Decision Trees and Gender Stereotypes in University Academic Desertion

被引:1
作者
Andrade-Zurita, Sylvia [1 ]
Armas-Arias, Sonia [1 ]
Nunez-Lopez, Rocio [1 ]
Arevalo-Peralta, Josue [1 ]
机构
[1] Univ Tecn Ambato, Fac Ciencias Humanas & Educ, Ambato, Ecuador
来源
THIRD INTERNATIONAL CONFERENCE ON IMAGE PROCESSING AND CAPSULE NETWORKS (ICIPCN 2022) | 2022年 / 514卷
关键词
Data mining; Decision trees; Artificial intelligence; Gender stereotypes; Higher education; Academic desertion;
D O I
10.1007/978-3-031-12413-6_25
中图分类号
TB8 [摄影技术];
学科分类号
0804 ;
摘要
Recently, Decision trees are referred as an essential tool for interpreting and understanding the trend in the academic desertion of male and female students. This research advancement has been utilised by the proposed study to provide a deeper understanding on the gender stereotypes of the University of Ecuador. This research work is proposed by using a quantitative technique with a descriptive, interpretative scope of the data mining findings acquired in the years 2014 and 2015 in the enrollment and gender-wise student graduation in the years 2021 and 2022. As a result of the high rate of non-graduate students, the degree of male students has a stronger tendency in most faculties, despite the fact that the number of women enrolled is 56.6%. It is verified that the utilized supervised technique based on decision trees allows identifying the Faculties of the Technical University of Ambato. It is quite positive for the education of women because 2.1% of all women obtained their third level degree in the denominations of engineering and bachelor's degrees. It is considered that Faculties such as Civil and Mechanical Engineering, as well as Systems, Electronic and Industrial Engineering should increase the number of women in their enrollment and ensure their qualifications through affirmative action policies.
引用
收藏
页码:321 / 331
页数:11
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