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COMPARATIVE ANALYSIS OF PREDICTION TECHNIQUES TO DETERMINE STUDENT DROPOUT: LOGISTIC REGRESSION VS DECISION TRESS

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dc.creatorLUIS ALFREDO PÉREZ AGUILERA
dc.creatorMÓNICA ALEJANDRA CANIUPÁN MARILEO
dc.creatorELIZABETH ELIANA GRANDÓN TOLEDO
dc.creatorGILDA ELENA VARGAS MAC-CARTE
dc.date2018
dc.date.accessioned2025-01-10T15:08:11Z
dc.date.available2025-01-10T15:08:11Z
dc.date.issued2018
dc.description.abstractCURRENTLY, THE DETECTION OF STUDENTS WHO MAY DROP OUT FROM AN ACADEMIC PROGRAM IS A RELEVANT ISSUE FOR UNIVERSITIES, SO THERE ARE EFFORTS TO EXAMINE THE VARIABLES THAT DETERMINE STUDENTS DROP OUT. DROP OUT IS DEFINED IN DIFFERENT WAYS, HOWEVER, ALL THE STUDIES CONVERGE IN THAT FOR A STUDENT TO DROP OUT A COURSE OF STUDY, SOME VARIABLES MUST BE COMBINED. THIS STUDY PRESENTS A COMPARISON OF PERFORMANCE INDICATORS OF THE CURRENT DROP OUT MODEL OF THE UNIVERSIDAD DEL BÍO-BÍO (UBB), WHICH IS BASED ON LOGISTIC REGRESSION TECHNIQUE AND IT IS COMPARED WITH A NEW MODEL BASED ON DECISION TREES. THE NEW MODEL IS OBTAINED THROUGH DATA MINING METHODOLOGIES AND IT WAS IMPLEMENTED THROUGH THE SAP PREDICTIVE ANALYTICS TOOL. TO TRAIN, VALIDATE, AND APPLY THE MODEL, REAL DATA FROM THE UBB DATABASES WERE USED. THE COMPARISON SHOWS THAT THE PREDICTION OF STUDENT´ DROP OUT OF THE PROPOSED MODEL OBTAINS AN ACCURACY OF 86%, A PRECISION OF 97% WITH AN ERROR RATE OF 14%, BETTER INDICATORS THAN THE CURRENT VALUES DELIVERED BY THE MODEL BASED ON LOGISTIC REGRESSION. SUBSEQUENTLY, THE PREDICTION MODEL OBTAINED WAS OPTIMIZED CONSIDERING OTHER VARIABLES, IMPROVING EVEN MORE THE PREDICTION INDICATORS. HIGHER EDUCATION INSTITUTIONS SHOULD TAKE INTO ACCOUNT THE VARIABLES THAT EXPLAIN THE MOST THE PHENOMENON OF STUDENT S DROP OUT TO IMPROVE THE RETENTION OF THEIR STUDENTS.
dc.formatapplication/pdf
dc.identifier.doi10.1109/SCCC.2018.8705262
dc.identifier.issn1522-4902
dc.identifier.urihttps://repositorio.ubiobio.cl/handle/123456789/10590
dc.languagespa
dc.publisher37TH INTERNATIONAL CONFERENCE OF THE CHILEAN COMPUTER SCIENCE SOCIETY, SCCC 2018
dc.relation.uri10.1109/SCCC.2018.8705262
dc.rightsPUBLICADA
dc.subjectStudent Dropout
dc.subjectSAP
dc.subjectPredictive Analytics
dc.subjectLogistic Regression
dc.subjectDecision Trees
dc.subjectData Mining
dc.titleCOMPARATIVE ANALYSIS OF PREDICTION TECHNIQUES TO DETERMINE STUDENT DROPOUT: LOGISTIC REGRESSION VS DECISION TRESS
dc.title.alternativeANÁLISIS COMPARATIVO DE TÉCNICAS DE PREDICCIÓN PARA DETERMINAR LA DESERCIÓN ESCOLAR: REGRESIÓN LOGÍSTICA VS ÁRBOLES DE DECISIÓN
dc.typeACTA DE CONFERENCIA
dspace.entity.typePublication
ubb.EstadoPUBLICADA
ubb.Otra ReparticionDEPARTAMENTO DE SISTEMAS DE INFORMACION
ubb.Otra ReparticionDEPARTAMENTO DE SISTEMAS DE INFORMACION
ubb.Otra ReparticionDEPARTAMENTO DE SISTEMAS DE INFORMACION
ubb.Otra ReparticionDEPARTAMENTO DE ESTADISTICA
ubb.SedeCONCEPCIÓN
ubb.SedeCONCEPCIÓN
ubb.SedeCONCEPCIÓN
ubb.SedeCONCEPCIÓN
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