Interpretable Multiview Early Warning System Adapted to Underrepresented Student Populations

Abstract

Early warning systems have been progressively implemented in higher education institutions to predict student performance. However, they usually fail at effectively integrating the many information sources available at universities to make more accurate and timely predictions, they often lack decision-making reasoning to motivate the reasons behind the predictions, and they are generally biased toward the general student body, ignoring the idiosyncrasies of underrepresented student populations (determined by socio-demographic factors such as race, gender, residency, or status as a freshmen, transfer, adult, or first-generation students) that traditionally have greater difficulties and performance gaps. This paper presents a multiview early warning system built with comprehensible Genetic Programming classification rules adapted to specifically target underrepresented and underperforming student populations. The system integrates many student information repositories using multiview learning to improve the accuracy and timing of the predictions. Three interfaces have been developed to provide personalized and aggregated comprehensible feedback to students, instructors, and staff to facilitate early intervention and student support. Experimental results, validated with statistical analysis, indicate that this multiview learning approach outperforms traditional classifiers. Learning outcomes will help instructors and policy-makers to deploy strategies to increase retention and improve academics.