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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.
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 journal|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 journal|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|doi-access=free}}</ref> According to the [[IEEE Standards Association]]'s IC Industry Consortium on Learning Engineering, "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."<ref>{{Cite web|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>
== History ==
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In 2017, the [[IEEE Standards Association]] form 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].
== 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 journal|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>
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[[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]] ===
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=== [[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 ===
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[[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|title=2019 Data Science Bowl|url=https://kaggle.com/c/data-science-bowl-2019|access-date=2020-07-21|website=Kaggle|language=en}}</ref> The DataScience Bowl sought machine learning insights from researchers and developers, specifically into how digital media can better facilitate early-childhood STEM learning outcomes.
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 ===
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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>
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 name="researchgate.net">{{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
== Criticisms of learning engineering ==
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== 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 Learning Engineer with expertise in multiple disciplines might be sufficient. However, learning engineering problems often exceed any one person’s ability to solve.
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>
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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]▼
▲== External links ==
[https://sagroups.ieee.org/icicle/ International Consortium for Innovation and Collaboration in Learning Engineering] ▼
▲* [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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