Bayesian Model for Optimization Adaptive  E-Learning Process

dc.creator	F. J. Tapia
dc.creator	C. A. Lopez
dc.creator	M. J. Galan
dc.creator	E. Rubio
dc.date	2008-06-01T00:00:00Z
dc.date.accessioned	2015-07-20T22:08:07Z
dc.date.available	2015-07-20T22:08:07Z
dc.identifier	1863-0383
dc.identifier	https://doaj.org/article/f116172c0ecd4f2b8e369aa2783f1396
dc.identifier.uri	http://evidence.thinkportal.org/handle/123456789/11760
dc.description	In this paper, a Bayesian-Network-based model isproposed to optimize the Global Adaptive e-LearningProcess (GAeLP). This model determines the type ofpersonalization required for a learner according to his orher real needs, in which we have considered both objectsand objectives of personalization. Furthermore, cause-andeffectrelations among these objects and objectives with thelearning phases, the learner, and the Intelligent TutorialSystem (ITS) are accomplished. These cause-and-effectrelations were coded into a Bayesian Network (BN), suchthat it involves the entire GAeLP. Four fundamental phasesthat have a direct effect in the learner’s learning process areconsidered: Learner’s previous knowledge Phase, Learner’sProgress Knowledge Phase, Learner’s /Teacher’s Aims andGoals Phase, and Navigation Preferences and ExperiencesPhase. The efficacy of the Bayesian networks is proventhrough the first phase, in which learners of differentknowledge area were select. The main results in this workare: causal relations among objects and objectives ofpersonalization, knowledge phases, learner and electronicsystem. Personalization profiles set and their probabilities inthe first phase were obtained to diagnose the type ofpersonalization of the learner.
dc.language	English
dc.publisher	International Association of Online Engineering (IAOE)
dc.relation	http://online-journals.org/i-jet/article/view/215/437
dc.relation	https://doaj.org/toc/1863-0383
dc.rights	CC BY
dc.source	International Journal of Emerging Technologies in Learning (iJET), Vol 3, Iss 2, Pp 38-52 (2008)
dc.subject	Bayesian networks
dc.subject	e-Learning
dc.subject	Learning metrics.
dc.subject	Technology (General)
dc.subject	T1-995
dc.subject	Technology
dc.subject	T
dc.subject	DOAJ:Technology (General)
dc.subject	DOAJ:Technology and Engineering
dc.subject	Theory and practice of education
dc.subject	LB5-3640
dc.subject	Education
dc.subject	L
dc.subject	DOAJ:Education
dc.subject	DOAJ:Social Sciences
dc.subject	Technology (General)
dc.subject	T1-995
dc.subject	Technology
dc.subject	T
dc.subject	DOAJ:Technology (General)
dc.subject	DOAJ:Technology and Engineering
dc.subject	Theory and practice of education
dc.subject	LB5-3640
dc.subject	Education
dc.subject	L
dc.subject	DOAJ:Education
dc.subject	DOAJ:Social Sciences
dc.subject	Technology (General)
dc.subject	T1-995
dc.subject	Technology
dc.subject	T
dc.subject	Theory and practice of education
dc.subject	LB5-3640
dc.subject	Education
dc.subject	L
dc.subject	Technology (General)
dc.subject	T1-995
dc.subject	Technology
dc.subject	T
dc.subject	Theory and practice of education
dc.subject	LB5-3640
dc.subject	Education
dc.subject	L
dc.subject	Technology (General)
dc.subject	T1-995
dc.subject	Technology
dc.subject	T
dc.subject	Theory and practice of education
dc.subject	LB5-3640
dc.subject	Education
dc.subject	L
dc.title	Bayesian Model for Optimization Adaptive E-Learning Process
dc.type	article