Modeling Engineering Persistence through Expectancy Value Theory and Machine Learning Techniques
Xiaomei Wang, Arinan De Piemonte Dourado, Pamela Bilo Thomas, Campbell Rightmyer Bego · 2022 IEEE Frontiers in Education Conference (FIE) · 2022
This Research to Practice Full Paper presents an investigation of engineering retention using machine learning models. We use random forests and artificial neural networks in the form of multilayer perceptrons to analyze the interaction between different factors, such as demographic information, standardized test scores, first semester grades, and surveys to predict student retention in engineering. We find that obtained models can predict with good accuracy if students will remain in engineering, with F1 scores of at least 75 percent. We find that each model places different levels of importance on distinct factors.