What is adaptive learning? Definition explained
Data is the core of adaptive learning
Unlike traditional learning approaches, where all learners go through the same material at the same pace, adaptive learning allows for individual progress. You determine how fast or slow you move forward, and the system adapts to your needs!
Data is the core of adaptive learning. It tracks where you currently stand and delivers exactly the content you need — whether you are still working on certain challenges or want to further develop your strengths.
Artificial intelligence and smart tools make this even more efficient by continuously optimizing and adjusting the learning process.
How does adaptive learning actually work?
To better understand adaptive learning, let's take a brief look behind the scenes. What mechanisms are behind it, and how does technology ensure that your learning path constantly adapts to your individual needs?
1. Starting point: assessing learning level
The adaptive system starts with an assessment. Through initial tests or tasks, it determines how much the learners already know.
Example in the workplace:
In a project management employee training program, the system might check whether participants are already familiar with Scrum or Kanban.
Those who are familiar get more challenging tasks on topics like project optimization or team leadership.
Beginners, on the other hand, start with the basics of agile working to ensure they have a solid foundation before moving on to more complex topics.
Example in a school setting:
In school, a math program could first present a few basic tasks to see if a student has a firm grasp of fundamental knowledge or if there are gaps. Those who are proficient get more difficult tasks, while others work on the basics until they have mastered them.
2. Personalized content
Once the system has assessed the learning level, it gets to work; the content is tailored to the learners. Those who easily master the first tasks receive more demanding challenges. Those who need more practice get additional support at a simpler level.
Example in the workplace:
In a sales training program, the system might identify that some participants are already skilled in negotiation techniques. This group then receives more complex tasks, such as international negotiation scenarios or strategic negotiation approaches. Those who are still at the beginning practice the basics, like needs analysis or handling objections, before moving on to advanced topics.
Example in a school setting:
In school, a math program might proceed similarly. A student who masters basic arithmetic can attempt more challenging tasks, such as algebraic equations. Those who struggle with fractions get additional exercises and clear explanations to solidify the basics.
3. Real-time feedback
An essential element is immediate feedback. When a task is solved correctly, the system moves on to the next, more challenging level.
Example in the workplace:
Imagine you're participating in a project management training program. After successfully completing a task on resource planning, you immediately receive the next, slightly more difficult challenge. Made a mistake? No problem! The system gives you detailed hints on how to allocate resources better and offers additional exercises so you can perfect it next time.
Example in a school setting:
In a language learning program, feedback could be just as immediate. Did you conjugate a verb incorrectly? The program immediately shows you the correct form and prompts you to try the sentence again. This way, the error is corrected on the spot, and you reinforce the learning immediately.
4. Adapting the learning path
Throughout the learning process, the system continuously collects data on how well learners are progressing. It not only identifies strengths and weaknesses but adjusts accordingly.
Example in the workplace:
In leadership training, the system might discover that an employee is strong in decision-making but occasionally struggles with conflict management. In this case, the system offers targeted case studies or simulations to improve that specific skill. At the same time, the learning path for decision-making becomes more challenging with complex scenarios to further develop already strong abilities.
Example in a school setting:
Let’s say a student excels in algebra but needs more work in geometry. The adaptive system recognizes this and adjusts the learning path automatically: more advanced tasks in algebra, like quadratic equations, are provided. Meanwhile, geometry lessons include extra exercises and interactive lessons to ensure the fundamentals are mastered before moving on to the next topic.
5. Integration of different learning methods
Since not everyone learns in the same way, adaptive learning often combines different formats:
Example in the workplace:
In a sales training, the system could use various learning methods to ensure all participants fully understand the content. First, a video could demonstrate a successful sales technique. Then, an interactive role-playing game might allow participants to apply the technique in a simulated sales situation. To reinforce the learning, a quiz could follow, ensuring that the key points are internalized before moving on to more advanced topics.
Example in a school setting:
A history program might show students a short video about a historical event, followed by an interactive timeline and a quiz to reinforce what they’ve learned.
6. Ongoing optimization
Once a learner has fully mastered a topic area, it is marked as 'completed'. The system no longer wastes time on it and instead focuses on new, unfamiliar topics that still need to be mastered.
7. Motivational aids
Many adaptive learning systems use gamification elements to keep learners motivated
Many adaptive learning systems use gamification elements to keep learners motivated. For instance, learners might earn points, badges, or see progress bars to make their achievements visible. This motivates them and provides additional rewards during the learning process.
Example in the workplace:
In a sales training program, employees might earn points or badges, such as 'Negotiation pro' or 'Communication champion,' after completing certain modules. These badges not only show progress but also motivate employees to tackle the next challenge and celebrate more successes.
Example in the school setting:
In a learning app, students might collect points by successfully solving math problems, which place them in a ranking. With each level they reach, they can see their progress in a bar and earn virtual badges — from 'Fraction master' to 'Equation king'.
What are the pros and cons of adaptive learning?
Adaptive learning offers many advantages, but there are also some downsides that should not be overlooked.
Advantages of adaptive learning
Individualized learning: everyone receives content tailored to their current needs, making the learning process more efficient and focused.
Flexibility: learners can progress at their own pace, which is especially beneficial for different learning styles.
Motivation boost: personalized learning paths and immediate feedback keep motivation high. Success is felt more quickly.
Immediate feedback: learning progress and mistakes are recognized instantly, and the system adjusts in real time, making the learning process smoother.
Time saving: Only the relevant content is taught, so no time is wasted on already familiar material.
Disadvantages of adaptive learning
Dependence on technology: adaptive learning requires modern technologies, which are not always accessible or easy-to-use.
Less human interaction: the digital focus can reduce personal exchange with teachers and peers.
High cost: setting up and maintaining adaptive learning systems can be expensive and time consuming.
Privacy risks: the collection and analysis of personal data may raise concerns about privacy protection.
How can you incorporate adaptive learning into your company?
Implementing adaptive learning in your company can be efficient by following a few important steps:
Identify target groups and needs: Conduct an analysis to determine which areas your employees need targeted training in. This allows you to develop learning content tailored to specific skills and knowledge.
Choose the right learning platform: Choose an adaptive learning platform that is easy-to-use and integrates well with your existing infrastructure. Make sure it supports data-driven learning to enable personalized learning paths.
Create personalized learning paths: Use the technology to dynamically adjust learning content based on employees' progress. Depending on their knowledge level, simpler or more complex topics are offered, making the learning process more efficient.
Use and optimize data: Continuously monitor your employees' progress and adjust learning content. Analysis tools help identify weaknesses and address them specifically.
Provide support: Offer your employees training or support to help them make the most of the learning platform.
How an LMS can support adaptive learning
A learning management system (LMS) is a software platform that enables companies, consultants, coaches, and educational institutions to create, manage, and distribute learning content. Our LMS, Easy LMS, provides a user-friendly and flexible solution to make learning processes simple and effective.
Key features include:
Useful resources
Leaders
Journal of International Business Studies
Smart learning environments
An analysis of the research on adaptive Learning
Neovation
