Adaptive learning is like a navigation app for cars—it gets you to your destination in the most efficient way. To avoid traffic or accidents, a navigation app may reroute you several times. Adaptive learning adjusts your (learning) route based on where you’re at in your learning journey.
With adaptive learning, your learning trip ends up following unexpected directions. Read more and learn how amazing it is to go in those directions and why they benefit your journey.
What Is Adaptive Learning?
Adaptive learning is a technology-based, interactive training method. It provides individual learning programs tailored to the learner.
By gathering data throughout the training process, adaptive learning technology optimizes training content. This means that each individual gets the content that matches their learning results.
“That sounds like custom learning,” you might think. Well…not quite. The next section will explain the difference.
Personalized Learning: The Perfect Blend
Personalized learning blends adaptive and customized learning. Therefore, a personalized learning experience adapts to the learner’s progress. Plus, it allows the learner to customize the experience to their preferences—goals, skills, career path, and more.
When the learner customizes the learning experience, it becomes more meaningful to them. Customization is one component of personalized learning. Think of customization as setting preferences—just like you do when you create a playlist in an app. When that app starts suggesting specific songs based on your customized preferences, that’s personalization.
The other component is continuity. Personalized learning is a continuous learning journey. It constantly adjusts content—with adaptive learning technology—to you and your preferences.
The adaptive learning process focuses on individual learning needs. It determines those needs with data analytics, which extract insights from real data.
Let’s dig into the perks of adapting training to the learner’s needs.
The Benefits of Adaptive Learning
Here’s why an adaptive learning experience is so beneficial to the learning process and goals:
- It makes the learning experience personal and meaningful. Adaptive learning suggests learning paths that resonate with you the most.
- It’s highly efficient. Adaptive learning only teaches you what you don’t know—it doesn’t waste time on skills or concepts you’ve already mastered.
- It’s effective. An adaptive learning experience starts with what you struggle with. Then it repeats that content throughout the training until you master the concepts.
- It’s inclusive. The adaptive learning process embraces different learning paces in a single training program. So, regardless of how fast learners absorb lessons, the learning experience respects and adapts to their progress. For instance, an adaptive course might share extra materials with slower learners or offer them remedial lessons and concept recaps. With this approach, a learner only moves on to new concepts when they have absorbed the previous ones.
- It standardizes learning outcomes. Each learner absorbs knowledge in different ways, so they have different learning outcomes with traditional training methods. Although learners have distinct styles, adaptive learning allows them to reach the same learning outcomes. Why? Because adaptive learning gives learners distinct pathways of getting to the same learning outcomes.
- It’s digital. And we know that digital ways of learning—such as the interactive technology that adaptive learning uses—appeal to younger learner generations. As a result, the learning experience is more engaging, which contributes to achieving its objectives.
- It’s data-based. Through data analytics, adaptive learning technology gathers and analyzes huge amounts of data from learning activities. Those data can be, for instance, assessment results or the amount of time spent on completing a task. Data analytics detects patterns in learning data, identifies learners’ skills and needs and delivers content based on those needs. And it does all of that in real-time—much faster than humans could.
- It’s more suitable for learners struggling with content than traditional methods. Learners who usually have difficulties succeeding with traditional methods get to the expected learning outcomes with adaptive learning. Custom tasks and personalized learning plans aren’t common in traditional learning settings, but adaptive learning includes them.
- It has a positive effect on learners. Adaptive learning makes learners persevere in their learning tasks and commit more to the training process. They feel more confident that they’re learning, and the chances that they’re dishonest when authoring assignments reduce. Finally, because of the adaptive learning pace, learners feel less stressed and overwhelmed.
Do you want to find out how adaptive learning operates? Jump on to the next section and pull back the curtain.
How Adaptive Learning Works
Below you’ll find the strategies underlying the operationalization of adaptive learning.
Resembling one-on-one tutoring with personalized learning experiences
Back in the ’80s, Benjamin S. Bloom researched learning methods in the hopes of finding one as effective as tutoring. Bloom named three ways students receive instruction:
- Conventional teaching. A teacher gives a lecture to a group of students in a classroom. Students listen, study outside of the classroom, and do tests once in a while to receive a grade.
- Mastery learning. This method is equal to conventional teaching, with one difference. Students receive feedback on their tests and continue to take tests on the same content until they master the subject matter.
- One-on-one tutoring. It’s the same as mastery learning except for the teacher-student ratio: there’s one tutor per 1–3 students. However, because that ratio allows for more personalized instruction, the probability of students needing to repeat tests decreases considerably.
The results of Bloom’s research were astonishing! Tutored students outperformed conventional students by 98%. And mastery learning students outperformed conventional students by 84%.
The conclusion was obvious: students who receive personal, one-on-one instruction master the subject matter considerably more than students in a conventional, one-size-fits-all learning environment.
Now, here’s the challenge: which companies can afford a tutor for every single employee? Bloom considered that intelligent technology could overcome the challenge by working just like a tutor.
Adaptive learning technology works similarly to a tutor in that it personalizes learning experiences. To a certain extent, it does the work that a tutor would do.
Confronting the learner with their unconscious incompetence
The goal of any training program is for learners to acquire the knowledge and develop the skills they need. And sometimes, they need a little more time and effort to accomplish that goal across the entire training scope.
So, identifying the points in which they need to invest more to get to the desired learning outcomes is imperative. They might not even have a clear notion of the areas they need improvement in.
In many organizations, the lack of this notion—or unconscious incompetence—is a serious issue. Your employees may not know what they don’t know. And sometimes, they may think they know something when they really don’t. Employees may come into your training course unreceptive to learning if they think it isn’t relevant to their learning needs. Adaptive learning addresses unconscious incompetence in training. Let’s take what we did for Google as an example.
Google asked us to produce a programmatic advertising course. They told our team that their employees thought they knew everything about the topic, but they didn’t. Therefore, our goal was to catch Google employees at not knowing everything—in a nice way, of course. And our strategy was to open the employees’ minds to learning…unobtrusively.
Using adaptive learning technology, we created a very difficult yet fun pre-test before each course’s modules. When Google employees realized they didn’t know everything about programmatic advertising, they became motivated to learn.
Adaptive learning also allowed us to check Google employees’ skill development progress. We used scoring for different training elements and adaptive simulations for this purpose.
Scoring and simulations spot improvement areas, and adaptive learning technology adjusts training accordingly. From the learner’s perspective, this means they’ll have to apply extra effort in those areas. Simultaneously, adaptive learning applauds the learner’s success, which motivates them.
Practicing until it sticks to the brain
Malcolm Gladwell claimed in his “Outliers” book that practice is everything anyone needs to nearly become a pro. So, although someone doesn’t have a natural aptitude for a complex task, they can master it with effort. Gladwell even quantified that effort: 10,000 hours of practice.
Many have debunked the book since it first came out in 2008. However, Gladwell was right about one thing: practice does make perfect—or nearly perfect. If you practice something over and over again, eventually, it sticks to your brain.
That’s why adaptive learning technology works so well. Once it determines a gap in an individual’s learning journey, it repeats that point throughout the training. And it only stops when it has evidence that the learner acquired the knowledge.
Pushing memory to the max
What’s the point of learning something and forgetting it in the blink of an eye? Ebbinghaus’s forgetting curve suggests that without using what we learned, we tend to forget it. Roughly 70% of learners forget what they learned within 24 hours.
Personalized learning—which includes adaptive learning—improves knowledge retention and recall. However, we recommend going beyond adaptive learning for even better results.
Our advice is to use a blended learning approach, which combines learning technology with instructor-led training. Here are some options for you:
- An eLearning module followed by a webinar with a Q&A session
- Giving a live class after an eLearning module
- The first option followed by the second option
We mentioned adaptive learning technology quite a few times so far, but there’s still a lot to say about it. Head on to the next section to discover more about this incredible technology.
Adaptive Learning Technology Explained
Adaptive technology uses personalization to shape the digital channels that learners frequently use nowadays. That’s why an internet browser, an ecommerce website, or a social media network show information targeted at the user.
The main tenet of adaptive learning technology is to highlight what each individual learner needs to know to progress in the learning journey. In adaptive learning, learners only move forward when they’ve mastered previous concepts. So assessment is key to advancing in adaptive training programs.
For instance, consider that you’re using an LMS—or learning management system—to deliver your training course. You can equip it with adaptive capabilities to use the learner’s actions as input to determine their learning path.
But how does technology scale the benefits of adaptive learning to multiple learners simultaneously? Let’s discuss that next.
Designing if-then-else adaptations
With this kind of strategy, adaptive learning benefits scale by design. In other words, the learning experience designer defines a conditional content sequence. And that sequence guides the learner toward the expected learning outcomes and content mastery.
Are you wondering how exactly? With the support of adaptive learning technology, a conditional content sequence reacts to if-then-else conditions. Therefore, if a condition occurs, then something else happens with the learning content.
A condition is a factor with a specific value. Here are some examples of factors that can take different values:
- Learning performance—such as test results or time spent to complete a task or lesson
- Recurring mistakes that the system detects—those might reveal a learner’s misconceptions
- Knowledge level—for instance, the difference between prior knowledge and acquired knowledge
- Preferences for content types
- Demographic data that relates to the learner
Based on the values that these factors take, adaptive learning systems make decisions such as:
- Moving the learner in a certain direction on the learning path—for advanced learners that might be a fast track
- Providing hints about a concept before the learner continues to the next concept
- Allowing the learner to decide what to learn next
Developing automatic adaptivity
Another strategy to scale adaptive learning benefits is to use computer algorithms. They automatically evaluate what the learner knows and what they should experience next in the learning journey.
For instance, when a training course begins, an algorithm might measure the learner’s prior knowledge. Different measurement techniques exist—such as quizzes—but the goals are to:
- Avoid struggling learners becoming frustrated
- Prevent gifted learners from becoming bored
The result of the algorithm’s evaluation determines the next learning element. Evaluation continues as learners go through the training program.
Other aspects that an adaptive learning algorithm might appraise are:
- The rate at which learning happens
- The learner’s interaction with learning elements
Some adaptive learning systems simply deliver content at a variable speed. Sometimes they don’t have the ability to tailor the learning journey to each individual.
Effective adaptive learning systems should:
- Offer remediation resources to learners in need
- Incentivize exceptional learners with advanced content
Whether it’s with predetermined rules or automatic algorithms, adaptive learning tries to replicate the role of an instructor in one-on-one tutoring. So, the content depends on the needs of each learner individually.
Learn how adaptive learning can help your business succeed. Contact us today!