Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University
The academic success of university students is a problem that depends in a multi-factorial way on the aspects related to the student and the career itself. A problem with this level of complexity needs to be faced with integral approaches, which involves the complement of numerical quantitative anal...
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2022
|
| Materias: | |
| Acceso en línea: | Ver en el sistema de origen Enlace permanente (DOI o Handle) Enlace permanente (DOI o Handle) |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1881761980089368576 |
|---|---|
| author | Orrego Granados, David Ugalde, Jonathan Salas, Rodrigo Torres, Romina López Gonzales, Javier Linkolk |
| author_browse | López Gonzales, Javier Linkolk Orrego Granados, David Salas, Rodrigo Torres, Romina Ugalde, Jonathan |
| author_facet | Orrego Granados, David Ugalde, Jonathan Salas, Rodrigo Torres, Romina López Gonzales, Javier Linkolk |
| author_sort | Orrego Granados, David |
| collection | Producción científica |
| description | The academic success of university students is a problem that depends in a multi-factorial way on the aspects related to the student and the career itself. A problem with this level of complexity needs to be faced with integral approaches, which involves the complement of numerical quantitative analysis with other types of analysis. This study uses a novel visual-predictive data analysis approach to obtain relevant information regarding the academic performance of students from a Peruvian university. This approach joins together domain understanding and data-visualization analysis, with the construction of machine learning models in order to provide a visual-predictive model of the students’ academic success. Specifically, a trained XGBoost Machine Learning model achieved a performance of up to 91.5% Accuracy. The results obtained alongside a visual data analysis allow us to identify the relevant variables associated with the students’ academic performances. In this study, this novel approach was found to be a valuable tool for developing and targeting policies to support students with lower academic performance or to stimulate advanced students. Moreover, we were able to give some insight into the academic situation of the different careers of the university. |
| format | Article |
| fuente_str | CRIS |
| fuente_tipo_str_mv | CRIS|Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/433 |
| institution | UPeU |
| language | English |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4332026-10-30T16:14:37ZVisual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian UniversityOrrego Granados, DavidUgalde, JonathanSalas, RodrigoTorres, RominaLópez Gonzales, Javier Linkolkstudents’ performancesmachine learninglearning analyticseducational data miningbusiness intelligence in educationThe academic success of university students is a problem that depends in a multi-factorial way on the aspects related to the student and the career itself. A problem with this level of complexity needs to be faced with integral approaches, which involves the complement of numerical quantitative analysis with other types of analysis. This study uses a novel visual-predictive data analysis approach to obtain relevant information regarding the academic performance of students from a Peruvian university. This approach joins together domain understanding and data-visualization analysis, with the construction of machine learning models in order to provide a visual-predictive model of the students’ academic success. Specifically, a trained XGBoost Machine Learning model achieved a performance of up to 91.5% Accuracy. The results obtained alongside a visual data analysis allow us to identify the relevant variables associated with the students’ academic performances. In this study, this novel approach was found to be a valuable tool for developing and targeting policies to support students with lower academic performance or to stimulate advanced students. Moreover, we were able to give some insight into the academic situation of the different careers of the university.Articlehttps://www.mdpi.com/2076-3417/12/21/11251/pdf?version=1667900869https://doi.org/10.3390/app12211125110.3390/app122111251https://hdl.handle.net/20.500.15037/433W4308475339enApplied Sciences2076-34172022-11-062026-06-13T08:13:30Z2026-06-13T08:13:30Z |
| spellingShingle | students’ performances machine learning learning analytics educational data mining business intelligence in education Orrego Granados, David Ugalde, Jonathan Salas, Rodrigo Torres, Romina López Gonzales, Javier Linkolk Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University |
| title | Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University |
| title_full | Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University |
| title_fullStr | Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University |
| title_full_unstemmed | Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University |
| title_short | Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University |
| title_sort | visual predictive data analysis approach for the academic performance of students from a peruvian university |
| topic | students’ performances machine learning learning analytics educational data mining business intelligence in education |
| topic_facet | students’ performances machine learning learning analytics educational data mining business intelligence in education |
| url | https://www.mdpi.com/2076-3417/12/21/11251/pdf?version=1667900869 https://doi.org/10.3390/app122111251 https://hdl.handle.net/20.500.15037/433 |
| work_keys_str_mv | AT orregogranadosdavid visualpredictivedataanalysisapproachfortheacademicperformanceofstudentsfromaperuvianuniversity AT ugaldejonathan visualpredictivedataanalysisapproachfortheacademicperformanceofstudentsfromaperuvianuniversity AT salasrodrigo visualpredictivedataanalysisapproachfortheacademicperformanceofstudentsfromaperuvianuniversity AT torresromina visualpredictivedataanalysisapproachfortheacademicperformanceofstudentsfromaperuvianuniversity AT lopezgonzalesjavierlinkolk visualpredictivedataanalysisapproachfortheacademicperformanceofstudentsfromaperuvianuniversity |