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dc.contributor.authorVialardi Sacín, César
dc.contributor.authorChue Gallardo, Jorge
dc.contributor.authorPeche, Juan Pablo
dc.contributor.authorAlvarado, Gustavo
dc.contributor.authorVinatea, Bruno
dc.contributor.authorEstrella, Jhonny
dc.contributor.authorOrtigosa, Álvaro
dc.contributor.otherVialardi Sacín, César
dc.contributor.otherChue Gallardo, Jorge
dc.contributor.otherPeche, Juan Pablo
dc.contributor.otherAlvarado, Gustavo
dc.contributor.otherVinatea, Bruno
dc.contributor.otherEstrella, Jhonny
dc.date.issued2011
dc.identifier.citationVialardi-Sacín, C., Chue-Gallardo, J., Peche, J. P., Alvarado, G., Vinatea, B., Estrella, J., y Ortigosa, Á. (2011). A data mining approach to guide students through the enrollment process based on academic performance. User modeling and user-adapted interaction, 21(1-2), 217-248. doi:10.1007/s11257-011-9098-4es_PE
dc.identifier.issn0924-1868
dc.identifier.urihttps://hdl.handle.net/20.500.12724/1990
dc.description.abstractStudent academic performance at universities is crucial for education management systems. Many actions and decisions are made based on it, specifically the enrollment process. During enrollment, students have to decide which courses to sign up for. This research presents the rationale behind the design of a recommender system to support the enrollment process using the students’ academic performance record. To build this system, the CRISP-DM methodology was applied to data from students of the Computer Science Department at University of Lima, Perú. One of the main contributions of this work is the use of two synthetic attributes to improve the relevance of the recommendations made. The first attribute estimates the inherent difficulty of a given course. The second attribute, named potential, is a measure of the competence of a student for a given course based on the grades obtained in relatedcourses. Data was mined using C4.5, KNN (K-nearest neighbor), Naïve Bayes, Bagging and Boosting, and a set of experiments was developed in order to determine the best algorithm for this application domain. Results indicate that Bagging is the best method regarding predictive accuracy. Based on these results, the “Student Performance Recommender System” (SPRS) was developed, including a learning engine. SPRS was tested with a sample group of 39 students during the enrollment process. Results showed that the system had a very good performance under real-life conditions.en_EN
dc.formatapplication/html
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofurn:issn:1573-1391
dc.rightsinfo:eu-repo/semantics/openAccess*
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.sourceRepositorio Institucional Ulima
dc.sourceUniversidad de Lima
dc.subjectData miningen_EN
dc.subjectAdministración de sistemas de informaciónes_PE
dc.subject.classificationPendientees_PE
dc.titleA data mining approach to guide students through the enrollment process based on academic performanceen_EN
dc.typeinfo:eu-repo/semantics/article
dc.type.otherArtículo en Scopus
dc.identifier.journalUser Modeling and User-Adapted Interaction
dc.publisher.countryNL
dc.identifier.eissn1573-1391
dc.identifier.doihttps://doi.org/10.1007/s11257-011-9098-4
ulima.catOI
dc.identifier.isni0000000121541816
dc.identifier.scopusid2-s2.0-79955843738


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