The Theory of Relativity has remodelled the whole world. Once understood only by select individuals in the scientific fraternity, is now an essential part of the academic syllabus for various courses. Based on this fact alone, we can see how phenomenally the education scenario has changed over decades. What previously took leading scientists a lot of effort to understand is now common knowledge for students before they even graduate from college.

The learning curve, no doubt, has evolved and refined. With increasing technological intervention, this is amplifying further with each passing day. The reason can be largely attributed to the advanced pedagogical methods that technology inherently brings with it.

Just take graphics and animation as an example. They can be used to explain aerodynamics in physics, molecular structures in chemistry and internal organs in biology—all without leaving any reasonable scope for doubt in students’ minds. Today, technological tools are simplifying the process of imparting knowledge with inordinate precision.

With new digital entrants in the education space, these advanced learning methodologies are being refined before being targeted at students to enhance their learning curve. Traditional pedagogical methods often set a uniform learning pace that every single student has to follow. This causes problem because there is a gap in the learning ability of the highest and lowest performing student. Those close to the former in ability may end up spending a needless amount of time despite mastering concepts quickly. In contrast, those close to the latter in ability may find the learning pace too quick and miss out on valuable information.

It’s also possible that students might be quick to grasp a concept in a particular chapter and yet, struggle with another. There is, therefore, no parity in learning speeds between individuals; and sometimes, the same individual himself.

This is where big data is extremely useful. It is a collection of large sets that can be analysed for interactions, patterns and trends to understand user behaviour better. It assesses every single step taken by a user on an e-learning app.

Be it a lecture viewed, a question practised, a test taken or a doubt asked; every single gesture is captured and processed to understand the strengths, weaknesses, preferences and learning ability of each student. Machine learning algorithms use this data to tailor individual learning paths. This way they can learn better, at their own pace.

For example, if a student solves a question easily, yet struggles with another concept, these algorithms then offer him or her with unique ways to understand and get proficient at such concepts. They provide feedback, track progress, suggest areas of improvement and offer personalised ways to improve. The implementation of artificial intelligence creates a self-optimising system that learns and evolves over time.

Data is accumulated by capturing various permutations and combinations of a multitude of users through their activity on the app. This consists of factors like, but not limited to, chapters frequented, time taken to solve questions, number and frequency of lectures watched, test scores, as well as historical records available with educational institutions.

This is how e-learning apps serve as real-time enablers of personalised education. Big data is no longer a new concept in the global educational landscape. Businesses are now actively using it to understand consumer behaviour, traffic authorities for efficient crowd management and food administrations for the identification of drug-related illnesses.

Educational institutions and service providers are also actively integrating advanced algorithms into their pedagogies to revolutionise learning for students. With it, we are already witnessing an evolution in the way knowledge is imparted across the globe.

(The writers are chief executive officer & cofounder,; and co-founder,, respectively)