have emerged out of labs into our daily routine activities. Whether it is the buzz around autonomic vehicles, drones, recognition, various voice response systems like and Google assistant, every single one of these products have some form of at its core.
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Artificial Intelligence () as an idea seems to have caught the imagination of both industry and academia alike. Although related academic research has been in place since late nineties but it is recently that products and services inspired by have emerged out of labs into our daily routine activities. Whether it is the buzz around autonomic vehicles, drones, recognition, various voice response systems like and Google assistant, every single one of these products have some form of at its core.
Undoubtedly, ever increasing processing speeds and storage capacities along with possibilities of machine to machine (M2M) communication have set the cat out of the bag. Today we produce more data in a single day then possibly we did in the entire year in eighties. It did not take long for the industry stalwarts to realize that they were not storing and analyzing a lot of data that could help in offering completely new and customized services, thereby bringing completely new revenue streams in existence. Now with every vertical across industry engaged in understanding exactly how and analytics could help them transform, education as an industry too followed suite. This article aims to differentiate the achievable from the hype in this arena.
Various scholarly and popular articles over years have identified domains within higher education where and analytics together could reshape the future. Let us break this down into in-classroom and learning oriented areas as well as beyond classroom processes. For learning oriented and in-classroom processes, traditional classrooms have often been blamed to be unresponsive and mass delivery focused. In other words it does not enable personalization of educational experience (this needs to be interpreted as way beyond anytime access to material which is easily available today). Every participant irrespective of backgrounds might have different learning needs and more importantly learning may happen at a varied pace.
based algorithms could help assess before hand the kind of learning set-up and speed that would be suitable for a specific participant. Subsequently, a blended, adaptive learning environment could be implemented wherein the coursework could move with appropriate video lectures, forums and specific teaching assistance as is required by the participant. The most obvious impact of and analytics could be felt in evaluation methods.
could help the participant in identifying the level of understanding reached after relevant sections by exposing students to adaptive evaluation, at times leading to participant going back to the sections where learning is identified as inadequate. could also help in assessing the parts of coursework where greater human intervention may be required for higher learning impact.[…]