Frameworks, personalization, and actionable insights: learning science principles to support student success during COVID-19 and beyond

“So many of the ways that teachers personalize learning experiences are based on in-person techniques. Online teaching and learning requires tools that can facilitate personalization – and these tools should be rooted in learning science.” – Jeff Bergin

Like most of the principles that we have discussed in this blog series, the pandemic has not created a need for the application of learning science foundations to digital solutions - but it has helped confirm that research-based principles like learning frameworks and models are critical to developing the personalized features and learning analytic insights that are the foundation of effective ed tech.

Jeff Bergin, Vice President of Professional Learning at Western Governors University and his colleagues previously introduced six key principles for learning experience design. Recently, he took time to talk with me about three that have become even more salient as the world navigates education in COVID-19 and beyond.

A conversation with Jeff Bergin, Vice President of Professional Learning, Western Governors University.

KMW: Hi, Jeff – thanks so much for taking time to talk with me today, I’m sure a lot of people are very interested in your thoughts on learning science as we have navigated the pandemic.

JB: Hi, Kara, so happy to be chatting! You know I’ve long been a proponent of designing learning experiences on a foundation of empirically based principles and given what a pivotal point we’re in in education, I couldn’t think of a better time to have this conversation.

KMW: Then let’s jump right into it! You’ve thought and written a lot about aligned learning frameworks and models. Can you talk a little about these foundations? How are these different?

JB: Great question. Frameworks are generally artifacts with statements of what a learner should be able to know or do, while learning models are how they may go about learning. Frameworks are based on learning goals while models are based on learning theories. And once models are tested through impact research, they contribute to our understanding of the theories.

KMW: To be practical, how can instructors and instructional designers use frameworks and models in their everyday teaching or design work?

JB: Practically speaking, frameworks guide assessment items and instructional elements while models guide how those things are delivered. Models guide the entire learning experience. And this is important because, with so many instructors teaching online for the first time and so many students trying to adjust to online learning, it’s easy to get distracted by the technology – and not focus on what really matters: the learning experience students are having.

Personalization in digital learning experiences

KMW: Speaking of learning experiences, do you have any thoughts on the role of personalization in digital experiences?

JB: We’ve seen from a lot of the literature the positive influence that personalization and adaptivity can have on a digital learning experience. In fact, it can be nearly as effective as human tutoring, which is the gold standard for learning. But often personalization is mistaken for completely independent learning experiences or isolated adaptive assessments. We need to think about this a little differently. Personalization – whether it’s through algorithms or humans using analytics – can enable just-in-time activities and interactions that are personalized to a learner or group of learners.

often personalization is mistaken for completely independent learning experiences or isolated adaptive assessments. We need to think about this a little differently.

KMW: “Personalized” learning for a group of learners is an interesting concept. What might that look like?

JB: For example, imagine that you’re teaching a course online – let’s say it’s a workforce development course in a healthcare field. Instructors could use personalization to auto-group students together based on a number of factors – their opinions, interests, behaviors, performance, or particular approaches to problem solving – to enable more effective discussions, debates, or collaborations. This type of personalization supports argument construction, self-explanation, perspective taking, as well as critical thinking, collaboration, and creativity. Offering complex, adaptive tasks that allow learners to interact and take ownership over their learning are valuable when learning is occurring in a completely remote environment and opportunities for in-person group interactions are not possible.

Actionable insights to support learning

KMW: One of the challenges I have been hearing from educators quite a bit during the pandemic is that they believe they were stripped of important insights into their class’ learning when they moved online. How can personalized learning help?

JB. This is a concern that I’ve heard, too. In traditional classrooms, educators can leverage informal conversations and non-verbal cues to gauge comprehension and misconceptions, but gathering this information can be difficult in a virtual environment. However, if a digital learning tool is built on the foundation of a learning model, then dynamic reports can be revealed to educators offering them valuable insights. Important information about outcomes like meaningful levels of engagement, learning progressions, persistent misconceptions, and proficiency can support decisions about which students to pro-actively reach out to and where to focus limited virtual instructional time. Becca Runyon did a lot of great work in this important area of insights for educators that serve as a helpful guide. 

if a digital learning tool is built on the foundation of a learning model, then dynamic reports can be revealed to educators offering them valuable insights.

KMW: If there is one thing you’d like to see change in this space, what would it be?

JB: Often, in both ed tech and in the classroom, there is a failure to connect personalization and adaptivity with learning theory. In fact, we see it all the time – folks who think that data scientists should operate in isolation. When this happens, it’s unfortunate because then we are just looking at numbers, not how those numbers might be influencing learning – and, our understanding of learning. If we are to evolve our understanding of how humans learn, we must root everything in learning theory. And that’s why learning models are so important.

KMW: This is all really very interesting Jeff, thank you! It’s clear that the application of learning science foundations, such as frameworks and models, to digital solutions - particularly adaptivity, personalization, and dynamic reporting can really support educators and learners in these increasingly complex educational contexts. Thank you so much for taking the time to share these thoughts and resources with us.

JB: Happy to, Kara – I think this is an important blog series and have enjoyed hearing different perspectives from the experts. Now more than ever it’s important that we work together to solve some of these deep educational issues and I’m happy to be part of those conversations.

Wrapping up

Thanks again to Jeff Bergin, and we’d love to hear any thoughts you have on this topic, feel free to leave comments – and of course, we hope you and your loved ones are all doing well during this difficult time.

Come back to the blog next week when we'll chat with Alex Britez, Director of User Experience at Macmillan Learning, and his thoughts on how the pandemic has shaped UX and what designers should be thinking about now.