Analysis of students' e-learning behavior based on Bik-Means clustering algorithm

DOI

10.2991/cnct-16.2017.92How to use a DOI?

Keywords

E-Learning Behavior, Clustering, Bik - Means algorithm

Abstract

With the development of education technology, the number of E-learning users rises dramatically. How to evaluate and classify students through their learning ability and how to provide personalized guidance to students become valuable research points. Work in this paper based on the open source e-learning behavior data viaDataShop (http://www.pslcdatashop.org/help page=citing). First of all, this data is preprocessed to get each student's correct rate of each learning time (CRELT) (The basic unit of time is one hour) and hint times of each question (HTEQ). Then the data is classified via Bik-Means algorithm to get the classification about students' learning ability.

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  - Li-xian Zhao
AU  - Rong Li
AU  - Jun-min Ye
AU  - Zhi-feng Wang
AU  - Xun Bin
AU  - Da-xiong Luo
PY  - 2016/12
DA  - 2016/12
TI  - Analysis of students' e-learning behavior based on Bik-Means clustering algorithm
BT  - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)
PB  - Atlantis Press
SP  - 665
EP  - 672
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnct-16.2017.92
DO  - 10.2991/cnct-16.2017.92
ID  - Zhao2016/12
ER  -