ABSTRACT
For decades, learners and teachers alike have been involved in the manual process of give and take when it comes to interactions in education. Teachers are burdened in terms of preparing course content and grading/evaluation of learner performance whereas learners are involved in a “one size fits all” gauntlet of learning which is not individualized as per their needs, learning context and cognitive abilities. There is a notion that one must be taught with respect to their age, which, if considered deeply, may feel inhibitive and incorrect on the basis of different age groups. The use of deep learning, a subset of Artificial Intelligence, can allow learning management systems to achieve greater power and flexibility in terms of representation of the world as a nested architecture of concepts that differ at an abstract level. These systems can actively track learner knowledge, engagement and performance, and evaluate the likelihood of a learner dropping out of the course which may detect learners at risk and provide them with timely support/guidance throughout their course coverage. The use of procedures such as predictive modeling and grouping can play a very important role in this context for adaptive learning systems. It significantly benefits learners to individualize learning as per their needs and abilities, to enhance the learning quotient, performance and appraisal. Learners who are capable of completing basic concepts faster than the others can move on towards learning advanced concepts while others learn the basic concepts at their own pace. This book chapter will encompass predictive modeling basics, types of learning, and a detailed account of intelligent tutoring systems and their design, and will comprise of various types of predictive models and some elaborations based on a few commercial models as examples.
