Adaptive learning helps increase learner engagement by:
- Offering learning content and challenges based on the individual’s level.
- Ensuring flexible experiences that allow learners to progress at their own pace.
- Avoiding the tedious repetition of already-mastered skills, allowing learners to progress to more complex skills once they are ready.
- Providing enough data to instructors so they can tailor instruction accordingly
To do that, the adaptive system evaluates the learner’s level of knowledge, gaps, and preferred learning styles and uses that information to recommend user-aligned learning paths. You can learn more about adaptive learning and its advantages for schools, universities, and corporate learning and development here and here.
At CanopyLAB, we are constantly iterating upon our adaptive functionalities and are happy to share our experience researching adaptive models, conceptualizing- and building them.
Algorithms used in adaptive learning
An adaptive learning system is a collection of multiple models used to analyze and understand the users’ behaviors and create a suitable learning path for each user. Two popular approaches can build those adaptive learning models: Content-based filtering and Collaborative filtering. Each model is described below:
An adaptive learning system is a collection of multiple models used to analyze and understand the users’ behaviors and create a suitable learning path for each user.
Content-based Filtering
Content-based filtering is an algorithm that attempts to predict a suitable recommendation for a user based on that user’s activities. The algorithm suggests using attributes assigned to objects and matching them to a user. This means that just as your Disney+ or Spotify accounts suggest new shows, movies, and music you should listen to based on your past preferences, our algorithm can also offer a new “object” – a course, exercise, or learning path – based on your past activities and performance.
just as your Disney+ or Spotify accounts suggest new shows, movies, and music you should listen to based on your past preferences, our algorithm can also offer a new “object” – a course, exercise, or learning path – based on your past activities and performance.
Moreover, a user’s profile is created using data derived from the learner’s prior knowledge, performance, and feedback on the platform. By deriving information from multiple courses and analyzing learners’ activities, this algorithm aims to find their knowledge gaps and suggest a suitable learning path for each learner. (That is to say: cut out all of the boring, repetitive actions typical to the classroom.) In a traditional classroom, a teacher has to keep track of every student’s progress, and in a company, the L&D team must keep track of every single employee’s progress, which can get overwhelming and quite disorganized pretty quickly, which results in some learners staying behind and getting neglected. However, content-based filtering makes it possible to get information on every user based on their activity and suggests a personalized learning path to ensure they reach their goals, learn new skills, and exploit their full potential!
content-based filtering makes it possible to get information on every user based on their activity and suggests a personalized learning path
Content-based filtering is a straightforward algorithm that can work even with limited information. In this way, we can avoid the cold start problem, meaning when the platform is new and lacks user connections. However, one challenge of designing this algorithm is selecting a good set of course attributes. With incorrect or inconsistent attributes, the algorithm might give irrelevant or repetitive suggestions. For example, let’s say you liked the movie Iron Man and so the algorithm will suggest you watch Captain America next. The thing is, you probably don’t need anyone to recommend that, and so the suggestions may become repetitive. It would be better if the engine could come up with unexpected results, so this algorithm is usually paired with collaborative filtering.
Collaborative Filtering:
Another recommendation algorithm that is used in adaptive learning is collaborative filtering. Collaborative filtering consists of filtering information or patterns using collaboration among multiple users.
In an online learning platform, when multiple students take similar courses and interact with each other, the system creates a connection between them and groups them (based on their knowledge level, learning style, etc.). The same learning pattern will be suggested to students within the same group.
Because of this it is used in social networks such as Facebook or marketplaces such as Amazon. Additionally, adaptive learning platforms that also have an integrated social network, like CanopyLAB, allow users to get unexpected and exciting recommendations.
Collaborative filtering analyzes user behaviors in-depth. Hence, it can give more personalized suggestions to learners than the previous approach. This particular functionality really boils down to how the internet understands a group’s behaviors and provides recommendations to new users based on that understanding.
The CanopyLAB approach to adaptive learning
The rockstars in our development team at CanopyLAB designed a Multi-model Adaptive Learning framework, which is composed of:
- Learner Model: Analyzes users’ historical activities on the platform and builds a “user learning profile” based on: knowledge and exercise tags they have gained, courses they have learned, their performance on past activities, etc. In a few words, this is how our platform understands your needs and gives you personalized recommendations.
- Content Model: Using Natural Language Processing (NLP) methods to detect attributes of courses and exercises automatically. More specifically, a content model helps the platform understand the content by breaking the courses into smaller parts and studying how they relate to each other and their details. In this way, the algorithms automatically derive information from the course’s materials and help course creators build their content, reducing the time they spend on this task so they can focus on one-to-one tutoring or on creating new content.
- Adaptive Engines: We combine multiple methods, including content-based filtering and collaborative filtering, to build multiple adaptive learning patterns to leverage the benefits of both algorithms. We combine them to make sure we provide an adaptive learning experience that is as personalized for each user as possible.
Some of the features we use at CanopyLAB to make sure you get a tailored learning experience include:
- Adaptive Quiz – Repetitive Format: Including this quiz in your courses allows learners to practice the concepts they need to learn by repeating them until they master them. This gives them more chances to find the correct answers and fully comprehend the ones they got right the first time around. In a few words, this quiz is based on the premise that practice makes perfect.
- Adaptive Quiz – Difficulty Format: The difficulty format adaptive quiz is more advanced than the repeat format. It adapts to the student’s level by giving a skilled student more challenging questions and a struggling student easier questions. By doing this, it claims to fight learning fatigue. After answering a question, learners will move up or down in difficulty levels depending on their ability to answer correctly!
- Adaptive Entrance Quiz: This is used to evaluate users’ knowledge before taking the course. Based on that, the algorithm will suggest which materials learners should focus on and which materials can be skipped because of already having mastered their subject matter. Just like when you first open a Duolingo account, it benchmarks your knowledge of the language you want to learn and suggests the level you should start with to progress most efficiently. Our impressive AI and development team is working to implement this feature on our platforms!
AI-powered adaptive learning is the educational methodology of the future because, thanks to its algorithms, it is possible to create personalized learning paths based on any content and allows learners to gain new skills at their own pace.
In conclusion, AI-powered adaptive learning is the educational methodology of the future because, thanks to its algorithms, it is possible to create personalized learning paths based on any content and allows learners to gain new skills at their own pace. Furthermore, the data gathered and studied by these algorithms give instructors the actionable information needed to develop better courses that address the needs of their students and ensure no one feels left behind. As we’ve mentioned in earlier blogs, if a learner feels too challenged or that a task is too difficult, the learner is likely to lose faith and even feel anxiety. On the other hand, if the learner isn’t challenged, they will probably lose interest and eventually all engagement. For the learning process to have the right conditions, we need to locate the zone in the middle of these two scenarios – that sweet spot where the learner is challenged just enough to learn. As this zone is specific to each individual, we’re harnessing the power of AI to ensure our algorithms come together to create the perfect educational experience for anyone and everyone.
we’re harnessing the power of AI to ensure our algorithms come together to create the perfect educational experience for anyone and everyone.
Are you ready to experience the future of education and the many benefits of adaptive learning?
Try out CanopyLAB’s intelligent, adaptive, and social learning space and give your team and students a personalized learning experience that will leave them curious for more.
