Trends in information models on retention-university dropout
Abstract:
The generation of data in educational environments must be used to extract relevant and timely information that improves the quality and productivity of academic institutions. This research aims to analyze current studies on student retention and / or dropout in order to determine the variables that affect them and trends in data analysis models. The guidelines of Roy Hubara and Sturm (2019) were followed to review articles from four databases, classifying them according to the approach used. The results obtained indicate that the variables that affect student retention are related to cognitive, social and organizational factors and that the tendency is to develop pbkp_redictive-prescriptive models for the study of these concepts. Finally, it is proposed to develop pbkp_redictive models based on statistics and learning models to improve student retention and dropout rates.
Año de publicación:
2020
Keywords:
- Student retention
- data analytics
- Artificial Intelligence
- Student dropout
Fuente:
Tipo de documento:
Article
Estado:
Acceso restringido
Áreas de conocimiento:
- Educación superior
- Tecnología educativa
Áreas temáticas:
- Funcionamiento de bibliotecas y archivos