Enhancing High-School Dropout Identification: A Collaborative Approach Integrating Human and Machine Insights

Abstract Despite its proven success in fields like engineering, business, and healthcare, human-machine collaboration in education remains relatively unexplored. This study aims to highlight the advantages of human-machine collaboration for improving efficiency and accuracy of decision-making processes in educational settings. High school dropout prediction serves as a case study for examining human-machine collaboration's efficacy. Unlike previous research that prioritized high accuracy with immutable predictors, this study seeks to bridge gaps by identifying actionable factors for dropout prediction through human-machine collaboration. Utilizing a large dataset from the High School Longitudinal Study of 2009 (HSLS:09), two machine learning models were developed to predict 9th -grade students' high school dropout history. Results indicated that the Random Forest algorithm outperformed the deep learning algorithm. Model explainability revealed the significance of actionable variables such as students’ GPA in the 9th grade, sense of school belonging, and self-efficacy in mathematics and science, along with immutable variables like socioeconomic status, in predicting high school dropout history. The study concludes with discussions on the practical implications of human-machine partnerships for enhancing student success.