-30|title=This is Data Science: Using Machine Learning to Broaden Pathways from Community College {{!}} Computing, Data Science, and Society|url=https://data.berkeley.edu/news/data-science-using-machine-learning-broaden-pathways-community-college|access-date=2020-07-21|website=UC Berkeley - Computing, Data Science, and Society}}</ref>
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Similarly, researchers Kelli Bird and Benjamin Castlemen’s work focuses on creating an algorithm to provide automatic, personalized guidance for transfer students.<ref>{{Cite web|last1=Castleman|first1=Benjamin|last2=Bird|first2=Kelli|title=Personalized Pathways to Successful Community College Transfer: Leveraging machine learning strategies to customized transfer guidance and support|url=https://www.povertyactionlab.org/evaluation/personalized-pathways-successful-community-college-transfer-leveraging-machine-learning|access-date=2020-07-21|website=The Abdul Latif Jameel Poverty Action Lab (J-PAL)|language=en}}</ref> The algorithm is a response to the finding that while 80 percent of community college students intend to transfer to a four-year institution, only roughly 30 percent actually do so.<ref>{{Cite web|last1=Ginder|first1=S.|last2=Kelly-Reid|first2=J.E.|last3=Mann|first3=F.B.|date=2017-12-28|title=Enrollment and Employees in Postsecondary Institutions, Fall 2016; and Financial Statistics and Academic Libraries, Fiscal Year 2016: First Look (Provisional Data)|url=https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2018002|access-date=2020-07-21|website=National Center for Employment Statistics|language=EN}}</ref> Such research could lead to a higher pass/fail rate<ref>{{Cite journal|last1=Kakish|first1=Kamal|last2=Pollacia|first2=Lissa|date=2018-04-17|title=Adaptive Learning to Improve Student Success and Instructor Efficiency in Introductory Computing Course|url=https://www.researchgate.net/publication/324574230}}</ref> and help educators know when to intervene to prevent student failure or drop out.<ref>{{Cite web|last=Delaney|first=Melissa|date=2019-05-31|title=Universities Use AI to Boost Student Graduation Rates|url=https://edtechmagazine.com/higher/article/2019/05/universities-use-ai-boost-student-graduation-rates|access-date=2020-07-21|website=Technology Solutions That Drive Education|language=en}}</ref><ref>{{Cite journal|last1=Kakish|first1=Kamal|last2=Pollacia|first2=Lissa|date=2018-04-17|title=Adaptive Learning to Improve Student Success and Instructor Efficiency in Introductory Computing Course|url=https://www.researchgate.net/publication/324574230}}</ref>
== Challenges ==
The multidisciplinary nature of learning engineering creates challenges. The problems that learning engineering attempts to solve often require expertise in diverse fields such as [[software engineering]], [[instructional design]], [[___domain knowledge]], [[pedagogy]]/[[andragogy]], [[psychometrics]], [[learning sciences]], [[data science]], and [[systems engineering]]. In some cases, an individual
Each discipline brings its own metaphors and use of 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 document|date=2020|last1=Chandler|first1=Chelsea|last2=Kessler|first2=Aaron|last3=Fortman|first3=Jacob|title=Language Matters:Exploring the Use of Figurative Language at ICICLE 2019 {{!}} IEEE IC Consortium on Learning Engineering {{!}} Proceedings of the 2019 Conference on Learning Engineering|url=http://sagroups.ieee.org/icicle/wp-content/uploads/sites/148/2020/07/ICICLE_Proceedings_Learning-Engineering.pdf}}</ref>
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