To deliver a truly personalised learning experience, adaptive learning considers three foundational elements of teaching: the curriculum, the content and the individual.
Adaptemy uses 15 algorithms across these three pillars to get the experience just right, and we begin by building a curriculum map.
Understanding the curriculum
Every iteration of our software begins life like this: We work with a publisher’s subject matter expert to understand the curriculum, using two theoretical frameworks.
The first is Knowledge Space, a mathematical cognitive science developed in 1980 and adopted by the earliest adaptive learning solutions in 2001.
This theory models the ways in which elements of a domain of knowledge can be gathered to identify the distinct knowledge states of individuals. In other words, how much is there to be learnt about this subject?
The second framework is Bayesian theory, a model of probabilities which estimates an individual’s understanding of a concept, based on previous experiences and knowledge.
We have built a bespoke curriculum map design tool which helps publishers build the curriculum map. These two theories allow us to comprehensively understand all there is to be learnt about a subject and how to navigate a learner through the subject in a logical and progressive manner.
Understanding how people learn
One of algorithms Adaptemy products reference here is Learning Styles. It determines which style of content (video, text, audio and so on) is generally best for the content, and which type best suits the profile and preference of each student.
In this way, the software is continually serving the next, best piece of content to the student based on the curriculum map and their personal learning styles.
Critical to all of this however, and before this journey can begin, is the need to understand where each student is on their learning journey.
To master a subject, it is necessary to only progress when competence has been achieved. We need to understand each student’s level of competence and move forward from there.
We’re building a Zone of Proximal Development here; that is, an understanding of what a student can do without help and what they can do with help.
Unlike some more traditional approaches to competence and skills testing, we establish this over two to three short sessions. It is a very negative experience to sit a full-scale competence test on the first day of school. Rather, we recommend a 10-minute experience on the first day, 20 minutes on the second and so on.
And because we use Bayesian theory, our adaptive learning technology gradually learns about the student, continually accumulating and applying evidence. The technology says, ‘every time you do something with this system, I’ll learn a little bit more about you and update your experience accordingly’. This iterative process delivers an accurate and successful experience.