Type to search

Self Learning

Once Available to a Few Privileged Students, Adaptive Learning is Now Accessible to All


Personalised learning is based on the premise that no two students learn the same way; their learning pace and style vary. Therefore, there can be no correct or single sequence to learning. The one-size-fits-all approach adopted by traditional learning systems leads to discrimination amongst students; while some are termed smart, others get labelled as unsmart. Personalised learning identifies learning gaps and mitigates those to ensure that the student makes progress. It is a more efficient way of learning as it caters to individual learning needs. However, personalized learning requires disproportionate resources because it involves understanding how students learn, how much they know and their interest levels.

The call for and interest in personalised learning is not new. It dates back to almost a century ago, with educators frowning upon the proliferation of mass schooling. For instance, in the 1920s, Carleton Washburne, an American education reformer, conducted an experiment in Illinois. He devised a strategy that came to be called programmed instruction, a precursor to personalised learning. It stated that while certain essentials were needed to be mastered by students to make learning progress, students also needed to be allowed to make learning progress at their own pace with no strict standards to adhere to. History is marked with many similar initiatives to boost personalised learning, but none of these efforts was sustained due to the lack of finance and other resources like highly-skilled teachers.

Personalised learning available, but not practical

Initially, implementing personalised learning was a tedious affair. It needed an ideal student-to-teacher ratio where a skilled teacher could give individualised attention to each student in a class strength of 6 to 8 students. Only a select few elite schools were able to facilitate a personalised learning environment to students. However, a modest school, lacking skilled teachers and the ideal student-to-teacher ratio, could not implement personalised learning. Thus, in the absence of such learning opportunities in most schools, parents chose to enrol students in coaching classes or appoint private tutors. The scenario in group coaching classes was not that encouraging either. Appointing skilled private tutors might have been an ideal scenario, but it would certainly have been a costly one.

A private teacher for one subject charges approximately ₹ 2,000 to ₹ 5, 000 per student for 1 month. The advent of technology in the education sector has democratised learning, making it available to a larger audience at an affordable cost. Adaptive learning would cost around ₹ 200–500 for each student per month. Moreover, this can help even a less-skilled teacher to facilitate quality learning to students.

Technology brings personalised learning to all

In the last few decades, technology has gratified humans with many innovations and has been able to reach not just a select few, but also a large section of the population. For instance, international call rates have gone down drastically in the last couple of years and is no longer perceived as a luxury, but rather a part and parcel of regular life. That is the power of technology in real life, and it can exert far-reaching effects on the education sector too. With technology, more specifically, artificial intelligence making inroads into the education sector, personalised learning has finally got a boost in the form of adaptive learning, a tech-mediated way of presenting learning materials as per the individualised learning requirement of each student.

Adaptive Learning technology

Adaptive learning requires gaining a thorough understanding of a child and his skill level and also quantifies the knowledge of a child on different topics accurately through different Item Response Theory (IRT), one of the components of adaptive learning. IRT asks aspirants a mid-level question and based on their response to it, the difficulty level of the next question and the subsequent ones are modified. Questions are asked based on the estimated ability of the candidate; hence, fewer questions suffice in assessing a candidate’s capability.

Knowledge Space Theory (KST), one of the other components of adaptive learning technology mimics a teacher’s understanding of students’ learning gaps and requirements accurately. Artificial intelligence (AI) closely monitors the performance and learning pattern of a student, and with this data fed into the adaptive platform, KST delivers a knowledge graph for each student. KST maps the accurate knowledge state of a student being assessed within the vast knowledge space.

There are two stages – what a student already knows, and what they are ready to learn. There are separate but distinct knowledge sets which have to be mastered by the student in order to master the subsequent ones. For instance, in math, addition has to be mastered to move on to the concept of multiplication.

An optimal learning path
Artificial intelligence (AI) learns about the performance of students and categorises students as per their learning capacity and needs. Once the knowledge graph of a particular student has been established, artificial intelligence recommends to students the optimal learning path based on its past experience with this particular student and also with students from the same category. While a kinaesthetic learner might learn best with hands-on activity or simulations, a visual learner will gain a clearer understanding of concepts by watching audio-visual videos. Adaptive learning technology understands the resource a student best responds to. Thus, learning is more effective when students learn through a tool that interests them the most.

We, at Next Education, are working on our adaptive Learning Management System, NextLearningPlatform (NLP). It would present the optimal resource tool from our vast pool of content containing different kinds of content — simulations, audiovisuals and real-life videos which encourage inquiry-based learning in students. This would ensure that students follow the best-suited learning path and attain learning goals in an optimal time. Besides being time-effective and cost-effective, adaptive learning technology keeps up the interest level of students. Adaptive Learning believes that all knowledge can be mastered by students and helps them reach a desired state of knowledge. The categories of smart and unsmart students cease to exist, thus ending the discrimination. It is certainly a morale booster too.

Beas Dev Ralhan

Beas Dev Ralhan has done his Master’s degree in Chemistry at IIT Bombay and has an MBA from the London School of Business. A technologist at heart and entrepreneur by nature, he has led Next Education from a fast-paced educational technology company to one of the most respected education solutions provider in India.


You Might also Like

Leave a Comment

Your email address will not be published. Required fields are marked *