摘要：AbstractStudent 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.
关键词：KeywordsStudent AnalyticsAgent-Based SimulationLearning AnalyticsEducational Data MiningAcademic Analytics
