Show simple item record

dc.contributor.authorCecenardo-Galiano, Carlos
dc.contributor.authorSumaran-Pedraza, Carolina
dc.contributor.authorObregon-Palomino, Luz
dc.contributor.authorIparraguirre-Villanueva, Orlando
dc.contributor.authorCabanillas-Carbonell, Michael
dc.date.accessioned2024-05-22T18:02:05Z
dc.date.available2024-05-22T18:02:05Z
dc.date.issued2023
dc.identifier.urihttps://hdl.handle.net/20.500.13067/3163
dc.description.abstractAcademic achievement (AP) in recent years has shown minimal progress with a difference of 0.05%, according to the report made by the Program for International Student Assessment (PISA). For this reason, the objective of this research is to build a predictive multiclass classification model for the AP of students in an elementary school. It was conducted with a dataset of 218 third-year high school students. The Cross Industry Standard Process for Data Mining (CRISP-DM) methodology was used to create the model, which consists of 6 phases and is effective in data mining (DM) projects. The random forest (RF) algorithm was also used. The results indicated that the RF model obtained the highest prediction rates compared to other studies, with an accuracy of 95% of the model, respectively. Finally, it is observed that the attributes that mostly influence prediction are the scores of Ability 02 end of I bimester, Positive Impression, Ability 01 end of I bimester, Ability 03 end of I bimester, and Adaptability. Thus, it is concluded that academic attributes are more relevant than psychological attributes in predicting RF.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherSpringer Linkes_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/es_PE
dc.subjectAcademic achievementes_PE
dc.subjectElementary schooles_PE
dc.subjectData mininges_PE
dc.titlePredictive Model with Machine Learning for Academic Performancees_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.journalProceedings of Eighth International Congress on Information and Communication Technologyes_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.02.04es_PE
dc.relation.urlhttps://doi.org/10.1007/978-981-99-3043-2_81es_PE
dc.source.volume695es_PE
dc.source.beginpage975es_PE
dc.source.endpage988es_PE


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

info:eu-repo/semantics/restrictedAccess
Except where otherwise noted, this item's license is described as info:eu-repo/semantics/restrictedAccess