This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…

The present work has its focus on university-level engineering education students that do not intend to major in computer science but still have to take a mandatory programming course. Phenomenography and variation theory are applied to empirical data from a study of students' conceptions of computer programming. A phenomenographic outcome space…