Applying Machine Learning to Improve Curriculum Design

Creating curriculum with an ever-changing student body is difficult. Faculty members in a given department will have different perspectives on the composition and academic needs of the student body based on their personal instructional experiences. We present an approach to curriculum development that is designed to be objective by performing a comprehensive analysis of the preparation of declared majors in Computer Science (CS) BS programs at two universities. Our strategy for improving curriculum is twofold. First, we analyze the characteristics and academic needs of the student body by using a statistical, machine learning approach, which involves examining institutional data and understanding what factors specifically affect graduation. Second, we use the results of the analysis as the basis for applying necessary changes to the curriculum in order to maximize graduation rates. To validate our approach, we analyzed two four-year open enrollment universities, which share many trends that help or hinder students' progress toward graduating. Finally, we describe proposed changes to both curriculum and faculty mindsets that are a result of our findings. Although the specifics of this study are applied only to CS majors, we believe that the methods outlined in this paper can be applied to any curriculum regardless of the major.