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Not everyone learns at the same pace. Some students grasp new concepts quickly, while others need more time or a different approach. Traditional education often struggles to accommodate these differences, which is where adaptive learning comes in.
Fortunately, educational technology (EdTech) has an ace up its sleeve: adaptive learning. Being both an EdTech tool and a pedagogical approach, it gives students the opportunity to approach lessons at their own pace.
EdTech's greatest impact often comes from serving those with the greatest needs.
What is adaptive learning?
Adaptive learning is an approach that modifies educational content and pacing based on individual needs. It uses algorithms to monitor student performance, adjusting content dynamically based on individual progress.
Adaptive learning can be applied across various learning environments, from online courses to in-person classrooms with adaptive software. The goal is to provide the right level of challenge and support for each learner, preventing both frustration and boredom.
As highlighted in a recent article by Dr. Contrino and colleagues in SpringerOpen.
It is becoming increasingly clear that not all students require the same education, and incorporating adaptive learning (AL) has increased in demand in recent years. Our results suggest that AL is a solid strategy for teaching courses.
The market also reflects the increased demand for adaptive learning solutions. For example, in its report, Grand View Research stated that the global adaptive learning market size was valued at $346 million USD in 2022. With a compound annual growth rate (CAGR) of 21.4%, it is expected to grow to $16.300 million USD in 2030.
With the rising interest in adaptive learning, some might confuse it with a similar approach — personalised learning.
Adaptive learning vs personalised learning
At first glance, adaptive and personalised learning may seem similar since both approaches tailor education to the individual. However, they achieve this goal in quite different ways.
Adaptive learning is primarily technology-driven. It relies on algorithms to track a student’s performance and adjust content in real time. The system responds dynamically, identifying strengths and weaknesses and adapting lessons accordingly. This approach eliminates the need for direct human intervention in making these adjustments.
Personalised learning, on the other hand, is a broader concept. While it may incorporate adaptive learning, it is not solely dependent on technology. Instead, personalised learning often involves human decision-making from teachers, trainers, or course designers to craft learning experiences that fit an individual’s interests, goals, and preferred learning styles.
To sum up, adaptive learning is like a GPS that recalculates your route, constantly adjusting your path, and personalised learning is like planning your entire journey in advance.
While adaptive learning focuses on moment-to-moment adjustments, personalised learning takes a more holistic approach, considering a student’s unique needs and goals over time.
See also: A new way to learn: how educational technology help students
How adaptive learning works
Adaptive learning doesn’t follow a fixed curriculum. Instead, it adjusts as the learner progresses. Here’s how it’s usually done.
Step 1: Assessment
Adaptive learning begins with an initial assessment to gauge a student’s knowledge, skills, and gaps. This could be a short quiz or an analysis of previous performance. The system uses this data to determine the best starting point.
Step 2: Adjustment
As the students progress, the system continuously analyses their responses. If they struggle with a concept, the program may provide additional explanations, examples, or easier problems. If students excel, they are moved to more challenging material.
Step 3: Feedback
Immediate feedback helps students understand mistakes and correct them before they become habits. Instead of waiting for a teacher’s response, the system provides hints, explanations, or alternative problem-solving approaches.
Step 4: Adaptation
The system tracks performance over time, identifying patterns and adjusting accordingly. If a student repeatedly struggles with a specific type of challenge, the system may revisit the concept later in a different format.
Step 5: Improvement
Teachers and course designers can use adaptive learning data to refine teaching strategies. They can improve content and make better instructional decisions by identifying common challenges and success patterns.
See also: How to implement the flipped classroom method and reap its benefits
Implementing adaptive learning: best practices
Effective use of adaptive learning technique requires more than just technical skills. Proper results rely on content quality, clear goals in mind, and a balance between automation and oversight. Additionally, in-depth data analysis and working on student engagement play a role in the effectiveness of adaptive learning.
Clear learning goals
Adaptive learning works best when there’s a clear objective. Having clear goals for students to achieve will guide how to adjust content. Without clear goals, the system may struggle to provide meaningful recommendations.
Quality content
Technology can adapt lessons, but it won’t work as efficiently without quality content. Well-structured, engaging materials ensure students actually benefit from the adaptive approach rather than just being shuffled between different difficulty levels.
Balance between automation and human oversight
While adaptive learning relies on algorithms, human involvement is still needed. Teachers should monitor progress, provide guidance, and intervene when technology alone is insufficient to address learning challenges.
Keep students engaged
Adaptive learning should feel interactive, not robotic. To do that, use a mix of question types, multimedia, and real-world examples to keep learners interested and encourage deeper understanding.
Analyse data for continuous improvement
Review student data regularly to identify trends and areas for improvement. If students struggle with the same topic, this may indicate that the content or instructional approach needs to be adjusted.
Conclusion
Adaptive learning provides a flexible way to accommodate different learning speeds and styles. Using technology to analyse progress and adjust lessons in real time helps students stay on track without feeling overwhelmed or unchallenged.
At the same time, adaptive learning is not a “magic pill from everything”. Good results still rely on quality content, clear goals, and human support. Effective and mindful use of this technology makes learning more efficient and engaging.
How Altamira can help
If you’re looking to create your own adaptive platform or incorporate AI tools into an already existing one, our approach is guaranteed to provide a stress-free solution.
- Zero headaches: as a software partner with a proven track record of pushing technology to its limits, we provide results in days, not weeks
- Streamlined development: get double the value from your investment thanks to our quick development cycle
- Transparency first: we put emphasis on clear communication so you always know what’s happening with your project.
Get in touch and build your own adaptive learning platform.
FAQ
An online math platform that adjusts difficulty based on your answers is a good example. If the students solve a problem quickly, it moves them to more complex questions. If they are struggling, it provides hints or simpler problems. This real-time adjustment helps learners progress at their own pace.
Contact us to learn more about adaptive learning platforms.
Adaptive learning comes in a few forms:
- Rule-based systems – Adjust content based on preset conditions (e.g., getting three wrong answers triggers a review lesson).
- AI-powered platforms – Use machine learning to personalise lessons based on behaviour and performance.
- Competency-based learning – Moves students forward only when they demonstrate mastery rather than sticking to a fixed schedule.
- Data-driven feedback – Uses analytics to provide customised insights to both learners and instructors.
An adaptive classroom adjusts teaching methods, materials, and pacing based on students’ needs. This could mean using technology that tracks student progress or simply a teacher who modifies lessons in real time to help students grasp concepts better. It’s about flexibility—ensuring no one is left behind or held back.
Contact us to build your own adaptive solution.
Both adjust to a student’s needs, but there’s a key difference:
- Adaptive learning relies on technology to analyse performance and adjust content in real-time.
- Personalised learning is broader—it can include teacher-driven strategies, student choice, and custom learning plans, not just automated adjustments.
Think of adaptive learning as a smart GPS that changes routes as you drive, while personalised learning is more like planning your own road trip based on preferences.
