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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
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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 document|last=Saxberg|first=Bror|s2cid=12156278|date=April 2017|title=Learning Engineering
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>
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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 ''wheel spinning'' (i.e., not mastering a skill within a set timeframe) and when they are persisting productively.<ref>{{Cite journal|last1=Kai|first1=Shimin|last2=Almeda|first2=Ma Victoria|last3=Baker|first3=Ryan S.|last4=Heffernan|first4=Cristina|last5=Heffernan|first5=Neil|date=2018-06-30|title=Decision Tree Modeling of Wheel-Spinning and Productive Persistence in Skill Builders|url=https://jedm.educationaldatamining.org/index.php/JEDM/article/view/210|journal=
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
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>
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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 Learning Engineer with expertise in multiple disciplines might be sufficient. However, learning engineering problems often exceed any one person’s ability to solve.
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
== See also ==
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