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Learning engineering

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Learning Engineering is an interdisciplinary field that employs learning analytics and rapid large-scale experimentation in educational environments.[1] Its application of computer science methods to education helps to better understand the science of learning.

History

Herbert Simon, a cognitive psychologist and economist, first coined the term “learning engineering” in 1967.[2] However, associations between the two terms “learning” and “engineering” began emerging earlier,e in the 1940s[3] and as early as the 1920s.[3][4] 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.[5]

Subsequently, the term “learning engineering” has come to emphasize a focus on applied research (rather than foundational or theoretical research), as well as incorporating learning science research in order to improve real-life learning outcomes.[6]

Overview

Learning Engineering is aimed at addressing a deficit in the application of science and technology to education. Its advocates emphasize the need to connect computing technology and generated data with the overall goal of optimizing learning environments.[7]

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.[8]

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 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 “off-task behavior”[9] or “wheel spinning”[10] to better understand student engagement and predict whether individual students are likely to fail.

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.[11]

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 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[12] and Coursera use A/B testing to determine which type of feedback is the most effective for learning outcomes.[13]

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.[14][15]

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 Baker at the University of Pennsylvania, have developed models of student learning, engagement, and affect to relate them to learning outcomes.[16]

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[17], a platform that integrates critical functionalities like resources and teaching management systems along with a community-based forum.[18]

Other platforms like MATHia, Algebra Nation, LearnPlatform, 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 on the Pittsburgh Science of Learning Center DataShop.[19] Their datasets range from sources like Intelligent Writing Tutors[20] to Chinese tone studies[21] to data from Carnegie Learning’s MATHia platform.

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.[22] 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.[23]

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.[24] Tools like ASSISTments[25] alert teachers when students consistently fail to answer a given problem, which keeps students from tackling insurmountable obstacles[26], promotes effective feedback[26] 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.[27] Their predictive model analyzes course descriptions and offers recommendations regarding transfer credits and courses that would align with previous directions of study.[28]

Similarly, researchers Kelli Bird and Benjamin Castlemen’s work focuses on creating an algorithm to provide automatic, personalized guidance for transfer students.[29] 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.[30] Such research could lead to a higher pass/fail rate[31] and help educators know when to intervene to prevent student failure or drop out.[32][33]

See also

References

  1. ^ Dede, Chris; Richards, John; Saxberg, Bror (2018). "Learning Engineering for Online Education: Theoretical Contexts and Design-Based Examples". Routledge & CRC Press. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  2. ^ Simon, Herbert A. (Winter 1967). "The Job of a College President". Carnegie Mellon University University Libraries - Digital Collections.{{cite web}}: CS1 maint: url-status (link)
  3. ^ a b Watters, Audrey (2019-07-12). "The History of the Future of the 'Learning Engineer'". Hack Education. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  4. ^ Wilcox, Karen E.; Sarma, Sanjay; Lippel, Philip (April 2016). "Online Education: A Catalyst for Higher Education Reforms" (PDF). MIT Online Education Policy Initiative.{{cite web}}: CS1 maint: url-status (link)
  5. ^ "The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 1978". NobelPrize.org. Retrieved 2020-07-21.
  6. ^ Lieberman, Mark. "Learning Inch Toward the Spotlight". Inside Higher Education. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  7. ^ Saxberg, Bror (April 2017). "Learning Engineering | Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale" (Document). doi:10.1145/3051457.3054019. {{cite document}}: Cite document requires |publisher= (help); Unknown parameter |s2cid= ignored (help)
  8. ^ Koedinger, Kenneth; Cunningham, Kyle; Skogsholm, Alida; Leber, Brett; Stamper, John (2010-10-25). "A Data Repository for the EDM Community". Handbook of Educational Data Mining. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. Vol. 20103384. pp. 43–55. doi:10.1201/b10274-6. ISBN 978-1-4398-0457-5.
  9. ^ Cocea, Mihaela; Hershkovitz, Arnon; Baker, Ryan S.J.d. "The Impact of Off-task and Gaming Behaviors on Learning: Immediate or Aggregate?" (PDF). Penn Center for Learning Analytics.{{cite web}}: CS1 maint: url-status (link)
  10. ^ Beck, Joseph E.; Gong, Yue (2013). Lane, H. Chad; Yacef, Kalina; Mostow, Jack; Pavlik, Philip (eds.). "Wheel-Spinning: Students Who Fail to Master a Skill". Artificial Intelligence in Education. Lecture Notes in Computer Science. 7926. Berlin, Heidelberg: Springer: 431–440. doi:10.1007/978-3-642-39112-5_44. ISBN 978-3-642-39112-5.
  11. ^ Milliron, Mark David; Malcolm, Laura; Kil, David (Winter 2014). "Insight and Action Analytics: Three Case Studies to Consider". Research & Practice in Assessment. 9: 70–89. ISSN 2161-4210.
  12. ^ Heffernan, Neil. "TEACHER ASSIST". sites.google.com. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  13. ^ Saber, Dan (2018-06-15). "How A/B Testing Powers Pedagogy on Coursera". Medium. Retrieved 2020-07-21.
  14. ^ Thanaporn, Patikorn; Heffernan, Neil. "Effectiveness of Crowd-Sourcing On-Demand Tutoring from Teachers in Online Learning Platforms". Google Docs. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  15. ^ "Programme". Learning @ Scale 2020. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  16. ^ Fischer, Christian; Pardos, Zachary A.; Baker, Ryan Shaun; Williams, Joseph Jay; Smyth, Padhraic; Yu, Renzhe; Slater, Stefan; Baker, Rachel; Warschauer, Mark (2020-03-01). "Mining Big Data in Education: Affordances and Challenges". Review of Research in Education. 44 (1): 130–160. doi:10.3102/0091732X20903304. ISSN 0091-732X. S2CID 219091098.
  17. ^ "Flamingo Learning System". University of Florida Lastinger Center. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  18. ^ Poekert, Phil (2019-10-28). "Poekert: At the University of Florida, We Have Lots of Data on Students and Math. Now, We Need Researchers to Help Us Mine It". The 74 Million. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  19. ^ "Datashop". Pittsburgh Science of Learning Center Datashop. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  20. ^ "Intelligent Writing Tutor". Pittsburgh Science of Learning Center Datashop. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  21. ^ "Chinese tone study". Pittsburgh Science of Learning Center Datashop. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  22. ^ "2019 Data Science Bowl". Kaggle. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  23. ^ Baker, Ryan S.J.D. (2010). "Data mining for education" (PDF). International Encyclopedia of Education. 7: 112–118. doi:10.1016/B978-0-08-044894-7.01318-X.
  24. ^ Kai, Shimin; Almeda, Ma Victoria; Baker, Ryan S.; Heffernan, Cristina; Heffernan, Neil (2018-06-30). "Decision Tree Modeling of Wheel-Spinning and Productive Persistence in Skill Builders". JEDM | Journal of Educational Data Mining. 10 (1): 36–71. doi:10.5281/zenodo.3344810. ISSN 2157-2100.
  25. ^ "ASSISTments | Free Education Tool for Teachers & Students". ASSISTments. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  26. ^ a b Heffernan, Neil (2019-10-09). "Persistence Is Not Always Productive: How to Stop Students From Spinning Their Wheels - EdSurge News". EdSurge. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  27. ^ "Zach Pardos is Using Machine Learning to Broaden Pathways from Community College". UC Berkeley School of Information. 2019-09-30. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  28. ^ Hodges, Jill (2019-09-30). "This is Data Science: Using Machine Learning to Broaden Pathways from Community College | Computing, Data Science, and Society". UC Berkeley - Computing, Data Science, and Society. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  29. ^ Castleman, Benjamin; Bird, Kelli. "Personalized Pathways to Successful Community College Transfer: Leveraging machine learning strategies to customized transfer guidance and support". The Abdul Latif Jameel Poverty Action Lab (J-PAL). Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  30. ^ Ginder, S.; Kelly-Reid, J.E.; Mann, F.B. (2017-12-28). "Enrollment and Employees in Postsecondary Institutions, Fall 2016; and Financial Statistics and Academic Libraries, Fiscal Year 2016: First Look (Provisional Data)". National Center for Employment Statistics. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  31. ^ Kakish, Kamal; Pollacia, Lissa (2018-04-17). "Adaptive Learning to Improve Student Success and Instructor Efficiency in Introductory Computing Course". {{cite journal}}: Cite journal requires |journal= (help)
  32. ^ Delaney, Melissa (2019-05-31). "Universities Use AI to Boost Student Graduation Rates". Technology Solutions That Drive Education. Retrieved 2020-07-21.{{cite web}}: CS1 maint: url-status (link)
  33. ^ Kakish, Kamal; Pollacia, Lissa (2018-04-17). "Adaptive Learning to Improve Student Success and Instructor Efficiency in Introductory Computing Course". {{cite journal}}: Cite journal requires |journal= (help)

Further reading

Mark Lieberman. "Learning Engineers Inch Toward the Spotlight". Inside Higher Education. September 26, 2018.

The Simon Initiative

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