This Innovative Practice Category Work In Progress paper presents an application of machine learning and data mining to student performance data in an undergraduate electrical engineering program. We are developing an analytical approach to enhance retention in the program especially among underrepresented groups. Our approach will provide quantitative assessment of student performance in courses. Specifically, by hierarchically mapping the content of assignments to course learning objectives, we can better decipher which concepts a particular student is struggling with and, with the help of peer mentors, create tailored intervention techniques to help the student be successful in the program. These results will also be useful to academic advisors who can work with the student to determine class schedules that promote success in the program. In addition, students can take a proactive approach to their learning. In our approach, data from our learning management system and other available sources will be used to predict several outcomes for individuals such as when a student is beginning to have trouble with the material or if factors outside of the classroom are affecting their success. Here, we present our initial database schema and preliminary results relating number of class re-takes to time-to-graduation. [This paper was published in: "2018 IEEE Frontiers in Education Conference (FIE)," IEEE, 2018, pp. 1-5.]