Using artificial neural networks to identify learning styles

Jason Bernard, Ting Wen Chang, Elvira Popescu, Sabine Graf

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

27 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings
EditorsCristina Conati, Neil Heffernan, Antonija Mitrovic, M. Felisa Verdejo
Number of pages4
Publication statusPublished - 2015
Event17th International Conference on Artificial Intelligence in Education, AIED 2015 - Madrid, Spain
Duration: 22 Jun. 201526 Jun. 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Conference on Artificial Intelligence in Education, AIED 2015


  • Artificial neural network
  • Felder-silverman learning style model
  • Identification of learning styles


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