Multi-source and Multimodal Data Fusion for Predicting Academic Performance in Blended Learning University Courses

On today’s article data fusion approaches are used for predicting the final academic performance of university students using multiple-source, multimodal data from blended learning environments. Wilson Chango, Rebeca Cerezo and Cristóbal Romero claim that the best predictions are produced using ensembles and selecting the best attributes approach with discretized data. The best prediction models show us that the level of attention in theory classes, scores in Moodle quizzes, and the level of activity in Moodle forums are the best set of attributes for predicting students’ final performance in our courses.