Learning engineering: Difference between revisions

Revision as of 20:33, 20 November 2020

Revision as of 22:04, 1 December 2020

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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|date=|title=Learning Inch Toward the Spotlight|url=https://www.insidehighered.com/digital-learning/article/2018/09/26/learning-engineers-pose-challenges-and-opportunities-improving|url-status=live|archive-url=|archive-date=|access-date=2020-07-21|website=Inside Higher Education|language=en}}</ref>

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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}}</ref>

=== Platform Instrumentation ===

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|last=|first=|date=|title=Flamingo Learning System|url=https://lastinger.center.ufl.edu/innovations/flamingo-learning-system/|url-status=live|archive-url=|archive-date=|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/|url-status=live|archive-url=|archive-date=|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.

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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=JEDM {{!}} Journal of Educational Data Mining|language=en|volume=10|issue=1|pages=36–71|doi=10.5281/zenodo.3344810|issn=2157-2100}}</ref> Tools like ASSISTments<ref>{{Cite web|last=|first=|date=|title=ASSISTments {{!}} Free Education Tool for Teachers & Students|url=https://new.assistments.org/|url-status=live|archive-url=|archive-date=|access-date=2020-07-21|website=ASSISTments}}</ref> alert teachers when students consistently fail to answer a given problem, which keeps students from tackling insurmountable obstacles,<ref name=":1">{{Cite web|last=Heffernan|first=Neil|date=2019-10-09|title=Persistence Is Not Always Productive: How to Stop Students From Spinning Their Wheels - EdSurge News|url=https://www.edsurge.com/news/2019-10-09-persistence-is-not-always-productive-how-to-stop-students-from-spinning-their-wheels|url-status=live|archive-url=|archive-date=|access-date=2020-07-21|website=EdSurge|language=en}}</ref>, promotes effective feedback<ref name=":1" /> and educator intervention, and increases student engagement.

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|last=|first=|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|url-status=live|archive-url=|archive-date=|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|url-status=live|archive-url=|archive-date=|access-date=2020-07-21|website=UC Berkeley - Computing, Data Science, and Society}}</ref>