In his keynote address at last month’s Learning Innovation Summit, Chan Zuckerberg Institute VP of Learning Science, Dr. Bror Saxberg, treated attendees to the sort of presentation only a seasoned learning scientist with both a medical degree (an M.D. from Harvard, no less) and an engineering background (Bror received his Ph.D. in Electrical Engineering and Computer Science from MIT) could deliver. The presentation, titled “Show me the Evidence: The Art of Applying Learning Science in Learning Innovation,” brought together principles of learning design, brain science, and years of industry knowledge. It was a deeply thought-provoking primer on the power of learning design at scale, and ways in which we need to approach teaching and learning in the digital age to best serve learners.
With kind-hearted zingers, Dr. Saxberg shared memorable examples of how instructors and the edtech industry have gotten it wrong in the past. His message throughout the presentation was clear, though: when we design learning experiences grounded in learning science, we have the potential to make an enormous positive impact on students.
To do so, we need to follow the data produced by rigorous education research. This sort of research and application is exactly what Bror is spearheading at the Chan Zuckerberg Initiative. At CZI, he works on ways to expand and apply learning science results and good learning measurement practice in real-world learning situations across the full span of education — K12, higher education, and beyond.
Here are the top three new things I learned about brains, technology, and learning design from Bror’s fascinating #LIS18SV keynote. The full video is available below — let me know your favorite kernels of wisdom from Bror’s talk on Twitter @smart_sparrow!
1. Brains are inconvenient
Bror explained that, despite our desires, “Brains don’t bucket.” Instead, he says you need many different styles of instructional design to reach a learner, not simply kinesthetic for Sally and auditory for Billy. “A lot of research about learning demonstrates how darn inconvenient that is,” he chuckled.
Instead of treating brains like they’re easily classifiable, Bror says that learning designers need to start with how learning actually works, not how you wish it worked. “An awful lot of instructional design and technology is based around what we wish were true about learning.”
One step to fixing this oversight involves exploring the division of labor required by different tasks between working memory and long-term memory. Bror encouraged attendees to be cognizant of these restraints when we design. For example, it’s possible to do many various things with your working memory while also utilizing your long-term memory, like making a mental grocery list while driving your car, but it’s almost impossible to accomplish two different tasks, both with working memory. This neurological constraint is critical for learning designers to consider when presenting material to students.
2. With great tech comes great responsibility
“The technology doesn’t care,” cautions Bror. “It will take bad learning solutions and make them more affordable, more reliable, more available, and even more data-rich.”
How do we counteract this? With good learning solutions, of course! Technology alone is just technology. “You want to power [technology] with good solutions,” says Bror, which leaves us with a difficult task. “You have to figure out: what are those good solutions?”
Sharing ways to evaluate solutions and shining examples of the same comprised much of the Learning Innovation Summit. It is indeed worth noting, however, the disservices that technological advances have done to learners in the past 15–20 years. Bror’s perspective gave me a new sense for the weight of the work we do in the edtech community, and that of instructors and learning designers.
3. Good learning solutions aren’t one-and-done
Like all good things, good learning solutions take time. And like good studies, they also take many reliably-collected data points and iterations for continuous improvement. “A lot more evidence is coming out of these [educational] technologies,” Bror pointed out, “which is giving us a deeper view of learning.” The true advantage to this, as Bror sees it, is the opportunity for learning solutions to be dynamic.
We need to use the data offered by digital learning platforms such as Smart Sparrow to make progress and to iterate, instead of “just taking a one-shot run at it.” Furthermore, the data most important to continuous cycles of improvement is much more than test and assignment scores. Smart Sparrow’s platform, for example, offers data on student attempts, time spent on questions, and common misconceptions, which are all meant to support instructors in this process of refining their digital learning design. To fine tune learning experiences even further, assessments can encompass more elusive metrics such as attention and engagement, a focus for the growing field of psychometrics.
Ready to learn more?
This was just one of the many insightful topics discussed during the summit. Check out even more presentations from LIS 2018 — a full playlist of expert talks, including the full LIS panel, is available now.