PASSt-A: Agent-based student analytics aimed at improved feasibility and study success

Abstract

Student analytics relates student characteristics (e.g. gender, country of origin, prior education) to Key Performance Indicators such as length of study and drop-out quota. In that context, work has been largely based on Data Analytics and statistical analysis. Dynamic aspects of studying - such as individual factors affecting study success, student-student and student-lecture interactions - cannot be captured in that manner, which is why this paper argues for the employment of Agent-Based and Discrete Event Simulation in addition to the aforementioned approaches. Apart of being novel, our contribution lies in the conception of a simulation model called PASSt-A, which defines the data semantics and procedures used for study analytics in an extensible manner.