Using Artificial Neural Networks to Identify Learning Styles

Abstract

Adaptive learning systems may be used to provide personalized content to students based on their learning styles which can improve students’ performance and satisfaction, or reduce the time to learn. Although typically questionnaires exist to identify students’ learning styles, there are several disadvantages when using such questionnaires. In order to overcome these disadvantages, research has been conducted on automatic approaches to identify learning styles. However, this line of research is still in an early stage and the accuracy levels of current approaches leave room for improvement before they can be effectively used in adaptive systems. In this paper, we introduce an approach which uses artificial neural networks to identify students’ learning styles. The approach has been evaluated with data from 75 students and found to outperform current state of the art approaches. By increasing the accuracy level of learning style identification, more accurate advice can be provided to students, either by adaptive systems or by teachers who are informed about students’ learning styles, leading to benefits for students such as higher performance, greater learning satisfaction and less time required to learn.

The authors acknowledge the support of Alberta Innovates Technology Futures, NSERC, and Athabasca University.