Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American Context
The prediction of university dropout is a complex problem, given the number and diversity of variables involved. Therefore, different strategies are applied to understand this educational phenomenon, although the most outstanding derive from the joint application of statistical approaches and comput...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Education Sciences |
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| Online Access: | https://www.mdpi.com/2227-7102/13/2/154 |
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| author | Daniel A. Gutierrez-Pachas Germain Garcia-Zanabria Ernesto Cuadros-Vargas Guillermo Camara-Chavez Erick Gomez-Nieto |
| author_facet | Daniel A. Gutierrez-Pachas Germain Garcia-Zanabria Ernesto Cuadros-Vargas Guillermo Camara-Chavez Erick Gomez-Nieto |
| author_sort | Daniel A. Gutierrez-Pachas |
| collection | DOAJ |
| description | The prediction of university dropout is a complex problem, given the number and diversity of variables involved. Therefore, different strategies are applied to understand this educational phenomenon, although the most outstanding derive from the joint application of statistical approaches and computational techniques based on machine learning. Student Dropout Prediction (SDP) is a challenging problem that can be addressed following various strategies. On the one hand, machine learning approaches formulate it as a classification task whose objective is to compute the probability of belonging to a class based on a specific feature vector that will help us to predict who will drop out. Alternatively, survival analysis techniques are applied in a time-varying context to predict when abandonment will occur. This work considered analytical mechanisms for supporting the decision-making process on higher education dropout. We evaluated different computational methods from both approaches for predicting who and when the dropout occurs and sought those with the most-consistent results. Moreover, our research employed a longitudinal dataset including demographic, socioeconomic, and academic information from six academic departments of a Latin American university over thirteen years. Finally, this study carried out an in-depth analysis, discusses how such variables influence estimating the level of risk of dropping out, and questions whether it occurs at the same magnitude or not according to the academic department, gender, socioeconomic group, and other variables. |
| first_indexed | 2024-03-11T08:55:31Z |
| format | Article |
| id | doaj.art-f9e9cfd09a8c41fa852a5f5f627b6ea4 |
| institution | Directory Open Access Journal |
| issn | 2227-7102 |
| language | English |
| last_indexed | 2024-03-11T08:55:31Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Education Sciences |
| spelling | doaj.art-f9e9cfd09a8c41fa852a5f5f627b6ea42023-11-16T20:08:41ZengMDPI AGEducation Sciences2227-71022023-02-0113215410.3390/educsci13020154Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American ContextDaniel A. Gutierrez-Pachas0Germain Garcia-Zanabria1Ernesto Cuadros-Vargas2Guillermo Camara-Chavez3Erick Gomez-Nieto4Department of Computer Science, Universidad Católica San Pablo, Arequipa 04001, PeruDepartment of Computer Science, Universidad Católica San Pablo, Arequipa 04001, PeruDepartment of Computer Science, Universidad Católica San Pablo, Arequipa 04001, PeruDepartment of Computer Science, Universidad Católica San Pablo, Arequipa 04001, PeruDepartment of Computer Science, Universidad Católica San Pablo, Arequipa 04001, PeruThe prediction of university dropout is a complex problem, given the number and diversity of variables involved. Therefore, different strategies are applied to understand this educational phenomenon, although the most outstanding derive from the joint application of statistical approaches and computational techniques based on machine learning. Student Dropout Prediction (SDP) is a challenging problem that can be addressed following various strategies. On the one hand, machine learning approaches formulate it as a classification task whose objective is to compute the probability of belonging to a class based on a specific feature vector that will help us to predict who will drop out. Alternatively, survival analysis techniques are applied in a time-varying context to predict when abandonment will occur. This work considered analytical mechanisms for supporting the decision-making process on higher education dropout. We evaluated different computational methods from both approaches for predicting who and when the dropout occurs and sought those with the most-consistent results. Moreover, our research employed a longitudinal dataset including demographic, socioeconomic, and academic information from six academic departments of a Latin American university over thirteen years. Finally, this study carried out an in-depth analysis, discusses how such variables influence estimating the level of risk of dropping out, and questions whether it occurs at the same magnitude or not according to the academic department, gender, socioeconomic group, and other variables.https://www.mdpi.com/2227-7102/13/2/154student dropout predictionmachine learning modelssurvival analysis |
| spellingShingle | Daniel A. Gutierrez-Pachas Germain Garcia-Zanabria Ernesto Cuadros-Vargas Guillermo Camara-Chavez Erick Gomez-Nieto Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American Context Education Sciences student dropout prediction machine learning models survival analysis |
| title | Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American Context |
| title_full | Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American Context |
| title_fullStr | Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American Context |
| title_full_unstemmed | Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American Context |
| title_short | Supporting Decision-Making Process on Higher Education Dropout by Analyzing Academic, Socioeconomic, and Equity Factors through Machine Learning and Survival Analysis Methods in the Latin American Context |
| title_sort | supporting decision making process on higher education dropout by analyzing academic socioeconomic and equity factors through machine learning and survival analysis methods in the latin american context |
| topic | student dropout prediction machine learning models survival analysis |
| url | https://www.mdpi.com/2227-7102/13/2/154 |
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