Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Identifying factors that affect academic dropout and retention is a research area that brings a plurality of opinions and concepts. This article identifies current primary studies to understand the main factors related to dropout and retention. It is quantitative, exploratory, and explanatory research of an applied nature, using the technical procedures of case study and bibliographic research. The systematic review of the literature identifies the factors that impact academic dropout and retention and serves as a basis for a machine learning project. Academic, demographic, and learning factors can predict dropouts and retention. The definition of the factors used and the way of use is essential to obtain good forecasting results. The identified factors were used in the institution.
Recommended Citation
Silva, Edmilson Cosme; Freitas, Sergio; Soares Ramos, Cristiane; Muniz De Menezes, Amanda Emilly; and Rodrigues De Araujo, Leticia Karla Soares, "A Systematic Review of the Factors that Impact the Prediction of Retention and Dropout in Higher Education" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 2.
https://aisel.aisnet.org/hicss-56/da/learning_analytics/2
A Systematic Review of the Factors that Impact the Prediction of Retention and Dropout in Higher Education
Online
Identifying factors that affect academic dropout and retention is a research area that brings a plurality of opinions and concepts. This article identifies current primary studies to understand the main factors related to dropout and retention. It is quantitative, exploratory, and explanatory research of an applied nature, using the technical procedures of case study and bibliographic research. The systematic review of the literature identifies the factors that impact academic dropout and retention and serves as a basis for a machine learning project. Academic, demographic, and learning factors can predict dropouts and retention. The definition of the factors used and the way of use is essential to obtain good forecasting results. The identified factors were used in the institution.
https://aisel.aisnet.org/hicss-56/da/learning_analytics/2