  Online Judge (OJ) systems are typically considered within programming-related
courses as they yield fast and objective assessments of the code developed by
the students. Such an evaluation generally provides a single decision based on
a rubric, most commonly whether the submission successfully accomplished the
assignment. Nevertheless, since in an educational context such information may
be deemed insufficient, it would be beneficial for both the student and the
instructor to receive additional feedback about the overall development of the
task. This work aims to tackle this limitation by considering the further
exploitation of the information gathered by the OJ and automatically inferring
feedback for both the student and the instructor. More precisely, we consider
the use of learning-based schemes -- particularly, multi-instance learning
(MIL) and classical machine learning formulations -- to model student behavior.
Besides, explainable artificial intelligence (XAI) is contemplated to provide
human-understandable feedback. The proposal has been evaluated considering a
case of study comprising 2500 submissions from roughly 90 different students
from a programming-related course in a computer science degree. The results
obtained validate the proposal: The model is capable of significantly
predicting the user outcome (either passing or failing the assignment) solely
based on the behavioral pattern inferred by the submissions provided to the OJ.
Moreover, the proposal is able to identify prone-to-fail student groups and
profiles as well as other relevant information, which eventually serves as
feedback to both the student and the instructor.
