Adaptive user interface for personalized education based on Hidden Markov Model

Abstract

Personalized Education (PE) [1][2] encompasses the identification and understanding of individual learner’s needs and competence and then adopts and deploys appropriate learning pedagogy and content to bridge the learner’s knowledge gap. In this paper, we propose a technique based on the Hidden Markov Model (HMM) for intelligent man-machine interface adaptation for Personalized Education. Particularly, we model the user’s interactions with the PE system through a Markov process in such a way to enable the system to learn and to adapt PE services to different types of users. The HMM approach provides a formal framework for representing and learning different user modes which can subsequently be used to predict user competence and to anticipate user’s access to various personalized learning activities within the system.