Rethinking Adaptive Learning in the Age of Cognitive Computing
Adaptive learning, or also called personalized learning, is actually how education started out. The wealthy had their children educated at home by tutors. It was only later that public schools for the common man came into operation.
While schools provide a much needed social environment for children to develop their interpersonal skills, learning as a group in a class has never been ideal – only a few students flourish in this environment.
So, group teaching in schools is not ideal, but being taught by a tutor is not cost-effective either.
So what is the solution?
The solution lies in the many capabilities of cognitive computing.
Through technologies like cognitive computing, systems can be created that can provide targeted education to each individual student. These cognitive systems would be able to know immediately when a student is falling behind and take appropriate action to make sure the student gains a full understanding of concepts being learned.
What are cognitive systems?
Cognitive systems are a category of technologies that use natural language processing and machine learning to enable people and machines to interact more naturally to extend and magnify human expertise and cognition.
Cognitive systems can analyze structured and unstructured data from diverse information sources. At the same time, these systems are able to take context into account and consider conflicting information, which enables them to formulate optimal solutions to questions and problems.
These capabilities are ideal for optimizing the promise of adaptive learning.
Cognitive systems can be exposed to information related to each individual student. This would be information accumulated over a lifetime and presented in the form of structured as well as unstructured data.
School reports and class attendance records would be examples of structured data. Class notes, essays, emails, photos of craft and other activities as well as audio files are examples of unstructured data.
Cognitive systems can synthesize all this information and develop teaching strategies and teaching materials ideally suited to each individual student.
IBM has developed such a system, called Watson. Watson can take all the data about each student and infer meaning from it. It can then adjust its recommendations based on what it has learned.
The need for individualized education
Today’s students have a short attention span. They need non-lecture-based teaching strategies to stimulate learning.
Besides, individual students use unique learning styles, and they learn better from study material that suits their learning style. Cognitive computing is ideally suited to personalized learning.
Cognitive systems enable and optimize the goals of adaptive learning.
In the normal learning setup, students are expected to all master new concepts and information in a set period.
With adaptive learning, on the other hand, students are allowed to progress through the material at their own pace. In fact, based on their answers and level of confidence, the cognitive system might adapt the teaching material.
For instance, different students in the same class may read different material and watch different videos based on what the cognitive system has learned about them.
Depending on their answers and the confidence levels with which they answered the questions, the platform will feed them content that’s relevant to them at that specific point in time.
Adaptive learning is the future of student education. The technology makes it possible to provide learning opportunities that suit the needs of each individual learner, constantly taking into account their level of understanding as well as the level of confidence they display.