Finding Bottlenecks: Predicting Student Attrition with Unsupervised Classifier
This addresses the problem of improving graduation rates for higher education institutions by enabling early identification of at-risk students, but it appears incremental as it applies existing unsupervised methods to a specific dataset.
The paper tackled predicting student attrition and identifying bottleneck courses using unsupervised clustering on lower division course data from five departments at CSUN, achieving automated early warning without specifying concrete performance numbers.
With pressure to increase graduation rates and reduce time to degree in higher education, it is important to identify at-risk students early. Automated early warning systems are therefore highly desirable. In this paper, we use unsupervised clustering techniques to predict the graduation status of declared majors in five departments at California State University Northridge (CSUN), based on a minimal number of lower division courses in each major. In addition, we use the detected clusters to identify hidden bottleneck courses.