Personalizing Learning with the Whole Child in Mind
A Student-Centered Approach to Data and Technology
We know that education technology is evolving quickly — classroom tools deliver more precise individualization than ever before and capture valuable student data at an unprecedented scale. But a truly personalized approach to instruction takes more than data. It requires us to understand the context in which learning happens, celebrate the differences in every learner, and consider the social, emotional, and cultural factors that influence every student’s experience.
We recently partnered with EdSurge to host a panel of practitioners and researchers who explored the future of personalized learning and data-driven teaching in the context of every student’s lived experience.
The webinar panel included Dylan Arena, VP of Learning Science at McGraw Hill, Agam Altyyev, Director of Mathematics at LISA Academy, Julie Neisler, Quantitative Researcher and Data Privacy Officer at Digital Promise, and Vic Vuchic, Chief Strategy Officer at Digital Promise.
You can watch the recording in full here, or read on for highlights of the conversation.
What’s different about THIS conversation on personalized learning?
Vic: When personalized learning was really at the height of its status as a buzzword, I would often ask, “What exactly are we personalizing, and with what research?” Often, the room would go quiet — they were just personalizing whatever they could. But there’s this huge body of learning science research that helps us understand how learners vary. At the Learner Variability Project, we’ve built models that synthesize that information. There have been other efforts as well to understand how we synthesize and make this research actionable, learner-centered, and with a lens towards equity.
The second thing that has changed over the last five years, particularly over the pandemic, is much more of a focus on the whole child. It’s the understanding that it’s not just where you are on a math pathway. It’s about how you feel about math, if you have math anxiety, and if you see yourself as a mathematician.
Julie: The data we’re collecting is vastly different than what we’ve been able to collect before. Vic mentioned that he often asks, “What are we personalizing?” I often ask, “Where are the students?” If this is student-centered learning, where are the students that we’re centering and how are we including them in that research? Research has expanded to really honor students as the experts of their experience, and it’s important to bring them into product development to get their feedback.
How does personalized learning address the current retention and burnout crisis in education as a profession?
Agam: I work in the field and spend a half-day in classrooms working with teachers and coaches. It can be hard for teachers to visualize the differentiation piece, which can translate into a lot of work. On the other hand, personalized learning can almost function as a teacher’s aide. I see teachers working closely with two or three students who really need attention, and personalized learning takes care of differentiation for the rest of the classroom. Instead of handling twenty-five students, teachers are only dealing with two or three students academically — and socially, too. Having 1:1 interactions with students helps them grow.
Julie: Thinking of equity, students need to have access to resources, and need to be able to use the resources to get them where they need to be to be successful. Personalized learning, then, is each individual student filling that gap — getting the just-in-time remediation that they need, the culturally responsive material that they need, something that helps alleviate their math anxiety or presents information in a multimodal way. Being able to close these individual student equity gaps allows us to see those big-picture changes over time. As we continue to build this culture of using data to make decisions, particularly when it comes to personalizing learning paths for students, I think we’re going to leapfrog some of these current crises.
Is there an opportunity for technology to support social and emotional learning?
Dylan: Absolutely. If the only signal that we’re getting about students is proportion correct on assignments, then all our choices about how to differentiate or personalize must be based on those narrow academic measures. But we’re seeing more opportunities to capture and better understand where students are with respect to a more holistic picture. In the Learner Variability Project’s framework, you have the academic information, but you also have domain-general cognitive information, social and emotional information, and student background information. If you can get a richer sense of a student’s experience — not just that they’re dealing with two-digit multiplication but that they tend to be anxious, they have auditory processing troubles, and they’ve been getting bad sleep recently — you know a lot more as an instructor about where that student is and why, and what the right move is to support them.
There are increasingly many digital interactions for students that will help them express where they are in many different dimensions. Responsible learning science and data science can make use of those signals to help form a more complete picture of the learner, and use that picture, along with research-validated strategies, to suggest the right next step in supporting that learner.
How do we translate student learning data into actionable insights to personalize learning?
Agam: We ask teachers to drive with data, but in most cases, when we say “data-driven,” we’re asking teachers to collect their own data. Most of the time, raw data is what’s been thrown out to them — without colors, shapes, or dynamic elements, and they’re supposed to come up with something from it and drive it. For example, I’m the data guru in my district. In parent-teacher conferences, I don’t want my teachers hunting for information across different software to share grades, benchmark assessments, and discipline problems. To solve that, I would create dynamic reports for teachers with all this information to foster great conversation between teachers and parents. Unfortunately, there aren’t many coordinators with this sort of role for teachers — but now I’m thinking, especially through this conversation, that schools need a data scientist! Ultimately, we should be able to let the teachers drive the data, but let the computers generate the data for them.
Dylan: This is the main goal of what we’re working on with McGraw Hill Plus — and we hope that the technology will make it so that you don’t have to have a data scientist in every school (although if you did, it would be fantastic). We want to bring together data from various programs into one place that tells you not only where a student is, but what a student needs next. We have a design goal of always providing actionability: We want to ease the teacher’s workflow and coordinate the classroom rather than only allowing for tasks that require another forty-hour workweek, like Agam’s work with collecting and synthesizing files.
Vic: One thing that often gets missed is the mental model. What’s the mental model that a teacher or a school has in terms of how they want to understand a student and how they want to support them? If that’s just literacy scores, that’s a really narrow mental model. This is where the whole learner model opens up that space — and it also goes for designing products. Many products are “adaptive,” but they’re very linear. As we work with products, we had one math product come to us and say: We’re a math product, why are you asking us to think about social and emotional learning? Of course, learning math is a social and emotional process, and we should think about it in an integrated fashion.
What can educators and educational leaders do to move closer to this vision?
Vic: First is the mental model of the whole learner and finding ways to step into this. How do we respect the complexity of learning, but represent it with actionability? Continue to respect and safely understand your learners in your effort to find ways to support them.
Agam: Digital platforms that personalize learning will never take the teacher’s role and relying on them too closely is never ideal. Additionally, let the teacher sit in the student’s role when trying out new technology. They’ll realize and appreciate the effort students put into learning, and they won’t resist personalized learning.
Dylan: Whenever school leaders are talking to service and curriculum providers, they should demand and expect those vendors to be able to tell them how their offerings will empower teachers rather than marginalize them, ease their day-to-day workflow, and coordinate the classroom.
Julie: Be intentional in your decisions — don’t go for the new shiny app! On the flip side of Agam’s advice, have the students sit in the role of the teacher. Have students try out the technology you’re thinking of adopting to see what meets their needs. Finally, center equity. Think about the assets that your students and instructors have and do your best to support them.