As people hear about the emerging field of learning engineering, they are often confused about how this is different than what they may already be doing, especially as an instructional designer or learning experience designer. How can you tell which one you are practicing? Well, the surprising answer is that it depends on how you brush your teeth.
When talking about this confusion with Erin Czerwinski, the Simon Initiative Product and Community Manager at the Open Learning Initiative at Carnegie Mellon University, she offered the analogy of dental care. Erin shared that it wasn’t until middle adulthood that she realized that the purpose in brushing your teeth wasn’t just to move the brush around your teeth for two minutes and call it done. Instead, she now brushes each tooth individually. She compared this to the differences between instructional design and learning engineering: instructional designers follow well-established and evidence-supported procedures for designing and delivering instruction, but they don’t necessarily use data and the learning sciences to maximize learner potential. Let’s unpack this metaphor a bit.
When I brush my teeth, I use an Oral-B toothbrush, which I selected after consultation with my dentist, dental hygienist, and my own consumer research. I gently move the spinning brush head around the entire surface of each individual tooth for 4 seconds before moving on to the next. After the two minutes are up, I rinse my mouth, then run my tongue over my teeth to feel the surface for anything I’ve missed. I then go back and clean up those areas. Making this change has affected my dental health: instead of multiple cavities every year, I haven’t had a cavity in many years. As an educator, I use the learning sciences to select evidence-based approaches to engage my students in learning activities, but I also collect data whenever possible to check their understanding and make modifications to my instruction based on the data. This has improved outcomes for many more of my learners.
I’m also an instructional designer. However, I have learned to adopt the learning engineering mindset and approach in my design practice. ICICLE, the International Consortium for Innovation and Collaboration in Learning Engineering defined learning engineering as:
“a process and practice that . . .
- applies the learning sciences,
- using human-centered engineering design methodologies, and
- data-informed decision-making
. . . to support learners and their development” (Goodell, 2022).
What this means is that I don’t just use traditional models of instructional design, like ADDIE, backwards design, formative feedback, differentiated instruction, etc. to determine how I will help learners achieve the course outcomes. Instead, I use all the tools at my disposal, including those mentioned above as well as searching for ways to instrument learning processes and gains to determine in the moment how students are making sense of course material and adjusting instructional design to accommodate the needs of a particular set of learners, with a particular set of assets and challenges, in a particular context, in a particular learning environment to increase learning outcomes for each student. Each learning context is unique with its own set of assets and challenges, so each course needs to adjust to the students who enroll and the system in which it is offered. We all have a mouth, teeth, and gums, but each is unique with its own actions and needs.
My teeth are different than yours. Sometimes they’re sensitive, so I need to use a prescription toothpaste that helps to conserve the limited amount of fluoride I have on my teeth due to my genetics and well water. Not everyone needs the same toothpaste. Similarly, we can’t just design learning opportunities as a one-size-fits-all product that every learner experiences the same way. Some students have shorter attention spans, slower processing speed, poor vision, more distractions in their environment, lack of interest in the content, or gaps in prerequisite knowledge. I look for ways to surface this information with my students so that I can modify the course to better suit their unique needs. This can be as complex as completing learner analyses and collecting data from the student information system, or as simple as adding an interactive question to a lecture video asking students to tell me how the concept I just presented relates to something they already know or what they want to learn more about.
Learning engineering is a systematic approach to understanding the entire learning context, the unique set of learners and instructors, specifying a particular challenge, and using the learning sciences to design appropriate learning encounters that are instrumented to illuminate the internal workings of the student mind. A dentist can easily look in my mouth to visually locate cavities, but sometimes they need x-rays to see inside or between teeth. Unfortunately, we have not yet invented a scanner that can tell us what students know, so we need to illicit their understanding in other ways. Instead of just using evidence-based instructional practices, learning engineering calls on us to use evidence-generating instructional practices. Effective learning is more about getting information out than it is getting information in. By asking students to share representations of their knowledge, whether it is simply responding to a multiple choice question, answering a deeper prompt verbally or in writing, or even building models of their understanding, we get to see their understanding and help them adjust, but there is the added advantage for the student of strengthening their understanding with effortful recall.
Learning engineering has helped me to identify missed opportunities in exchanges with learners. I see helping students to achieve learning outcomes as challenges to solve, rather than opportunities to impart knowledge. Rather than just creating a set of multiple choice questions to check whether students get the right answer, I now carefully construct distractors for each question that identify misconceptions. I use generative AI to help me develop these. Now, it’s not just whether the student understands a concept, it’s how they understand a concept or why they don’t understand it.
Do you just use the toothbrush your dental hygienist gifts you at the end of your annual cleaning? Do you dutifully move the toothbrush around your mouth because you’ve been told that you need to do this to maintain good dental health? Or do you carefully select the equipment you will use to make sure your teeth are actually clean? Do you pay individual attention to each tooth, check to make sure it is clean, and then remediate when necessary? The answers to these questions may just help you to determine whether you have the mindset of an instructional designer or a learning engineer.
References
Goodell, J. (Ed.). (2022). The learning engineering toolkit: Evidence-based practices from the learning sciences, instructional design, and beyond. Routledge.
