Deriving Prior Distributions for Bayesian Models Used to Achieve Adaptive E-Learning

dc.creator	Sanghyun S. Jeon
dc.creator	Stanley Y. W. Su (IEEE Fellow)
dc.date	2011-06-01T00:00:00Z
dc.date.accessioned	2015-07-20T22:08:56Z
dc.date.available	2015-07-20T22:08:56Z
dc.identifier	2073-7904
dc.identifier	https://doaj.org/article/ed25cd008915416bb057f3146a7e8b50
dc.identifier.uri	http://evidence.thinkportal.org/handle/123456789/12389
dc.description	This paper presents an approach of achieving adaptive e-learning by probabilistically evaluating a learner based not only on the profile and performance data of the learner but also on the data of previous learners. In this approach, an adaptation rule specification language and a user interface tool are provided to a content author or instructor to define adaptation rules. The defined rules are activated at different stages of processing the learning activities of an activity tree which models a composite learning object. System facilities are also provided for modeling the correlations among data conditions specified in adaptation rules using Bayesian Networks. Bayesian inference requires a prior distribution of a Bayesian model. This prior distribution is automatically derived by using the formulas presented in this paper together with prior probabilities and weights assigned by the content author or instructor. Each new learner’s profile and performance data are used to update the prior distribution, which is then used to evaluate the next new learner. The system thus continues to improve the accuracy of learner evaluation as well as its adaptive capability. This approach enables an e-learning system to make proper adaptation decisions even though a learner’s profile and performance data may be incomplete, inaccurate and/or contradictory.
dc.language	English
dc.publisher	Hong Kong Bao Long Accounting & Secretarial Limited
dc.relation	http://www.kmel-journal.org/ojs/index.php/online-publication/article/view/111/91
dc.relation	https://doaj.org/toc/2073-7904
dc.rights	CC BY
dc.source	Knowledge Management & E-Learning : an International Journal, Vol 3, Iss 2, Pp 251-270 (2011)
dc.subject	Adaptive e-Learning
dc.subject	Bayesian Model
dc.subject	Data Uncertainty
dc.subject	Prior Distribution
dc.subject	Group Profile and Performance Data
dc.subject	Education (General)
dc.subject	L7-991
dc.subject	Education
dc.subject	L
dc.subject	DOAJ:Education
dc.subject	DOAJ:Social Sciences
dc.subject	Education (General)
dc.subject	L7-991
dc.subject	Education
dc.subject	L
dc.subject	DOAJ:Education
dc.subject	DOAJ:Social Sciences
dc.subject	Education (General)
dc.subject	L7-991
dc.subject	Education
dc.subject	L
dc.subject	Education (General)
dc.subject	L7-991
dc.subject	Education
dc.subject	L
dc.subject	Education (General)
dc.subject	L7-991
dc.subject	Education
dc.subject	L
dc.title	Deriving Prior Distributions for Bayesian Models Used to Achieve Adaptive E-Learning
dc.type	article