Learning Engineering is more than a process,
it is a guiding principle for our work at Yet.
Proud to Support a New Generation of Learning Engineering.
When the IEEE received a proposal in 2017 for the creation of a new consortium for Learning Engineering, the proposal was sent from our email address.
What is Learning Engineering?
“Recognizing a need to support the development of learning engineering as a profession and an academic discipline, in December 2017 the Standards Association of the Institute of Electrical and Electronics Engineers (IEEE) approved the creation of a new program. They called it the IEEE IC Industry Consortium on Learning Engineering — otherwise known as ICICLE.”
— Shelly Blake-Plock, ICICLE charter chair, in EDUCAUSE Review (2018)
Defined
IEEE’s International Consortium for Innovation and Collaboration in Learning Engineering defines “Learning Engineering” as “a process and practice that applies the learning sciences using human-centered design and engineering methodologies and data-informed decision making to support learners and their development.”
In Practice
In our view at Yet, the key features of the process and practice as relate to scalable (and meaningful) data solutions are:
the design of quality learning data models and the software to support them
the standards-based instrumentation of learning technology ecosystems
experimentally-sound data model and learning systems evaluation
value-adding iteration based on thoughtful engineering principles
All four of these features should be considered in regard to the improvement of scalable learning outcomes. And all four are what we would consider illustrative of the real-world technical implementation of Learning Engineering to scalable data solutions.
The Learning Engineering Paradigm
“Note that Learning Engineering is not about technology in the sense that it might be misconstrued as a catch-all term for edtech and learning technologies. And it is not a novel buzzword for Instructional Design, as much of what goes on in a Learning Engineering solution — especially in the realm of technical data instrumentation and global standards alignment — is either parallel to, complementary towards, or wholly outside the realm of Instructional Design. Rather, Learning Engineering is an exercise whereby one should first engage problems in the mind’s eye — calibrating the required points of contact between what presumably goes on in a learning experience, what can more descriptively be known about what goes on in a learning experience through the capturing and analysis of formative data, and what technical (or practical) capabilities would provide the most elegant and secure manner of capturing such data and modeling it for the realization of efficient and meaningful use either by humans or by machines — whereby the objective is to improve learning outcomes and to scale one’s improvements. All of this idealized, it is then the task to identify and collaborate with those who have the myriad skills required to turn solution concepts into solution implementations. And then, and only then, is it time to apply those solution implementations in the physical (or digital) world in ways that are testable and which are necessary to meet the objectives.” — Shelly Blake-Plock, Notes on Learning Engineering (2024)
Solutions developed by Yet Analytics are designed to support the Learning Engineering process.
Always questioning. Always seeking a more elegant solution. Always arguing that the driving factor must be learning outcomes. Always demanding better of ourselves. Always driven to develop software that will provide a scalable solution.
When we started up Yet back in 2014, we did so with a conviction that we would make the world’s learning data more accessible, visible, and actionable. Fast-forward to today and see that we have built a suite of software products that puts ever more relevant capabilities in the hands of today’s learning engineers and learning technology teams.
Let’s work together.
To get started, reach out via form or by email to team@yetanalytics.com. Our team can support you in your process either through program design and advisory services or by providing software engineers to augment your team.
Tools for Learning Engineering: from Data Design to Instrumentation to Evaluation
Centriph
Supporting data design.
Use Centriph to design data structures and to model learning experiences as xAPI Profiles. This will increase the ability to produce interoperable machine-readable data.
SQL LRS
Supporting data instrumentation.
Align your data event stream to an xAPI Profile and validate the xAPI data output in SQL LRS. Unique to Yet’s learning record store is an onboard conditional logic machine.
DATASIM
Supporting data evaluation.
Use DATASIM to model xAPI data and to generate synthetic data scenarios. Understand the range of possible outcomes based on your data design and instrumentation.
Our contributions to selected research in the space
Blake-Plock, S., Ponton, J., Kaelber, A. (2021). From IdeaScale to Scaling an Idea: Learning Innovation at Joint Base Andrews. iFEST 2021. National Training and Simulation Association. Alexandria, VA.
Blake-Plock, S., Hoyt, W., & Casey, C. (2021). Instrumenting GIFT with xAPI: a use case for IEEE P9274.3.x standards activity and implications for the broader field of ITS and AIS. Proceedings of the Ninth Annual GIFT Users Symposium. US Army Combat Capabilities Development Command - Soldier Center. Orlando, FL.
Blake-Plock, S., Goodell, J., Kurzweil, D., Kessler, A., Olsen, J. (Eds.) (2020). IEEE IC Industry Consortium on Learning Engineering: Proceedings of the 2019 Conference on Learning Engineering. The Institute of Electrical and Electronics Engineers, Inc. New York, NY.
Blake-Plock, S. (2019). Point Paper. Panel: Learning Engineering, A New Academic Discipline and Engineering Profession. I/ITSEC. National Training and Simulation Association. Orlando, FL.
Blake-Plock, S. (2018). ICICLE: A Consortium for Learning Engineering. EDUCAUSE Review.
Blake-Plock, S. (2018). Learning Engineering: Merging Science and Data to Design Powerful Learning Experiences. GettingSmart.
Barr, A., McNamara, D., Saxberg, B., Blake-Plock, S. (2024). Learning Engineering: Past, Present, and Future (Panel Discussion). IEEE LTSC ICICLE 2024 Conference on Learning Engineering. Arizona State University. Phoenix, AZ.
Torrance, M., Blake-Plock, S., McCormick, M., Parent, A., Saxberg, B., Wagner, E. (2024). The Journey to Learning Engineering (Panel Discussion). IEEE LTSC ICICLE 2024 Conference on Learning Engineering. Arizona State University. Phoenix, AZ.
Blake-Plock, S., Johnson, A., Owens, K., B. Goldberg. (2023). STEEL-R: Connecting Synthetic Training and Experiential Learning to the Total Learning Architecture and IEEE LTSC Data Standards. IEEE IC Industry Consortium on Learning Engineering 2023 Conference. Carnegie Mellon University. Pittsburgh, PA.
Blake-Plock, S., Owens, K., Goodell, J. (2023). The Value Proposition of GIFT for the Field of Learning Engineering. Proceedings of the Eleventh Annual GIFT Users Symposium. US Army Combat Capabilities Development Command - Soldier Center. Orlando, FL.
Owens, K., Blake-Plock, S., Goodell, J. (2023). Learning Engineering Virtual Training Systems with Learning Science, Data Standards, and a Capabilities Maturity Model. I/ITSEC. National Training and Simulation Association. Orlando, FL.
Further reading
Podcast (coming soon)
Research & Development
Capabilities Statement