The Students Group Detection Based on the Learning Styles and Clustering Algorithms

DOI

10.2991/aebmr.k.201205.017How to use a DOI?

Keywords

learning styles, clustering algorithms, student groups detection, R-library NbClust

Abstract

The approach to automatically student groups detection based on Honey and Mumford’s questionnaire and index of learning styles questionnaire with the help of clustering methods are proposed in the paper. The methodology of our research consists of the following stages: 1) the evaluation optimal number of clusters and clustering students data based on Honey and Mumford’s questionnaire and Ward.D2 method realised in R-library NbClust; 2) the evaluation optimal number of clusters and clustering students data based on index of learning style questionnaire and k-Means method; 3) forming the clusters of students that are in one cluster based on both learning styles and description of the student clusters taking into account similarities on learning style preferences and similar interests in the social network.

Copyright

© 2020, 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  - Yu.Yu. Dyulicheva
AU  - Ye.A. Kosova
PY  - 2020
DA  - 2020/12/07
TI  - The Students Group Detection Based on the Learning Styles and Clustering Algorithms
BT  - Proceedings of the 2nd International Scientific and Practical Conference on Digital Economy (ISCDE 2020)
PB  - Atlantis Press
SP  - 106
EP  - 111
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.201205.017
DO  - 10.2991/aebmr.k.201205.017
ID  - Dyulicheva2020
ER  -