Learning engineering
Methods from learning analytics, design-based research, and rapid large-scale experimentation are used to evaluate designs, inform refinements, and keep track of iterations.
[8] 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.
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 Hess, founder of the American Enterprise Institute's Conservative Education Reform Network, the 2014 book using the term learning engineering.
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.
Digital learning platforms have generated large amounts of data which can reveal immediately actionable insights.
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.
[15] 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.
[20] Their datasets range from sources like Intelligent Writing Tutors[21] to Chinese tone studies[22] to data from Carnegie Learning’s MATHia platform.
Datasets, like those hosted by Kaggle PBS and Carnegie Learning, allow researchers to gather information and derive conclusions about student outcomes.
[24] 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.
For example, UC Berkeley Professor Zach Pardos uses Learning Engineering to help reduce stress for community college students matriculating into four-year institutions.
[29] Similarly, researchers Kelli Bird and Benjamin Castlemen’s work focuses on creating an algorithm to provide automatic, personalized guidance for transfer students.
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.
