In this paper, we propose to predict academic performance of university students from multi-sources data in multimodal and blended learning environments using data fusion and data mining. We have gathered data from 65 university students and different variables from four different sources. Firstly, we apply data fusion and preprocessing for creating a summary dataset in numerical and categorical format. Then, we have applied different white box classification algorithms provided by Weka data mining tool in order to select the best algorithm. Finally, we show the best predicting model in order to help instructor to take remedial actions with students at risk of dropout or failing.

In this paper we apply data fusion approaches for predicting the final academic performance of university students using multiple-source, multimodal data from blended learning environments. We collect and preprocess data about first-year university students from different sources: theory classes, practical sessions, on-line Moodle sessions, and a final exam. Our objective is to discover which data fusion approach produces the best results using our data. We carry out experiments by applying four different data fusion approaches and six classification algorithms. The results show 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