Educational Data Mining: Analysis of Drop out of Engineering Majors at the UnB - Brazil

R. Silveira, M. Holanda, M. Victorino, M. Ladeira
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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.
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教育数据挖掘:巴西UnB大学工程专业学生辍学分析
本文对巴西巴西利亚大学(UnB)本科工程专业学生的退学数据进行了分析。在巴西,和其他国家一样,有一定数量的工程专业学生注册了工程专业,然而,他们并没有从这些专业毕业。关于这一现象的原因的信息对于大学决策者在这个问题上采取行动是很重要的。本文旨在回答一个研究问题:什么是促使UnB工程专业的学生退出的主要因素?我们收集了2009年至2019年工科学生的社会和绩效数据。有些数据在类似的研究中可以被认为是罕见的,比如学生家到学校的距离,以及学生请假等因素,而不是表现因素。我们使用了三种数据挖掘技术:广义线性模型(GLM)、增强算法(GBM)和随机森林(RF)。研究结果表明,国际学生应该得到大学的重视,而像物理1这样的课程对工科学生来说可能很有挑战性。
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