University of Bahrain
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Chi-Square Function Applied to Learning Objects Intelligent Learning Mechanisms

Show simple item record Pires, Jorge Manuel Cota, Manuel Pérez 2018-07-09T05:49:51Z 2018-07-09T05:49:51Z 2014
dc.identifier.issn 2210-142X
dc.description.abstract The massive data set obtained from the analysis of a particular cognitive profile requires an evaluation function versatile enough for application to a Genetic Algorithm (GA) in order to be able to make decisions that involve a high degree of reliability - in the order of 90 to 95%. The problem to be studied is whether it is possible or not to evolve in cognitive terms, through the choice of learning object [7] more suitable, which we denominate as Knowledge Block (KB) - a Sharable Content Object Reference Model (SCORM) compatible structure – see Fig. 2. The Pearson’s Chi-square test (X2) is the evaluation function selected, because of its simplicity. By observation of merely two parameters — Observed Value (Oj) en_US
dc.language.iso en_US en_US
dc.publisher University of Bahrain en_US
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International *
dc.rights.uri *
dc.subject Learning en_US
dc.subject Evaluation en_US
dc.subject Chi-square en_US
dc.subject Cognitive en_US
dc.subject Profile en_US
dc.subject Genetic Algorithm en_US
dc.title Chi-Square Function Applied to Learning Objects Intelligent Learning Mechanisms en_US
dc.type Article en_US
dc.volume 03
dc.issue 03
dc.source.title International Journal of Computing and Digital Systems
dc.abbreviatedsourcetitle IJCDS

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