What is Adaptive Learning?

There’s so much buzz about adaptive learning in the eduction industry. It’s definitely one of the hottest trends out there today.

Is adaptive learning vs. personalized learning

I need to preface this post by saying that while adaptive learning is an approach to creating a personalized learning experience for students, it is only a piece of the overall personalized learning puzzle.

Personalized learning is more of an umbrella term that describes a range of approaches and models such as competency-based learning, guided pathways, adaptive learning, etc. It's also an area that will be heavily impacted by the Internet of Things.

The adaptive learning piece places the student at the center of the learning experience by making content dynamic and interactive.

So what is it?

Basically, adaptive learning is building learning experiences that adapt to each student’s individual learning needs versus creating one course for everyone.

With adaptive learning, there’s no cookie cutter, one size-fits all.

Given it's very nature, adaptive approaches generally require specialized tools for developing courses, tools that allow learning experience designers to collect data so they can adapt the courses to each student; then build a course that can provide different pathways based on each student’s learning style.

Essentially, an adaptive course takes a non-linear approach, adjusting to how a student interacts with a course.

Based on the student’s performance, an adaptive course will anticipate what types of content and resources that students need at a specific point in time in order for them to progress from one part of a course to another. 

Approaches to adaptive learning

There are generally two approaches to adaptive learning—facilitator driven and assessment driven.

Facilitator-driven

This is then when professors and instructors receive detailed student performance profiles that contain a significant amount data about the student.

This data is usually presented in the form of dashboards which allow facilitators to create different instructional experiences for each student.

Facilitator-driven solutions rely heavily on data from the course content. This data tells them how the student is interacting with the course.

For example, did a student watch the course lectures, if so, did they watch the entirety of each lecture only part of them? Did the student participate in the online class discussion boards, if so how often? How well did the student do on their exams? You get the picture.

This data is then pushed to the dashboard so the facilitator can get a clear view into student engagement and interaction with their course.

This content-driven approach generally links to specific pieces of content in a course inventory—lectures, learning activities, discussions, ebooks, etc.

Instructors can use specialized learning tools to adapt the course content at scale (i.e., number of students) or present different types of content to help struggling students. 


Assessment-driven

This is where student performance and/or mastery of a topic is continuously evaluated. This approach generally results in dynamic, almost real-time adjustments in the course content, learning resources, and course pathways.

Basically, individual students can progress through a course based on a common set of learning objectives, but in different ways and at their own pace. 

It’s important that assessment-driven solutions tie all of the course content—assets, items, and learning objects to standards and learning outcomes.

This is similar to the facilitator-driven approach; the key difference is that students can progress through an assessment-driven solution individually or a part of a group with little or no instructor interaction. 

This approach requires the tool to monitor, track, and analyze extensive amounts of data from the student’s overall learning experience. 

More sophisticated Assessment-driven tool can use the data they collect to categorize students and create a robust profile.

It’s important to note that these two approaches are not mutually exclusive.
Most tools on the market allow for either approach, of course with different emphases and levels of sophistication.

And there you have it. A high-level overview of adaptive learning.

Do you have anything you would like to add? Let’s continue the conversation in the discussion below.