Using Machine Learning and Mixed Effect Models to Predict Undergraduate STEM Dropout

We investigated STEM attrition by tracking 1,243 University of Miami students’ emotions, expectations, and academic goals using Ecological Momentary Assessments. Linear mixed-effects modeling revealed prediction errors in exam grades influence performance goals and major changes, while machine learning achieved 63% classification accuracy