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.

我们应用数据融合方法，使用多源多模态数据从混合学习环境中预测大学生的最终学业表现。我们通过应用四种不同的数据融合方法和六种分类算法进行实验，结果表明，使用集成和选择最佳属性方法进行离散化数据的最佳预测模型显示出在理论课程中的注意力水平、Moodle测验分数以及Moodle论坛中的活动水平是预测学生最终表现的最佳属性集。

融合多源多模态数据预测混合学习大学课程的学术表现