NDLI: On the application of data mining technique and genetic algorithm to an automatic course scheduling system

Content Provider	IEEE Xplore Digital Library
Author	Yao-Te Wang Yu-Hsin Cheng Ting-Cheng Chang Jen, S.M.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Inf. Manage., Providence Univ., Taichung (Yao-Te Wang) \|\| Coll. of Inf. Sci., Ling Tung Univ., Taichung (Ting-Cheng Chang) \|\| Dept. of Inf. Data Center, Ling Tung Univ., Taichung (Jen, S.M.) \|\| Dept. of Inf. Manage., Ling Tung Univ., Taichung (Yu-Hsin Cheng)
Abstract	In this study, a data mining technique and a genetic algorithm are applied to an automatic course scheduling system to produce course timetables that best suit studentspsila and teacherspsila needs. Course scheduling in colleges and universities is a highly complicated task for satisfying multiple constraints. Conventional course scheduling is done mainly from the schoolpsilas point of view, such that courses and timetables are planned according to the characteristics of individual departments and institutes, with little attention to studentspsila interests and their needs in career development. This study develops a practical automatic course scheduling system based on studentspsila needs, wherein the course scheduling process is divided into two stages. In the first stage, studentspsila needs in course selection are considered and an association among courses selected by students is mined using the association mining technique; while in the second stage, the genetic algorithm is used to arrange the course timetable. More particularly, this study is based on studentspsila willingness in course selection, analyzes the effects of course arrangement in different class periods on studentspsila learning performance, takes into account teacherspsila preferred schedules, determines the cost function value of each class period, and then applies the genetic algorithm for class period exchange, so as to produce an optimal course timetable. It is shown in the experiment results that the automatic course scheduling system proposed in this study not only can efficiently replace the onerous operation of conventional manual course scheduling, but also produce course timetables that truly fulfill userspsila needs and increase studentspsila and teacherspsila satisfaction, thereby providing a win-win-win solution for students, teachers and the school.
Starting Page	400
Ending Page	405
File Size	527352
Page Count	6
File Format	PDF
ISBN	9781424416738
DOI	10.1109/ICCIS.2008.4670852
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Genetic algorithms Educational institutions Cities and towns Information management Application software Processor scheduling Computer science Finance Information science course timetable data mining association mining genetic algorithm automatic course scheduling
Content Type	Text
Resource Type	Article

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