dc.contributor.author | Pires, Jorge Manuel | |
dc.contributor.author | Cota, Manuel Pérez | |
dc.date.accessioned | 2018-07-09T05:49:51Z | |
dc.date.available | 2018-07-09T05:49:51Z | |
dc.date.issued | 2014 | |
dc.identifier.issn | 2210-142X | |
dc.identifier.uri | https://journal.uob.edu.bh:443/handle/123456789/259 | |
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 | http://creativecommons.org/licenses/by-nc-sa/4.0/ | * |
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.identifier.doi | http://dx.doi.org/10.12785/IJCDS/030303 | |
dc.volume | 03 | |
dc.issue | 03 | |
dc.source.title | International Journal of Computing and Digital Systems | |
dc.abbreviatedsourcetitle | IJCDS |
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