Online Courses/eLearning Adaptive Learning: Is It AI Powered eLearning of the Future?

Adaptive Learning: Is It AI Powered eLearning of the Future?

What is Adaptive Learning?

Adaptive learning is a process where a course or course instructor adapts to the learning needs of learners, helping them grasp the underlying concepts quickly and efficiently. It’s been touted as a hot new trend used by Universities, training companies, and even the U.S. Department of Education.

To offer up a simple example, if you’ve got an online course helping people learn how to exercise, some people will struggle with doing pull-ups while others will struggle with doing sit-ups (or in my case, I’ll struggle with both).

In an adaptive learning course, your learners would have the ability to mark off, or take a quiz or assessment, showing what they’ve mastered (whether pull-ups or sit-ups). If they’ve mastered pull-ups, the course doesn’t need to keep showing them the training on pull-ups. Instead, your course would show them training on sit-ups until they’ve mastered that activity.

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Adaptive Learning is Not One-Size Fits All

The concept can also be defined as a learning experience which is more personalized and spontaneous. It’s more personalized because learners get continuous feedback as they try to understand the material.

Adaptive learning is similar to one-on-one teaching by a personal instructor.

Such teaching patterns are evident in the teaching styles of tutors and fitness trainers who provide instant feedback to the student by answering their questions for just-in-time feedback. Students can get instant help on the specific topic they need help with which enhances the learning experience. It also saves time since they are not having to learn material they’ve already mastered.

Adaptive learning can include some of the following:

  • Getting just-in-time-help answering questions or while taking an assessment.
  • Gaining feedback on the work to see how well learners are doing on a particular topic.
  • Knowing what to study next.

Take a look at some adaptive learning examples and see how companies are using this technology.

Personalized Learning Experience

The entire process also helps course instructors as they adapt to the passions and preferences of their learners.

Like you can imagine, in a traditional environment, instructors cannot provide an intensive personalized session to multiple students. As the number of students increases, it becomes almost impossible for instructors to adapt to the changing learning needs of a growing student base.

For example, everyone in a classroom is shown a presentation on the same thing, whether they’ve mastered it or not. This is precisely where adaptive learning technology comes into play as instructors can get help from technology in order to provide feedback to multiple students at once. Even better, when it comes to online training, the instructors don’t even need to be present!

Variations of Adaptive Technique

Adaptive technology adapts to the needs of students. Algorithm adaptivity and designed adaptivity are two basic concepts.

Designed Adaptivity

Designed adaptivity is a process where a course instructor designs the entire process. The course creator decides how to train the learners at any given stage. Using technology, the instructor can tell the system how to adapt to a situation based on a variety of adaptivity factors.

The adaptivity factors designed by the instructor enable the technology to react to different situations using “do THIS, then THAT” rules based on the actions of the learner.

Learners get instantaneous feedback based on their progress. For example, certain learners would need to spend additional time on a previous lesson while other learners can move onto the next lesson.

Algorithmic Adaptivity

In contrast, algorithmic adaptivity technology adjusts the learners experience. Using the algorithmic adaptivity method, learning revolves around what the learner already knows and based on that, what the learner should experience next.

Based on that, the algorithm decides the optimum solution to enhance the learning experience. Algorithmic adaptivity is often very useful where the lesson is easy to organize.

Adaptivity Factors & Types

Adaptive learning requires adaptivity factors and adaptivity types.

Adaptivity Factors

Adaptivity factors are sets of data that highlight the performance, behavior, and information of each learner. Since the learning experience of everyone is different, these factors help instructors understand how the adaptive system should adapt to the unique needs of the learner.

For instance, information can be a factor. Information can provide the instructor with useful data about the knowledge and demographics of a certain learner. Using the information, an instructor can design a system according to the knowledge and demographics of each learner.

Likewise, performance is another adaptivity factor that can provide course instructors with a solid cue on the ongoing progress of a learner. Using the performance benchmark, technology can help course instructors adapt to the progress of individual learners. If necessary, the instructor may also intervene depending on the flexibility of the designed system.

Another adaptivity factor includes using the unique behaviors of a group of learners.

Adaptivity Types

Apart from adaptivity factors, adaptivity type also plays an instrumental role in the development of a system.

Adaptivity type, or adaptables, can include real-time feedback and differentiated pathways. Real-time feedback may be necessary in certain circumstances when the instructor determines inappropriate behavior or lack of progress.

Real-time feedback doesn’t mean the course instructor has to be present. Instead, it can include a situation where a learner isn’t mastering the material, so instead of answering 10 questions and then giving a score, the software can provide feedback immediately after a question is answered wrong. The feedback can include the correct answer as well as a link to the course material that provides an explanation.

A differentiated pathway for individual learners is an example of adaptivity type. If there are learners from diverse backgrounds and experiences, pathways are often very effective methods to enhance learning experience. Using pathways, learners can experience different journeys on their path to successful completion of the course.

How to Use Adaptive Techniques?

Online course creators and instructors can use a variety of adaptive learning platforms to help their learners.

Change Curriculum Sequence

One option is to use the method to change the curriculum sequence as learners go through their courses. Depending on the situation, the system may allow the course creator to make changes to future lessons to adapt to the existing pace, skill, and mindset of learners.

Learn At Own Pace

Adaptive learning can provide the opportunity for learners to go through the material at their own pace. Countless scientific experiments have demonstrated the benefits of allowing learners to review material at their own optimal pace so they may grasp the entire concept. Accordingly, course creators can easily change the curriculum sequence and the pace of development for each individual learner.

Prior Knowledge Pathways

Another strategy used by course creators is to base the course sequence and pace on the prior knowledge of each learner. In these instances, the adaptive algorithm would continuously evaluate each learner before and after they have started the lesson. Based on their learning patterns, each learner is provided a different set of lessons. Struggling learners are not frustrated, and advanced learners are not bored.

Determining Skill Set

These learning systems are also used to determine the learners skill set. Modern computerized adaptive testing is a perfect example where each learner is given a question of varied difficulty level based on their previous answers.

Using the adaptive technology, course developers have realized that creating learning can be a lot of fun.

In Combination

In addition to adaptive learning techniques, many course creators are implementing microlearning into their courses. Microlearning revolves around creating bite-sized pieces of training content geared to the mastery of a single objective.

Both microlearning and adaptive learning can also be combined with elearning gamification. This strategy allows learners to gain experience points, placement on a leaderboard, gain badges, or other gamifying elements as they work through a course.

The Future of Adaptive Techniques

Adaptive technology is increasingly being used by educational institutes, private corporations, small online course businesses, and non-profit institutes. Even major publishers like McGraw Hill are shifting their priorities towards adaptive technology.

Similarly, vendor offerings have also increased significantly as course instructors are given lucrative discounts and incentive to integrate adaptive software in their course design. This technology can be incorporated into virtually any online course small or large. In fact, a growing number of institutions are recognizing the need for more personalized learning experiences.

In a nutshell, adaptive learning technologies are helping course creators determine the most effective sequence of learning for their learners.

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