Glossary

Adaptive Learning

What is Adaptive Learning? 

If there is one thing that corporate training and customer education have in common with one another, it’s the need for personalization. Modern learners, whatever their position, expect an educational experience tailored to their needs and preferences. Unfortunately, this is easier said than done, particularly at scale.

Enter adaptive learning. Instead of requiring participants to accept a one-size-fits-all approach to their training, adaptive learning allows instructors to quickly and easily deliver dynamic, customized training. Adaptive learning technology typically uses specialized artificial intelligence to adjust a course to each participant. 

Different adaptive learning tools have different approaches to this personalization, but the most common types of adaptive learning include: 

• Providing real-time hints or feedback to learners depending on how they engage with the material and respond to assessment questions. 
• Adjusting each participant’s learning path based on individual progress. For instance, if the AI detects that a student appears to be breezing through their assessments, it may fast-track them to higher-level content. 
• Dynamic assessments that ask different questions and change in difficulty based on a learner’s responses. 

How Does Adaptive Learning Work? 

An adaptive learning program consists of three components.

Learner Models evolve in tandem with the learner they’re designed to track. They’re essentially dynamic blueprints that provide the necessary context for personalization. They offer a range of insights about their learner, including progress, overall performance, potential strengths and weaknesses, and pace. 

Domain Models are semantic frameworks that help adaptive learning models “understand” the subject matter. They define the logical relationships between each core concept represented in the training material and also identify the progression paths required to master these concepts.

An Adaptive Engine ingests the domain and learner models and then uses machine learning and artificial intelligence to interpret, personalize, and deliver information from them. The engine also uses machine learning and artificial intelligence for the ongoing refinement of its models, improving both their accuracy and efficacy over time. 

The Benefits of Adaptive Learning

The benefits of adaptive learning in education include but are not limited to.

Better Outcomes

Everyone has a slightly different learning style and pace. Adaptive learning allows instructors to account for these differences by delivering personalized content to each student. In addition to potentially improving knowledge retention and comprehension, adaptive learning also improves learner engagement through specially tailored learning paths.

Personalization at Scale

Rather than requiring manual customization from the instructor, adaptive learning content is delivered automatically. Instead of having to spend countless hours devising and designing educational content, instructors can focus on engaging with students and proactively reaching out to anyone who seems to be having trouble. 

Efficient Education

Adaptive learning streamlines an instructor’s workflow and helps students spend their time more efficiently. Students with expertise in a certain topic may automatically skip that topic, allowing them to focus on learning new concepts and ideas. Students who are struggling, meanwhile, may be flagged for the instructor’s intervention.

Iterative Learning

Combined with learner analytics, adaptive learning can provide a student with immediate post-assessment feedback while also providing instructors with a real-time overview of everything that’s happening within that student’s learning environment. This can be used to direct learners with regard to where they should focus their efforts. Rather than finding out they failed an assessment weeks after the fact, a learner can be directed down a learning path that helps them learn more about why they failed. 

For instructors, an adaptive learning platform can also serve as an effective tool for collecting learner feedback about a course. In both cases, adaptive learning enables an iterative, strategic approach to training and education.