Early-predicting dropout of university students: an application of innovative multilevel machine learning and statistical techniques
Marta Cannistrà, Chiara Masci, Francesca Ieva, Tommaso Agasisti, Anna Maria Paganoni
Abstract
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 Italian university.
Topics & Concepts
Dropout (neural networks)Machine learningHigher educationComputer scienceMultilevel modelArtificial intelligenceMathematics educationStatistical learningStatistical analysisArtificial neural networkPsychologyStatisticsMathematicsLawPolitical scienceOnline Learning and AnalyticsOnline and Blended Learning