Personalized Adaptive Learning using Neural Networks

Abstract

Adaptive learning is the core technology behind intelligent tutoring systems, which are responsible for estimating student knowledge and providing personalized instruction to students based on their skill level. In this paper, we present a new adaptive learning system architecture, which uses Artificial Neural Network to construct the Learner Model, which automatically models relationship between different concepts in the curriculum and beats Knowledge Tracing in predicting student performance. We also propose a novel method for selecting items of optimal difficulty, personalized to student's skill level and learning rate, which decreases their learning time by 26.5% as compared to standard pre-defined curriculum sequence item selection policy.