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{{Short description|Interdisciplinary academic field}}
{{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.
'''Learning engineering''' is an interdisciplinary field that employs an iterative design process for improving learning, driven by [[learning sciences]] theory and by data from [[learning analytics]], [[design-based research]], and rapid large-scale experimentation.<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|url-status=live|archive-url=|archive-date=|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, <em>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.</em><ref>{{Cite web|date=|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> ▼
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.
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== History ==
[[Herbert A. Simon|Herbert Simon]], a [[Cognitive psychology|cognitive psychologist]] and [[economist]], first coined the term “learning engineering” in 1967.<ref>{{Cite web|last=Simon|first=Herbert A.|date=Winter 1967|title=The Job of a College President|url=http://digitalcollections.library.cmu.edu/awweb/awarchive?type=file&item=33692|url-status=live|archive-url=|archive-date=|access-date=|website=Carnegie Mellon University University Libraries - Digital Collections}}</ref> However, associations between the two terms “learning” and “engineering” began emerging earlier, in the 1940s<ref name=":0">{{Cite web|last=Watters|first=Audrey|date=2019-07-12|title=The History of the Future of the 'Learning Engineer'|url=http://hackeducation.com/2019/07/12/learning-engineers|url-status=live|archive-url=|archive-date=|access-date=2020-07-21|website=Hack Education|language=en-US}}</ref> and as early as the 1920s.<ref name=":0" /><ref>{{Cite web|last1=Wilcox|first1=Karen E.|last2=Sarma|first2=Sanjay|last3=Lippel|first3=Philip|date=April 2016|title=Online Education: A Catalyst for Higher Education Reforms|url=https://oepi.mit.edu/files/2016/09/MIT-Online-Education-Policy-Initiative-April-2016.pdf|url-status=live|archive-url=|archive-date=|access-date=|website=MIT Online Education Policy Initiative}}</ref> Simon argued that the social sciences, including the field of education, should be approached with the same kind of mathematical principles as other fields like physics and engineering.<ref>{{Cite web|title=The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 1978|url=https://www.nobelprize.org/prizes/economic-sciences/1978/simon/biographical/|access-date=2020-07-21|website=NobelPrize.org|language=en-US}}</ref>▼
=== Early History ===
▲[[Herbert A. Simon|Herbert Simon]], a [[Cognitive psychology|cognitive psychologist]] and [[economist]], first coined the term
=== Formal Recognition as a Process and Practice ===
Simon’s ideas about learning engineering continued to reverberate at Carnegie Mellon University, but the term did not catch on until businessman Bror Saxberg began promoting it in 2014 after visiting Carnegie Mellon University and the [[Pittsburgh Science of Learning Center]], or LearnLab for short. Bror Saxberg brought his team from the for-profit education company, [[Kaplan, Inc.|Kaplan]], to visit CMU. The team went back to Kaplan with what we now call learning engineering to enhance, optimize, test, and sell their educational products. While still at Kaplan, he was an advisor to education-focussed philanthropic initiatives and later joined the
[[Chan Zuckerberg Initiative]] (CZI) as Vice President, Learning Science
Vice President, Learning Science. While at Kaplan Bror Saxberg co-write with [[Frederick M. Hess|Frederick Hess]], founder of the [[American Enterprise Institute]]'s [https://www.aei.org/conservative-education-reform-network/ Conservative Education Reform Network], the 2014 book using the term ''learning engineering''. Then while at the Chan Zuckerberg Initiative Bror Saxberg co-wrote with [[Christopher Dede]] the Timothy E. Wirth Professor in Learning Technologies at the [[Harvard Graduate School of Education]] and John Richards the 2019 book ''Learning Engineering for Online Education''.
==== International Consortium for Innovation and Collaboration in Learning Engineering ====
In 2017, the [[IEEE Standards Association]] formed the [https://sagroups.ieee.org/icicle/about/ IC Industry Consortium on Learning Engineering] as a part of its [https://web.archive.org/web/20191001052508/https://standards.ieee.org/industry-connections/ Industry Connections] program. [[File:Learning Engineering Process with Iterative Design-Build.png|thumb|Learning engineering is an iterative process, informed by data, that starts with a challenge in context. The Creation stage may use iterative human-centered design-build cycles.]] Between 2017 and 2019, ICICLE formed eight Special Interest Groups (SIGs) as a collaborative resource to support the growth of Learning Engineering. The Curriculum, and Credentials SIG chaired by [[Kenneth Koedinger]] pioneered the work on a formal definition of learning engineering. Later work by the Design SIG led by Aaron Kessler led to the development of a learning engineering process model. In 2024 ICICLE changed its name to International Consortium for Innovation and Collaboration in Learning Engineering and became part of the [https://sagroups.ieee.org/ltsc/ IEEE Learning Technology Standards Committee].
==== Enterprise Learning Engineering Center of Excellence ====
On 1 December 2024 the [[U.S. Air Force|United_States_Air_Force]] [[Air Education and Training Command|Air_Education_and_Training_Command]] (AETC) established the [[Enterprise Learning Engineering Center of Excellence|Enterprise_Learning_Engineering_Center_of_Excellence]] (ELE CoE) "to directly support development and
delivery of Mission Ready Airmen and Guardians to Joint Force Commanders with the competencies needed to deter or defeat great power competitors."
The ELE CoE is dedicated to the systematic application of evidence-based principles, scientific methods and practices from the learning sciences, education research, and systems-thinking to produce effective, Airmen-centered learning outcomes and competency acquisition.<ref>{{Cite ["Fact sheet"]|publisher=United States Air Force|date=December 2024|title=Fact Sheet – Enterprise Learning Engineering Center
of Excellence|language=EN|url=https://www.aetc.af.mil/Portals/88/ADC%20Fact%20Sheets/ADC%20Fact%20Sheet_Enterprise%20Learning%20Engineering_6Nov24.pdf||access-date=2025-08-09}}</ref>
== 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
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
Learning Engineering can also assist students by providing automatic and individualized feedback.
[[Carnegie Learning]]’s tool LiveLab, for instance, employs big data to create a learning experience for each student user by, in part, identifying the causes of student mistakes. Research insights gleaned from LiveLab analyses allow teachers to see student progress in real-time.
== Common approaches ==
=== [[A/B testing|A/B Testing]] ===
A/B testing compares two versions of a given program and allows researchers to determine which approach is most effective. In the context of Learning Engineering, platforms like TeacherASSIST<ref>{{Cite web|last=Heffernan|first=Neil
[[Neil Heffernan]]’s work with TeacherASSIST includes hint messages from teachers that guide students toward correct answers. Heffernan’s lab runs A/B tests between teachers to determine which type of hints result in the best learning for future questions.<ref>{{Cite web|last1=Thanaporn|first1=Patikorn|last2=Heffernan|first2=Neil
[https://www.
=== [[Educational data mining|Educational Data Mining]] ===
Educational Data Mining involves analyzing data from student use of educational software to understand how software can improve learning for all students. Researchers in the field, such as [[Ryan S. Baker|Ryan Baker]] at the University of Pennsylvania, have developed models of student learning, engagement, and affect to relate them to learning outcomes.<ref>{{Cite journal|last1=Fischer|first1=Christian|last2=Pardos|first2=Zachary A.|last3=Baker|first3=Ryan Shaun|last4=Williams|first4=Joseph Jay|last5=Smyth|first5=Padhraic|last6=Yu|first6=Renzhe|last7=Slater|first7=Stefan|last8=Baker|first8=Rachel|last9=Warschauer|first9=Mark|s2cid=219091098|date=2020-03-01|title=Mining Big Data in Education: Affordances and Challenges|journal=Review of Research in Education|language=en|volume=44|issue=1|pages=130–160|doi=10.3102/0091732X20903304|issn=0091-732X|doi-access=free}}</ref>
=== Platform Instrumentation ===
Education tech platforms link educators and students with resources to improve learning outcomes.
=== Dataset Generation ===
Datasets provide the raw material that researchers use to formulate educational insights. For example, Carnegie Mellon University hosts a large volume of learning interaction data in LearnLab's DataShop.<ref>{{Cite web
[[Kaggle]], a hub for programmers and open source data, regularly hosts machine learning competitions. In 2019, PBS partnered with Kaggle to create the 2019 Data Science Bowl.<ref>{{Cite web
Datasets, like those hosted by Kaggle PBS and Carnegie Learning, allow researchers to gather information and derive conclusions about student outcomes. These insights help predict student performance in courses and exams.<ref>{{
=== 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
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
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. ▼
== Criticisms of learning engineering ==
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>▼
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? |journal=Journal of the Learning Sciences |volume=32 |issue=2 |pages=288–304 |doi=10.1080/10508406.2022.2100705 |s2cid=251547280 |issn=1050-8406|doi-access=free }}</ref> The criticisms raised include that learning engineering misrepresents the field of [[learning sciences]] and that despite stating it is based on [[cognitive science]], it actually resembles a return to [[behaviorism]]. Others have also commented that learning engineering exists as a form of [[surveillance capitalism]]. Other fields, such as [[Instructional Systems Design|instructional systems design]], have criticized that learning engineering rebrands the work of their own field.
▲
== Challenges for learning engineering teams ==
▲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
A 2021 convening of thirty learning engineers produced recommendations that key challenges and opportunities for the future of the field involve enhancing R&D infrastructure, supporting ___domain-based education research, developing components for reuse across learning systems, enhancing human-computer systems, better engineering implementation in schools, improving advising, optimizing for the long-term instead of short-term, supporting 21st-century skills, improved support for learner engagement, and designing algorithms for equity.<ref>{{Cite report|date=2021|last1=Baker|first1=Ryan|last2=Boser|first2=Ulrich|title=High-Leverage Opportunities for Learning Engineering |url=http://www.upenn.edu/learninganalytics/Learning_Engineering_recommendations.pdf}}</ref>
== See also ==
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== Further reading ==
Mark Lieberman. "[https://www.insidehighered.com/digital-learning/article/2018/09/26/learning-engineers-pose-challenges-and-opportunities-improving Learning Engineers Inch Toward the Spotlight]". Inside Higher Education. September 26, 2018.
== External links ==
* [https://www.cmu.edu/simon/index.html The Simon Initiative]
* [https://sagroups.ieee.org/icicle/ International Consortium for Innovation and Collaboration in Learning Engineering]
[[Category:Neologisms]]
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