A Method of Cross-Level Frequent Pattern Mining for Web-Based Instruction

Autor/inn/en	Huang, Yueh-Min; Chen, Juei-Nan; Cheng, Shu-Chen
Titel	A Method of Cross-Level Frequent Pattern Mining for Web-Based Instruction
Quelle	In: Educational Technology & Society, 10 (2007) 3, S.305-319 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1436-4522
Schlagwörter	Distance Education; Web Based Instruction; Foreign Countries; Methods; Mathematics; Electronic Learning; Educational Technology; Internet; Classification; Coding; Taiwan + Suchen Sie Ihr Suchwort? Distance study; Distance learning; Fernunterricht; Web Based Training; Ausland; Method; Methode; Mathematik; Unterrichtsmedien; Classification system; Klassifikation; Klassifikationssystem; Codierung; Programmierung
Abstract	Due to the rise of e-Learning, more and more useful learning materials are open to public access. Therefore, an appropriate learning suggestion mechanism is an important tool to enable learners to work more efficiently. A smoother learning process increases the learning effect, avoiding unnecessarily difficult concepts and disorientation during learning. However, many suggestion demands come from different abstraction levels, and traditional single level frequent pattern mining is not sufficient. This paper proposes a methodology for mining frequent patterns of learners' behavior which connote a hierarchical scheme to provide cross-level learning suggestions for the next learning course. With this system, a learner can get multiple levels of abstract suggestions instead of merely single level frequent pattern mining results. Our study shows that the algorithms can mine considerable quantities of frequent patterns from real life learning data. The experimental data are collected from a Web learning system originating from National Cheng Kung University in Taiwan. The proposed methodology gives learners many suggestions to help them learn more effectively and efficiently. Finally, we collect some representative cases to realize different requirements which are extracted from a learners' access database. These cases are classified into three types; notably, type three generalized four meaningful external factors which are inferred by our observations from these cross-level frequent patterns. (Contains 6 tables and 11 figures.) (As Provided).
Anmerkungen	International Forum of Educational Technology & Society. Athabasca University, School of Computing & Information Systems, 1 University Drive, Athabasca, AB T9S 3A3, Canada. Tel: 780-675-6812; Fax: 780-675-6973; Web site: http://www.ifets.info
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10