Uncovering cognitive influences on individualized learning using a hidden Markov models framework

Abstract

A defining characteristic of intelligent tutoring systems is their ability to adapt the learning plan to individual variances of the learners. A main question in this adaptation is the order of the learning concepts an intelligent tutoring system should offer to a human learner, when there is a dependency relationship between the concepts. We studied this problem by utilizing a Markov chain model within a hidden Markov models (HMMs) framework to harness an observed dependency relationship between concepts of a Java programming course. We found that hidden factors such as the age and gender of the learners affect their learning path, emphasizing the effectiveness of the underlying dependency relationship between the tested programming concepts. To better nurture a well-performing intelligent tutoring system, we provide a natural algorithmic approach that takes into account, beside existent concept dependencies, also individual characteristics of the learners, so as to minimize the learning time of learners and enhance the system’s efficiency.