Multi-source and multimodal data fusion for predicting academic
  performance in blended learning university courses
W. Chango R. Cerezo C. Romero
In this paper we applied data fusion approaches for predicting the final
academic performance of university students using multiple-source, multimodal
data from blended learning environments. We collected and preprocessed data
about first-year university students from different sources: theory classes,
practical sessions, on-line Moodle sessions, and a final exam. Our objective
was to discover which data fusion approach produced the best results using our
data. We carried out experiments by applying four different data fusion
approaches and six classification algorithms. The results showed that the best
predictions were produced using ensembles and selecting the best attributes
approach with discretized data. The best prediction models showed us that the
level of attention in theory classes, scores in Moodle quizzes, and the level
of activity in Moodle forums were the best set of attributes for predicting
students' final performance in our courses.