Course planning of extension education to meet market demand by using data mining techniques - an example of Chinkuo technology university in Taiwan

Authors:
Tai-Chang Hsia;An-Jin Shie;Li-Chen Chen
Affiliations:
Department of Industrial Engineering and Management, Chienkuo Technology University, Changhua, Taiwan, ROC;Department of Industrial Engineering and Management Yuan Ze University, Taoyuan, Taiwan, ROC;Extension Education Center Chienkuo Technology University Changhua, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

Citing 2
Cited 5

Data mining for customer service support

Information and Management
Building Data Mining Applications for CRM

Building Data Mining Applications for CRM

Data mining to improve industrial standards and enhance production and marketing: An empirical study in apparel industry

Expert Systems with Applications: An International Journal
Educational data mining: a review of the state of the art

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A customer-oriented organisational diagnostic model based on data mining of customer-complaint databases

Expert Systems with Applications: An International Journal
Use of machine learning techniques for educational proposes: a decision support system for forecasting students' grades

Artificial Intelligence Review
Review: Data mining techniques and applications - A decade review from 2000 to 2011

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.06

Visualization

Abstract

This study used data mining techniques to analyze the course preferences and course completion rates of enrollees in extension education courses at a university in Taiwan. First, extension courses were classified into five broad groups. Records of enrollees in extension courses from 2000-5 were then analyzed by three data mining algorithms: Decision Tree, Link Analysis, and Decision Forest. Decision tree was used to find enrollee course preferences, Link Analysis found the correlation between course category and enrollee profession, and Decision Forest found the probability of enrollees completing preferred courses. Results will be used as a reference for curriculum development in the extension program.