Although several research works show that students at risk of dropping out of a course or a study program can be predicted with relatively high accuracy, this information has so far often not been accessible to course directors, teachers, or students. The DiSEA project aims to research this issue and close this gap in the context of online-degrees. Building on previous research, machine learning methods will be used to identify risk and success factors. The overall aim is to develop an integrated model to predict success in digital study programs and derive recommendations and interventions for course design, student counseling, and student selfreflection. A user-centered design involving all stakeholders will be followed.
In Companion Proceedings of the 11th Learning Analytics and Knowledge Conference (LAK’21), p. 261-269. Workshop on Addressing Dropout Rates in Higher Education, Online – Everywhere, 2021.