@inproceedings{0d0a29044ffa43ddab2d02c32a70dc97,
title = "Automatic detection of learning styles in learning management system by using literature-based method and support vector machine",
abstract = "Each learner has their own preferences in the learning process. Differences in preferences are closely related to the learning style of each learner. Personalization of e-learning is an overview of online learning that has been customized content based on learning styles of each learner. Detecting learning style needs a technique that is effective and accurate. This study combines literature based method with Support Vector Machine (SVM) to detect students' learning styles. The data used is learning log data of Data Structures and Algorithms class at the Faculty of Computer Science, Universitas Indonesia. The test results showed that SVM has better accuracy compared to Naive Bayes.",
keywords = "E-learning Personalization, SVM, automatic detection, learning style",
author = "Amir, {Elfa Silfiana} and Malikus Sumadyo and Sensuse, {Dana Indra} and Sucahyo, {Yudho Giri} and Santoso, {Harry Budi}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 8th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016 ; Conference date: 15-10-2016 Through 16-10-2016",
year = "2017",
month = mar,
day = "6",
doi = "10.1109/ICACSIS.2016.7872770",
language = "English",
series = "2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "141--144",
booktitle = "2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016",
address = "United States",
}