Learning engineering: Difference between revisions

Revision as of 14:33, 7 July 2022

Revision as of 20:19, 21 August 2022

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[[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|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|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|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>

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 marketing 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. Bror Saxberg would later 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''.

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 ''learning engineering''. It caught on this time.

Subsequently, the term ''learning engineering'' has come to emphasize a focus on applied research (rather than foundational or theoretical research), as well as incorporating research findings about how people learn in order to support learning and improve real-life learning outcomes.<ref>{{Cite web|last=Lieberman|first=Mark|title=Learning Inch Toward the Spotlight|url=https://www.insidehighered.com/digital-learning/article/2018/09/26/learning-engineers-pose-challenges-and-opportunities-improving|access-date=2020-07-21|website=Inside Higher Education|language=en}}</ref>

== Overview ==

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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.

Education tech platforms link educators and students with resources to improve learning outcomes. For example, Phil Poekert at the [[University of Florida College of Education]]’s Lastinger Center for Learning has created Flamingo,<ref>{{Cite web|title=Flamingo Learning System|url=https://lastinger.center.ufl.edu/innovations/flamingo-learning-system/|access-date=2020-07-21|website=University of Florida Lastinger Center}}</ref> a platform that integrates critical functionalities like resources and teaching management systems along with a community-based forum.<ref>{{Cite web|last=Poekert|first=Phil|date=2019-10-28|title=Poekert: At the University of Florida, We Have Lots of Data on Students and Math. Now, We Need Researchers to Help Us Mine It|url=https://www.the74million.org/article/poekert-at-the-university-of-florida-we-have-lots-of-data-on-students-and-math-now-we-need-researchers-to-help-us-mine-it/|access-date=2020-07-21|website=The 74 Million|language=en-US}}</ref>

Other platforms like [https://www.carnegielearning.com/products/software-platform/mathia-learning-software/ MATHia], [https://www.algebranation.com/ms/what-is-algebra-nation/?ref=about Algebra Nation], [https://learnplatform.com/about-us LearnPlatform], [https://coursekata.org/ coursekata], and [[ALEKS]] offer interactive learning environments created to align with key 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|title=Datashop|url=https://pslcdatashop.web.cmu.edu/index.jsp|access-date=2020-07-21|website=Pittsburgh Science of Learning Center Datashop}}</ref> Their datasets range from sources like Intelligent Writing Tutors<ref>{{Cite web|title=Intelligent Writing Tutor|url=https://pslcdatashop.web.cmu.edu/Project?id=18|access-date=2020-07-21|website=Pittsburgh Science of Learning Center Datashop}}</ref> to Chinese tone studies<ref>{{Cite web|title=Chinese tone study|url=https://pslcdatashop.web.cmu.edu/Project?id=4|access-date=2020-07-21|website=Pittsburgh Science of Learning Center Datashop}}</ref> to data from [[Carnegie Learning]]’s MATHia platform.

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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 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 book|date=2020|last1=Chandler|first1=Chelsea|last2=Kessler|first2=Aaron|last3=Fortman|first3=Jacob|chapter=Language Matters: Exploring the Use of Figurative Language at ICICLE 2019 |publisher=IEEE IC Consortium on Learning Engineering |title=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. |date=2022-08-12 |title=Learning sciences and learning engineering: A natural or artificial distinction? |url=https://doi.org/10.1080/10508406.2022.2100705 |journal=Journal of the Learning Sciences |volume=0 |issue=0 |pages=1–17 |doi=10.1080/10508406.2022.2100705 |issn=1050-8406}}</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 on 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.

EachStill disciplineothers bringshave itscommented owncritically metaphorson andlearning 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|date=2020 |last1=Chandler |first1=Chelsea |url=http://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 |chapterdate=Language Matters: Exploring the Use of Figurative Language at ICICLE 20192020 |publisher=IEEE IC Consortium on Learning Engineering |titlechapter=ProceedingsLanguage ofMatters: Exploring the 2019Use Conferenceof onFigurative LearningLanguage Engineering|url=http://sagroups.ieee.org/icicle/wp-content/uploads/sites/148/2020/07/ICICLE_Proceedings_Learning-Engineering.pdfat ICICLE 2019}}</ref>

== 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>