PREDICTING STUDENT SUCCESS IN PRE-CALCULUS

In higher education, many courses have evolved into "gatekeeper" course. These courses are defined to be high-enrollment, low-completion courses which are cornerstones in general education. These courses often become graduation roadblocks and prevent students from attaining their educational goals. At the authors' home institution, Pre-calculus has become a gatekeeper course with approximately 40% of the students failing to complete the course, a failure rate commonly seen at many institutions of higher education. This paper details the development of a highly accurate neural network-based, predictive system that identifies students at the beginning of the semester as being "at-risk" of not completing the course. "At-risk" students are defined to be students likely to earn a C-or below in the course. The predictors/inputs into the neural network offer pedagogical insights into how a student's "at-risk" classification might be mitigated via numerous techniques such as concurrent support courses, online tutorials, "just-in-time" instructional aides, study skills, motivational interviewing, and/or peer mentoring.