  The goals of Learning Analytics (LA) are manifold, among which helping
students to understand their academic progress and improving their learning
process, which are at the core of our work. To reach this goal, LA relies on
educational data: students' traces of activities on VLE, or academic,
socio-demographic information, information about teachers, pedagogical
resources, curricula, etc. The data sources that contain such information are
multiple and diverse. Data mining, specifically pattern mining, aims at
extracting valuable and understandable information from large datasets. In our
work, we assume that multiple educational data sources form a rich dataset that
can result in valuable patterns. Mining such data is thus a promising way to
reach the goal of helping students. However, heterogeneity and interdependency
within data lead to high computational complexity. We thus aim at designing low
complex pattern mining algorithms that mine multi-source data, taking into
consideration the dependency and heterogeneity among sources. The patterns
formed are meaningful and interpretable, they can thus be directly used for
students.
