2024 ASEE Annual Conference & Exposition

Analyzing Attrition: Predictive Model of Dropout Causes among Engineering Students

Presented at First-Year Programs Division Technical Session 7: Retention & Success

This Complete Research develops a predictive model to elucidate factors affecting dropout rates in the first two years of tertiary education, using data from 1266 students at a School of Engineering in Chile. Focusing on socio-demographic variables from an institutional survey, such as family background, economic status, and employment, the study employs a quantitative, non-experimental methodology alongside Machine Learning techniques within a Knowledge Discovery in Databases (KDD) framework. Of the methods tested, including Neural Networks (NN), K-Nearest Neighbor (KNN), Naive Bayes (NB), Decision Tree (DT), and Logistic Regression (LR), the NN model proved most effective, demonstrating high Accuracy, Sensitivity, and Specificity (all above 0.7). A Weight-Based Feature Importance analysis identified economic factors, family composition, and social relationships as the top variables impacting dropout. This research enhances our understanding of factors influencing student attrition in the School of Engineering. The model acts as an early alert system, identifying potential dropouts and at-risk student groups before they commence their studies. Consequently, it allows the implementation of early interventions, such as financial support, improved study methods, and professional counseling, thereby significantly reducing dropout rates and improving student success.

Authors

Ms. Cristian Saavedra-Acuna Universidad Andres Bello, Concepcion, Chile [biography]

Cristian Saavedra is an assistant professor at the School of Engineering at the University Andres Bello
in Concepcion, Chile. He holds a bachelor’s degree in Electronics Engineering and a master’s degree in Technological Innovation and Entrepreneurship. Cristian is certiﬁed in Industrial Engineering, University
Teaching, Online Hybrid and Blended Education, and Entrepreneurship Educators. He teaches industrial
engineering students and carries out academic management activities. His main research interest areas are Innovation, entrepreneurship, engineering education, gender perspective studies in STEM education, and
data analysis and visualization.
Dr. Monica Quezada-Espinoza http://orcid.org/0000-0002-0383-0179 Universidad Andres Bello, Santiago, Chile [biography]

Monica Quezada-Espinoza is a professor and researcher at the School of Engineering at Universidad Andrés Bello in Santiago, Chile, where she collaborates with the Educational and Academic Innovation Unit (UNIDA) as an instructor in active learning methodologies and mentors engineering faculty in educational research. She is the Secretary of the Women in Engineering Division (WIED) of the American Society for Engineering Education (ASEE) and an associate researcher in the STEM Latin America Network, specifically in the STEM + Gender group. Her research interests are diverse and focus on university education in STEM fields, faculty and professional development, research-based methodologies, and the use of evaluation tools and technology for education. She is also passionate about investigating conceptual learning in abstract physics topics, developing strategies to improve the retention of first-year engineering students, and enhancing skills and competencies in higher education. Additionally, Monica is dedicated to exploring gender issues in STEM education, with a particular emphasis on studying and proposing improvements for the inclusion of women in highly male-dominated fields. For more information on her work, visit her ORCID profile.
Ms. Danilo Alberto Gomez Correa Universidad Andres Bello, Concepcion, Chile [biography]

Danilo Gómez is an assistant professor at the School of Engineering at the Andrés Bello University in Concepción, Chile. He has a Master's degree in applied statistics and Industrial engineering. In addition, Danilo has certifications in data science, machine learning, and big data.
In his role as a teacher, Danilo specializes in teaching industrial engineering students and carries out academic management activities. His main research areas can be reviewed at: https://orcid.org/0000-0002-8735-7832

Download paper (2.16 MB)

Are you a researcher? Would you like to cite this paper? Visit the ASEE document repository at peer.asee.org for more tools and easy citations.

» Download paper

« View session

For those interested in:

1st Generation
engineering
Faculty
gender
New Members
professional
Socio-Economic Status
undergraduate