Abstract
This full research paper presents a systematic analysis of 10 years' student performance data of Computer Science (CS) majors at San Francisco State University, a public 4-year degree-granting university, aiming to address the ongoing challenges of early dropouts and low graduation rate. The main objective is two-fold: (1) gain a comprehensive understanding of how the existing curriculum has been supporting (or hindering) students' progress towards graduation; and (2) suggest data-informed curricular changes. To this end, we utilize both explorative statistical analysis and data mining/machine learning approaches to first learn how individual courses and the prescribed course sequences influence a student's dropout/graduation status, and then build machine learning models to interpret/validate the observed interdependency among key courses in the current curriculum. Such patterns/models are consequently utilized to suggest impactful curricular changes towards reducing early dropouts and improving the overall student success as measured by graduation with a CS degree. One main finding of this research is that a successful CS student needs to excel in both critical thinking and core CS skills. To help students gain critical thinking skills, it is essential to strengthen the presence of mathematics and physics courses in the CS curriculum. Furthermore, our results suggest that CS students without a solid math foundation before starting their college career should complete a remedial math course earlier than putting it off for later. Moreover, before students advance to the second half of their CS study to gain core CS knowledge/skills (e.g., operating systems), they should complete the required physics class. Finally, we observe that it is necessary to introduce new prerequisite requirements among upper-level CS courses, for example, Operating Systems as a prerequisite to an upper-level CS core course on programming theories.
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