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[Series: Adaptive Learning] Understanding Adaptive Learning System Models

By McGraw-Hill Education 2 years agoNo Comments
Home  /  Series: Adaptive Learning  /  [Series: Adaptive Learning] Understanding Adaptive Learning System Models

As we delve further into the workings of adaptive learning technology, we want to take a closer look at the models commonly used within platforms (not exactly sure what adaptive learning is? Check out this blog to learn more). After all, the more we understand how the technology can differ, the better we can make more informed decisions when considering utilizing adaptive technology in the classroom.


Inference Models

Think of inference models as having a predetermined map of pathways students can follow when using adaptive learning technology. Although the paths may be predetermined, they are not limited – when content reaches upward of 500 or more topics, you can imagine how complex the map of pathways for students to follow can be.

As time moves on during a course and students complete more work and answer more questions using the adaptive learning technology, they continue to grow their personal map of pathways. Along the way, depending on how they answer, students can move both forward and backward in their progression along the path.

Within inference models, you can find three different models they are built upon: content, learner and instructional.

Content Model

This model houses the computer’s understanding of what needs to be learned, for example, the main topics that are covered in a course. The system understands how the topics are linked and is thus able to help scaffold or support a student who is learning each topic. The system also understands each possible progression, assisting in the creation of the complex map of topics and pathways.

Learner Model

This model is the computer’s understanding of what the user knows about the subject matter being learned. When a student begins a new course, they will complete a task so the system can assess their grasp of basic concepts to get a clear picture of their level of proficiency. As the student continues through the course, the system updates the model of student understanding.

Instructional Model

This model decides how to bring the previous two models together, determining which content to show next based on the student’s current level of understanding. As not all students learn the same, they will not start a course in the same place either. Some students will already have a firm grasp on basic concepts and can move on to more advanced material, while others will need to master the basics before progressing.


Biological Models

There are a fair few similarities between inference and biological models in terms of assessing student understanding and assigning content based on what they know. However, biological models are more flexible. Instead of having a predetermined map of pathways for students to navigate, the design of biological models allows for quick adaptation to a large expanse of possibilities that may occur in a student’s learning path.

However, creating a unique map of any topic for any student is not necessary. The semantic organization along with the process of making mistakes are combined to help the material adapt, making a one-of-a-kind learning experience that is constantly changing. This is accomplished by having detailed and semantically structured objectives for users, but more flexible connections between the objectives. This way, different students can arrive at the same learning goal but use different paths.

As a student’s understanding changes, biological models are able to adapt to their needs on a daily basis. In addition to adjusting to a student’s understanding, these models also take confidence levels, partially correct answers, response time and how long it took to read a response into consideration when presenting content. This makes biological models more flexible when determining the next step a student should take and when they are ready to move on to new material.


Instructor Takeaways

When considering integrating a new technology into your classroom, it’s important to understand how it works. With the knowledge that not all adaptive systems function the same way, you can make more informed decisions when choosing a product to use. Better understanding what adaptive learning is and how the technology works will help you make sure you’re choosing the product that will work best for your students.

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  Series: Adaptive Learning
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