TY - GEN
T1 - Using artificial neural networks to identify learning styles
AU - Bernard, Jason
AU - Chang, Ting Wen
AU - Popescu, Elvira
AU - Graf, Sabine
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Felder-silverman learning style model
KW - Identification of learning styles
UR - http://www.scopus.com/inward/record.url?scp=84948969559&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-19773-9_57
DO - 10.1007/978-3-319-19773-9_57
M3 - Published Conference contribution
AN - SCOPUS:84948969559
SN - 9783319197722
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 541
EP - 544
BT - Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings
A2 - Conati, Cristina
A2 - Heffernan, Neil
A2 - Mitrovic, Antonija
A2 - Felisa Verdejo, M.
T2 - 17th International Conference on Artificial Intelligence in Education, AIED 2015
Y2 - 22 June 2015 through 26 June 2015
ER -