Analysis of Online Learning Style Model Based on K-means Algorithm

DOI

10.2991/emle-17.2017.148How to use a DOI?

Keywords

adaptive learning system; online learning style; clustering

Abstract

Adaptive learning system is an ontology-based system that will provide relevant learning resources as a personalized bespoke online learning package for the learner based on their pedagogical needs. Thus, learner modeling and resources modeling need to be done in order to implementing the adaptive mechanism. Learning styles which refer to learners' preferred ways to learn can play an important role in learner modeling. This paper, by reviewing existing learning style theories and comparing the differences between traditional learning and online learning, proposes a new learning style model for online learning environment. What's more, an online learning style survey is designed for investigating learners' preferences and learning behavior. Then a classification rule for online learning style is discussed after mining the collected data based on k-means clustering algorithm. The results offer insights into a different classification mechanism for learning styles and prove the feasibility of the new online learning style model.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Rumei Li
AU  - Chuantao Yin
PY  - 2017/12
DA  - 2017/12
TI  - Analysis of Online Learning Style Model Based on K-means Algorithm
BT  - Proceedings of the 3rd International Conference on Economics, Management, Law and Education (EMLE 2017)
PB  - Atlantis Press
SP  - 692
EP  - 697
SN  - 2352-5428
UR  - https://doi.org/10.2991/emle-17.2017.148
DO  - 10.2991/emle-17.2017.148
ID  - Li2017/12
ER  -