{"title":"Educational Data Mining: Analysis of Drop out of Engineering Majors at the UnB - Brazil","authors":"R. Silveira, M. Holanda, M. Victorino, M. Ladeira","doi":"10.1109/ICMLA.2019.00048","DOIUrl":null,"url":null,"abstract":"This paper presents an analysis of data about the drop out of undergraduate engineering students at the University of Brasilia(UnB), Brazil. In Brazil, similar to other countries, there is a representative amount of engineering students that enroll in engineering majors, however, they don't get to graduate in those majors. Information about the reason for that phenomenon is important for action on the matter by university decisionmakers. This paper aims to answer the research question: What are the main factors that motivate engineering students to drop out of engineering majors at UnB? We have collected the social and performance data of engineering students from 2009 to 2019. Some of the data can be considered rare in similar studies, like students' distance from home to campus and factors like students' leave of absence requests rather than performance factors. We used three data mining techniques: Generalized Linear Model (GLM), Boosting algorithm (GBM) and Random Forest(RF). The results of the study showed that international students deserve some attention from the university and courses like Physics 1 can be challenging for engineering students.","PeriodicalId":436714,"journal":{"name":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","volume":"2010 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2019.00048","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
This paper presents an analysis of data about the drop out of undergraduate engineering students at the University of Brasilia(UnB), Brazil. In Brazil, similar to other countries, there is a representative amount of engineering students that enroll in engineering majors, however, they don't get to graduate in those majors. Information about the reason for that phenomenon is important for action on the matter by university decisionmakers. This paper aims to answer the research question: What are the main factors that motivate engineering students to drop out of engineering majors at UnB? We have collected the social and performance data of engineering students from 2009 to 2019. Some of the data can be considered rare in similar studies, like students' distance from home to campus and factors like students' leave of absence requests rather than performance factors. We used three data mining techniques: Generalized Linear Model (GLM), Boosting algorithm (GBM) and Random Forest(RF). The results of the study showed that international students deserve some attention from the university and courses like Physics 1 can be challenging for engineering students.