Mining the Change of Fuzzy Quantitative Association Rules for Summative Assessment

DOI

10.2991/icaicte.2013.36How to use a DOI?

Keywords

data mining, change mining, fuzzy association rules

Abstract

A learning management system (LSM) prevails and it accumulates an amount of data about the progress of student learning, demographic and students’ background over different sessions. Educators are very concerned about the shifts of unknown relationships among a thousand variables about students in a LMS for adjusting their teaching strategies and pedagogies. However, educators are not satisfied with the traditional reports, which are explored with limited relative variables on the issue of summative assessment and learning achievement in static sessions. The information about the changes of un-known relationships among many varia-bles cannot be produced with statistical methods in traditional reports. Our study proposes a mining change of fuzzy quan-titative association rules model to reveal the information. This model can discover the six types of changes rules from un-known relationships among many varia-bles with nominal or numerical attributes. Experiments are carried out to evaluate the proposed model. We empirical demonstrate how the model helps educa-tors understand the changing characteris-tics of students and to modify their teaching practices.

Copyright

© 2013, 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  - Chih-Hong Huang
AU  - Tony Cheng-Kui Huang
AU  - Shih-Sheng Chen
PY  - 2013/08
DA  - 2013/08
TI  - Mining the Change of Fuzzy Quantitative Association Rules for Summative Assessment
BT  - Proceedings of the 2013 International Conference on Advanced ICT and Education
PB  - Atlantis Press
SP  - 169
EP  - 173
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaicte.2013.36
DO  - 10.2991/icaicte.2013.36
ID  - Huang2013/08
ER  -