“Adaptive learning” is a popular buzzword in today’s discussion around education and training. But what does it really mean? What can it do? What are its limitations? Let’s explore the potential of this learning concept.
What Is It?The fundamental idea behind adaptive learning is that the learning system adjusts to an individual's learning requirements. Adaptive learning uses algorithms to tailor a learning experience to an individual. It delivers customized learning activities based on the learner's mastery in a given topic or course. This is not a new concept, but in recent years technological advancements driven by the growing demand for customized learning experiences that cannot be achieved on a large scale using traditional methods have been making it a reality.
How Does It Work?The technology behind adaptive learning involves artificial intelligence, psychology, psychometrics, and even machine learning. Most systems use a similar framework.
The instructor creates a knowledge map that links the topic areas within a course and the concepts within each topic area, showing how important concepts are related to each other. This is often referred to as an “inventory of knowledge” and is organized using various interconnected nodes to make it easier to locate information.
Below is an example of a simple knowledge map for a course involving six concepts. For the purposes of this example, let’s name them concepts A, B, C, D, E, and F.
For learners to be able to learn concept E, they must first master concepts A, B, and C. If learners can correctly answer questions from concept F, it indicates they have already mastered concepts A, B, C, D, and E.
Once the knowledge map is in place, the adaptive learning system performs a knowledge check or knowledge test to identify which concepts the learner already knows, just as a personal tutor may evaluate the learner’s knowledge by asking questions. In an adaptive learning system, the learner takes an online assessment or quiz that is graded automatically.
Using the knowledge check, the system “learns” about the learner's current level of mastery for each topic. The learning program combines this information with the knowledge map to form a learning path. This learning path focuses on the concepts the learner has not yet mastered.
Using this example, suppose that a group of learners must master six concepts in a course. Sara’s quiz results show she is already an expert in concepts A, B, and C, knows little about concept D, and knows nothing about concepts E and F. The adaptive learning system will create a custom learning experience for her that covers little from concepts A, B, and C, quite a bit from concept D, and even more from concepts E and F. On the other hand, Sara’s classmate Ravi is familiar with concept A but not with concepts B through F. The system will ensure that Ravi sees more content from the areas in which he is least proficient.
What Are the Benefits for Instructors and Learners?One of the goals of adaptive learning is to make the learning experience scalable and accessible to a wider audience and with less effort on the instructor’s part. An adaptive learning system can replace many of the routine and repetitive activities that instructors perform—for example, providing information, administering tests, grading, and analyzing student performance—leaving them free to address specific learner difficulties and teach higher-order thinking. Instructors can easily monitor learners’ progress, and the system can be designed to alert instructors when a learner is struggling with a concept and needs additional in-person coaching.
An adaptive learning system customizes content for the individual learner, focusing only on the content learner needs, rather than presenting the same material to all learners as traditional learning methods do. The learning system may also be self-paced, allowing learners to go at their own speed and practice activities as many times as required. Remote learners and learners who do not have access to traditional classrooms can benefit from this learning method.
What Are the Limitations?Adaptive learning tends to work well for lower-order thinking. It provides information and allows learners to practice straightforward problem solving. When the material demands higher-order thinking and open-ended responses, this system is less effective.
The limitations of auto-gradable question formats used in most adaptive learning systems play a part in this. For instance, in quantitative subjects like math or physics, the quizzes focus on learners reaching a final correct answer; however, in these subjects, most of the learning occurs as learners work through the steps and calculations that lead to a final answer. One way to overcome this limitation is to structure questions in such a way that the system evaluates learners based on the steps and calculations they take to arrive at the final answer. Even better, an ideal system would provide for multiple approaches to solve the same problem.
Just as a good personal tutor monitors the approach a learner takes to solve a problem, an effective adaptive learning system should help guide the learner through the steps required in problem solving. An efficient feedback system that points out exactly where the learner has gone wrong is essential in adaptive learning. This feedback system can be automated to provide relevant question-level and step-level hints to help the learner.
Learning depends on more than just presenting content. Learners also benefit from the social factors that accompany the traditional aspects, like being exposed to a variety of viewpoints and interactions with instructors and peers. A combination of a good instructor and the technology that adaptive learning offers strikes the perfect balance for a well-rounded, effective learning experience.
By using learner feedback, algorithms, and instructor coaching to deliver personalized, customized, targeted learning experiences, adaptive learning is an excellent tool to advance a learner’s knowledge and complement traditional teaching methods.