Introduction to Learning Engineering
While the field of learning engineering today utilizes modern technologies and methodologies, the phrase “learning engineering” dates to 1967 when it was coined by cognitive psychologist and economist Herbert Simon. Simon believed that education and curriculum development should be driven by mathematical principles in the same way that physics and engineering were.
This vision is possible today. Large scale educational technology platforms are now generating massive amounts of data. Simultaneously, new computer science tools, like improved computation, AI, and natural language processing, enable quicker and more novel data-driven insights. In order to leverage this, learning engineers combine these nascent computer and data advances with theories of learning science to create evidence-based learning systems that may be continuously and rapidly iterated upon to improve student outcomes.
Learning engineering is computer science + data science + learning science, applied in real education settings.
What Learning Engineering Is Not
Ed-tech and learning engineering are not necessarily synonymous. While learning engineering solutions often employ technology as part of their approach, their goal to improve student outcomes is not bounded by technology exclusively. Sometimes software may serve the purpose of iteratively improving the in-person instruction being provided by an educator.
Learning engineering is not learning science alone. Learning engineering seeks to improve the process and efficiency of learning science research and to apply its findings through platform instrumentation and other learning innovations.
Learning Engineering In Practice
The process of learning engineering begins with learning science to provide context and understanding to the learning challenge being addressed. With that understanding in place, human centered and engineering design principles are used to create and iterate on a proposed solution. Next, that solution is engineered, with data collection integrated to allow for analysis of the solution’s effectiveness. Data driven decision making then determines which problems to solve next and/or which designs and technical implementations to further iterate on.
This entire process requires perspectives in:
- Learning Science
- Data Science
- Software Engineering
- Instructional Design
- Learning Environment Engineering
- Education and Training Professional Practices
- Assessment, Measurement, Evaluation
- Subject Matter Expertise
Some common learning engineering approaches in the real world include:
- A/B Testing to determine which of two versions of a given program is most effective.
- Educational Data Mining to draw insights on learning outcomes from student use of educational software.
- Platform Instrumentation of ed-tech solutions for teachers and students.
- Dataset Generation to enable learning science research.