Personalized Learning Rhetoric: Do We Truly Understand It?

Personalized learning has become a well-worn buzzword in education circles. The term can be found in almost every piece of educational writing today. But do we actually understand what it means or do we just like the way it sounds?

Confession: I use the term as readily as everyone else without having a true grasp of what we mean by it or its full implication for educating students today. I’ve read many definitions and, like many leaders, continually struggle with what personalized learning looks like in schools.

Katrina Stevens, deputy director in the Office of Educational Technology at the U.S. Department of Education, says it’s easy to get confused by the discussion around personalized learning. She offered specific terms that often muddy the waters in our understanding of what the phrase actually means:

• Adaptive learning: technology used to assign human or digital resources to learners based on their unique needs
• Individualized learning: the pace of learning is adjusted to meet the needs of individual students
• Differentiated learning: the approach to learning is adjusted to meet the needs of individual students
• Competency-based learning: learners advance through a learning pathway based on their ability to demonstrate competency, including the application and creation of knowledge along with skills and dispositions

Long story short: Personalized learning is a more complex subject than we might think. It’s no wonder then that even school leaders can get confused when trying to implement personalized learning strategies.

I believe leaders must first ask themselves two questions about their understanding of the value of personalized learning—where the answers lead to two different outcomes.

Are we trying to understand how personalized learning models can improve the current system where all students are learning the same material at the same time, in the same way and are assessed using summative tests?

Obviously, my interest is in how we answer the second question and not the first. However, I could not begin to answer this question myself. That’s why I reached out to a friend for whom I have great respect and who has a unique perspective on personalized learning from his work in the testing world and in developing adaptive technology tools.

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David Kuntz is principal advisor to the CEO for adaptive learning at ACT, Inc., and is responsible for the strategic vision and direction of ACT’s adaptive learning initiatives.  ACT is a mission-driven, nonprofit organization dedicated to helping people achieve education and workplace success. Before joining ACT, David was chief research officer at Knewton, where he led the development of the world’s first large-scale, cloud-based adaptive learning platform for personalizing education. Needless to say, David knows a thing or two about adaptive and personalized learning.

I asked David for his thoughts on two questions.

1. From your work in developing adaptive technologies, how would you describe personalized learning?

There is a lot of confusion at present about the difference between personalized learning and adaptive learning. The short answer is that personalized learning is about making your education more relevant to you, whereas adaptive learning is about using data to modify and adjust your specific educational experience to make it more efficient and more effective.

A longer answer might best be served by an analogy.  Imagine you are in charge of a one-room schoolhouse.  You have a first-grade student, a fourth-grade student, and a 10th-grade student. You also have a first-grade textbook, a fourth-grade textbook, and a 10th-grade textbook. Giving the right book to the right student is, in some sense, personalized education—each student is taught material more appropriate for them than the alternatives. But it’s not adaptive. The fourth-grade textbook, for example, doesn’t change as the student learns and grows. It provides the same material, in the same sequence, for every student who receives it. On the other hand, instruction and assessment that is driven in real time by the data a student generates during the learning process is both adaptive and personalized.

2. How do you see adaptive learning models changing the architecture of schools in the next five to ten years?

Providing good advice and guidance to students depends critically on having good information about the strengths, weaknesses, needs, and goals of the student, and an understanding of how that information can best be leveraged toward the attainment of those goals by that student. The convergence of the present transformation of paper-based educational materials to digital, the easy availability of unprecedented computational power and data storage capacity, and the relative ubiquity of internet-connected mobile devices now makes it possible to develop and provide educational applications that, when used by students, enable the opportunity for unprecedented insight and guidance.

Adaptive learning, as a data- and measurement-driven approach to differentiated instruction and guidance, leverages machine-learning technologies to model important characteristics of students and educational content, from the data students generate when interacting with that content. From those models, such systems can draw inferences that help guide students, and teachers, toward improved learning outcomes. In short, this means leveraging, in real time, what we know about the student across multiple dimensions, and what we know about that student’s objectives, to identify what the student or teacher can do to help the student reach those objectives.

“In real time” is particularly important. It means that at any given moment, there’s an up-to-date picture of the student that is simultaneously summative and formative—summative in that it presents a robust snapshot of the student based on all of their work up to that point, and formative because it contains pointers and guidance to move each student closer to mastery of their specific learning goals. This is a game-changer.

The upshot is that instead of an educational system set up as a filter, to stratify students, we can have an educational system designed to promote mastery, not just of traditional cognitive academic materials, but also of the cross-cutting and behavioral skills that are so important in today’s economy. We can have an educational system that recognizes and responds to the many and various backgrounds, goals, interests, and the very different ways we learn to help guide students through the transition into a career and throughout their careers because they learned what they need to know in order to do the things they want to do.

David introduces a very different conversation than most of us have when we talk about or design personalized learning environments. In addition, the framework from Katrina Stevens, along with David’s perspective on adaptive learning, should push us to a more in-depth conversation on personalized learning design beyond the rhetoric.

Ask yourself: Do my schools look different with the use of technology to design a personalized learning environment? Do I understand the different terms used in a personalized environment? Can an adaptive learning model reshape our school architecture to better meet the needs of all children? Let us know what you’re thinking in the comments.