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On-Line Statistical Outlier Detection of irregular learning processes for e-learning
PROCEEDINGS

, Nagaoka University of Technology, Japan

EdMedia + Innovate Learning, in Honolulu, Hawaii, USA ISBN 978-1-880094-48-8 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC

Abstract

Recently, distance education by using e-Learning has been popular in actual educational situations. However, there is a problem that the instruction strategy tends to be one way, and so it sometimes makes the learners bored comparing with usual instruction methods. This paper proposes a method of on-line outlier detection of learners f irregular learning processes by using the learners f response time data for the e-Learning contents. The unique features of this method are as follows: 1.It proposes an outlier detection method by using Bayesian predictive distribution. 2. It is available for small sample, 3.It is convenient to calculate the predictive distribution. 4.On-line learning is realized on WWW. 5.It assists two ways instruction by using data mining results for the learners f learning processes. The system was utilized for actual classes. The results show the efficiency of the system.

Citation

Ueno, M. (2003). On-Line Statistical Outlier Detection of irregular learning processes for e-learning. In D. Lassner & C. McNaught (Eds.), Proceedings of ED-MEDIA 2003--World Conference on Educational Multimedia, Hypermedia & Telecommunications (pp. 227-234). Honolulu, Hawaii, USA: Association for the Advancement of Computing in Education (AACE). Retrieved October 29, 2020 from .

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