Adaptive Learning: How to Integrate It With Digital Learning

As a teacher, you know that no two students learn the same way. A one-size-fits-all approach simply doesn’t work. The problem? Periodic tests don’t give the real-time insights you need to help struggling students before they fall behind. 

Let’s be honest: between grading, lesson planning, and admin work, there’s barely any time to create personalized lessons for students. That’s where adaptive learning comes in. 

Adaptive learning uses digital learning platforms to help you close learning gaps with customized lessons. Here’s how it makes education truly student-centered!

This idea isn’t entirely new. Adaptive learning dates back to the 1950s, when psychologist B.F. Skinner created a teaching machine that focused on incremental skill building. 

The machine adjusted questions based on the student’s previous responses. In case of incorrect answers, students received helpful hints instead of new questions. This approach gave them instant feedback and let the students learn at their own speed. 

Adaptive learning started to gain popularity during the AI movement in the 1970s. Ultimately, the idea was to make learning effective by having technology respond to the learner's needs.

How does adaptive learning technology work? 

Adaptive learning software dynamically adjusts lessons or practice activities based on how well a student understands a concept or skill. It analyzes multiple data points, such as how they work through the course material, how often they log in, and how much time they spend on tasks. 

Then, the technology uses AI and machine learning to compare data from other students’ learning paths and performance. This thorough analysis helps adaptive learning software customize practice activities for each student.

As an example, picture a course with ten concepts and a quiz at the end before moving on to the next course. The instructor knows students typically score around 70% on the quiz, but there’s no clear answer for why they miss the other 30%. 

It could be a lack of understanding of certain concepts, disengagement, or outside factors like work or family responsibilities. This is where adaptive learning software comes in – it provides data to help the instructor pinpoint critical issues and revise lessons before moving on to the next course.

Source: RapL

Adaptive learning technology relies on two primary approaches:

Designed adaptivity 

Designed adaptivity is a computer-based, expert-led teaching method in which an educator designs the teaching sequence to guide learners toward content mastery. 

In this approach, teachers use different adaptivity factors to shape how the model adjusts feedback and lesson content. Through an "if this, then that" approach, the technology offers help to struggling students, shares advanced content with high achievers, and prompts fast-paced learners to slow down and focus. 

The result? Better control over learners' experiences.

Algorithmic adaptivity 

Algorithmic adaptivity changes depending on user needs and behaviors. The algorithm first understands what learners already know and what they need to experience next. Based on its findings, the algorithm chooses the right task at the right time for each student.

Examples include the Bayesian knowledge tracking (BKT) algorithm, which evaluates the rate at which learning occurs. Item response theory (IRT) is another framework that assesses a learner’s interaction with discrete items. 

Types of adaptive learning methods  

Adaptive learning customizations can be of three types: adaptive content, adaptive sequence, and adaptive assessment. These approaches work together to create more personalized learning experiences for students.

1. Adaptive content 

Adaptive content adjusts the learning material based on students' skill levels, learning styles, and course progress. This provides hints or feedback based on how students respond to questions. 

The system may give the students more straightforward examples of visual aids to reinforce understanding. However, it doesn’t completely change the curriculum sequence. 

2. Adaptive sequence 

Adaptive sequence collects and analyzes each student’s progress data to adapt their learning pathway. For example, if a student is struggling, the system may add additional practice and hold off on introducing new topics until they’ve mastered the current one. Conversely, excelling students can quickly go through easier topics to jump into more challenging content.

3. Adaptive assessment 

Lastly, adaptive assessment adjusts the difficulty of questions based on a student's response. For example, if a student answers correctly, the questions get more challenging. If they struggle, the questions become easier to match their skill level.

Let’s look at different adaptive learning variants that use these features.

Types of adaptive learning systems 

There are three types of adaptive learning systems, each defined by its unique mechanism for customization and feedback. These include:

1. Advanced algorithm (AA) adaptive systems 

AA adaptive systems help instructors scale one-on-one computer-to-student interaction, especially in math and science courses. These adaptive learning systems prescribe content modules based on each learner’s profile and their previous proven and applied knowledge. 

They also provide real-time learning path feedback by comparing a student’s performance to others who’ve taken the same course. To do this, AA adaptive systems analyze data like clickstreams, assessment attempts, time intervals, and other behaviors. This analysis offers valuable insights into how well instructional methods and content work.

2. Rules-based (RB) adaptive systems 

RB adaptive systems use a set of pre-determined rules to provide students with ongoing feedback and remediation. They rely on rules to guide students through a set learning path with fixed assignment sequences. 

Students can move forward or backward based on their performance in diagnostic assessments. The system can adjust rules for individual students and provide feedback after each learning unit. 

RB systems require manual changes to content sequence, meaning they don’t automatically improve the instructional method as students progress. 

3. Decision tree (DT) adaptive systems 

DT adaptive systems use a tree-like structure of prescribed content, assessment, and answer repositories to guide students through learning modules. This adaptive system uses static “if this, then that” branching sequences to offer limited assessment items in binary forms. It can also customize learner workflows based on data intervals and feedback. 

Examples of adaptive learning 

Let’s explore some examples of how different educational institutions adopt adaptive learning.

Learning experience platforms 

Platforms like Coursera, Udemy, and LinkedIn Learning rely on adaptive learning algorithms to analyze student behavior and customize learner experience. These learning experience platforms also recommend other courses based on students’ preferences and engagement. 

The University of Texas (UT) 

UT adopted a personalized, competency-based education program to help fill employment gaps in STEM and medical science professions. 

The UT Total Educational Experience (TEx) uses adaptive technology to deliver mobile-friendly courses that personalize learning based on student performance. TEx courses are interactive, adapting to each student’s learning style with features like simulations, team projects, and clinical experiences.

Arizona State University (ASU)

ASU used adaptive learning to launch the Knewton platform for its online and hybrid courses in 2011. 5,000 freshmen used the Knewton platform for remedial math, with a 75% pass rate compared to 66% the previous year. 

In 2017, ASU introduced a flipped classroom model, where students watch recorded lectures as homework and use class time for problem-solving with help from the instructor. 

Since adaptive technology can handle mundane tasks, teachers can focus more on working with small groups or one-on-one to help students tackle tricky concepts.

How to create adaptive learning scenarios 

Here are some of the steps instructors can take to develop content, sequence, and assessment:

• Break down knowledge into smaller units. Here, the instructor creates objective-based knowledge units or short lessons based on the course’s learning objectives. These lessons form the foundation of the adaptive learning experience and shape the sequence of activities and assessments.
• Create assessments and feedback. Next, the instructor must create assessments to provide a personalized learning experience. Instructors must also consider creating structured feedback when designing assessments so students can learn from correct and incorrect answers. 
• Design an adaptive learning path. The last step is to design a learning track that students can use to follow through the course material. Instructors can start with foundational knowledge and progress to more complex content based on pre-assessment results. They can also use AI to personalize recommendations based on students’ assessment performance. 

Where can you implement adaptive learning? 

Educators in different sectors implement adaptive learning to customize students’ learning experiences and cater to their needs. 

• K-12 education: Teachers use adaptive learning methods to accommodate diverse learning styles and deliver personalized lessons in subjects like math, science, and reading.
• Higher education: College and university facilities rely on adaptive learning methods to customize coursework, create individualized learning paths, and improve student engagement.
• Corporate training: Corporate trainers also use adaptive systems to personalize training modules to employees' specific roles, skills, and learning speeds.
• Learning management system (LMS): LMSs feature adaptive learning capabilities that help learners improve their argumentative writing performance. 
• Distance learning: Distance education systems use cognitive scaffolding (an automated system creating assessment paths based on cognitive abilities) to consider students’ answers and adapt themselves to students’ cognitive skills. 

Benefits of adaptive learning 

Adaptive learning systems offer several advantages for both learners and educators:

Personalizes learning experience 

Adaptive learning systems tailor lessons and assignments based on students’ learning patterns, strengths, and weaknesses. This custom learning path allows students to spend more time on difficult topics and skip concepts they already mastered. 

Offers insights about learners' performance 

Adaptive learning systems assess learners’ performance by analyzing their responses to questions, time spent on tasks, and overall engagement patterns. These insights help instructors identify which concepts learners grasp easily and which they find difficult. 

Teachers can use these systems to compare a student’s performance with peer average and understand how well learners are doing.

Enhances learning outcomes

Adaptive learning systems tailor educational experiences to each student's unique needs. They help students identify strengths and gaps in knowledge. With real-time feedback and personalized pacing, students can correct mistakes quickly and progress at their own speed. 

Adaptive learning tools also engage learners by creating different content formats and learning paths based on performance data. 

Best practices for adaptive learning 

Instructors can make adaptive learning effective by following these best practices:

• Conduct learner analysis. Consider using diagnostic assessments to determine each learner’s needs, preferences, and prior knowledge and to identify learning gaps and strengths. 
• Pick the right platform. Instructors should choose a platform that allows them to select learning activities and assessments that align with the course objectives.
• Integrate continuous assessment. Continue providing immediate, actionable feedback to learners and adjusting learning paths based on formative assessments.
• Foster collaborative learning: Consider encouraging learners to take part in group activities for knowledge sharing and topic discussion.

How digital learning ties with adaptive learning 

Adaptive learning is a subset of digital learning that focuses on personalization. It uses data and algorithms to tailor learning experiences to individual needs. Digital learning, on the other hand, is an umbrella term for all approaches that use technology to deliver content and improve educational experiences. 

Here’s how digital learning ties in with adaptive learning.

• Content personalization: Digital learning platforms offer a wide range of educational materials. Adaptive learning systems personalize the delivery of this content. They use data like a learner’s pace, knowledge gaps, and engagement to adjust the type, sequence, and difficulty of materials for each student.
• Dynamic learning paths: Digital learning content is usually linear or static. Adaptive learning changes this by creating flexible pathways that adjust as learners progress. For instance, a student struggling with algebra may receive extra foundational exercises. Meanwhile, a proficient learner can move straight to advanced topics.
• Blended learning models: Adaptive learning integrates easily into digital learning setups, especially in blended learning models that combine in-person and online instruction. Teachers can use adaptive insights to address challenges during class. Meanwhile, online tools can personalize learning for each student.

Say hello to learning that teaches you

Class sizes are growing, and it’s becoming harder to provide every student the attention they deserve. Through adaptive learning, you can create personalized, engaging experiences for all students, no matter their level. Adaptive learning makes it easier to create an inclusive classroom and improve learning outcomes for every student.

Looking for platforms that get students actively involved with their learning? Try the best digital learning platforms in 2025. 

Edited by Monishka Agrawal