Multi-source and multimodal data fusion for predicting academic performance in blended learning university courses

arXiv:2403.05552v1 Announce Type: cross
Abstract: 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 …

abstract academic arxiv courses cs.ai cs.cy cs.lg data environments fusion multimodal multimodal data multiple paper performance practical students theory type university