  Accurately predicting their future performance can ensure students successful
graduation, and help them save both time and money. However, achieving such
predictions faces two challenges, mainly due to the diversity of students'
background and the necessity of continuously tracking their evolving progress.
The goal of this work is to create a system able to automatically detect
students in difficulty, for instance predicting if they are likely to fail a
course. We compare a naive approach widely used in the literature, which uses
attributes available in the data set (like the grades), with a personalized
approach we called Personalized Student Attribute Inference (PSAI). With our
model, we create personalized attributes to capture the specific background of
each student. Both approaches are compared using machine learning algorithms
like decision trees, support vector machine or neural networks.
