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dc.creatorAmelec, Viloria
dc.creatorGaitan Angulo, Mercedes
dc.creatorJ. Kamatkar, Sadhana
dc.creatorDe la Hoz Hernández, Juan David
dc.creatorGarcía Guiliany, Jesús Enrique
dc.creatorRedondo Bilbao, Osman Enrique
dc.creatorHernandez-P, Hugo
dc.date.accessioned2020-01-30T13:49:42Z
dc.date.available2020-01-30T13:49:42Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/11323/5963
dc.description.abstractThis paper describes the use of Data Mining Techniques to improve teaching–learning processes in the linear programming course offered at the Engineering Faculty at Mumbai University, India. The proposed approach seeks to model the student’s interaction with the study material using prediction rules whose interpretation will allow to detect the weaknesses of the educational process and evaluate the quality of the study material. The proposed rule discovery method is the Evolutionary Algorithms and particularly the Grammar-Based Genetic Programming (GB-GP), which is compared to association rules and decision tree construction for discovering prediction rules.spa
dc.language.isoengspa
dc.publisherUniversidad de la Costaspa
dc.rightsinfo:eu-repo/semantics/closedAccessspa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectData mining techniquesspa
dc.subjectE-learningspa
dc.subjectEvolutionary algorithmsspa
dc.subjectGrammar-based genetic programming (GB-GP)spa
dc.titlePrediction rules in e-learning systems using genetic programmingspa
dc.typePreprintspa
dc.type.hasVersioninfo:eu-repo/semantics/draftspa


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