Similarity Measure For Course Efficiency Estimation based on the Wechat Platform

DOI

10.2991/lemcs-15.2015.363How to use a DOI?

Keywords

Description length; Data Mining; K-means; Course efficiency estimation

Abstract

The modern instruction system based on Wechat is widely used to help improving the course instructing efficiency. By using the interact characters of this education system, students can finish their course by themselves. However, how to estimate the course efficiency become more difficult. The clustering analysis is one efficient method to tackle this difficulty. In this paper, the design of the instruction system based on wechat is discussed simply firstly, and then the increment of the description length is proposed to instead the relative entropy as the similarity measure between two probability distributions. Its features are also discussed in detail. As the improvement, the increment of description length satisfies the symmetrical feature. On the basis of this similarity measure, K-means algorithm is employed to analysis the corresponding data from our wechat platform and to influence the corresponding course efficiency estimation. The experiment results indicate that the proposed similarity measure can lead to better clustering results than some other previous similarity measure.

Copyright

© 2015, 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  - Chunfen Bu
AU  - Min Chen
AU  - Aijiao Liu
AU  - Qin Zhao
PY  - 2015/07
DA  - 2015/07
TI  - Similarity Measure For Course Efficiency Estimation based on the Wechat Platform
BT  - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science
PB  - Atlantis Press
SP  - 1783
EP  - 1786
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-15.2015.363
DO  - 10.2991/lemcs-15.2015.363
ID  - Bu2015/07
ER  -