The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye-tracking, user logs, and click-stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area.

翻译：智慧学习环境等新型教育模式利用数字化与情境感知设备以促进学习过程。在这一新型教育场景中，可从多种不同来源捕获、融合与分析海量多模态学生数据。这为研究人员与教育工作者提供了独特机遇，使其能够发现新知识以更深入理解学习过程，并在必要时进行干预。然而，为有效整合多模态学习分析（MLA）的各类数据源，必须正确应用数据融合方法和技术。MLA中的数据源或模态包括音频、视频、皮肤电活动数据、眼动追踪、用户日志与点击流数据，同时涵盖学习制品及更自然的人类信号（如手势、凝视、语音或书写）。本综述介绍了学习分析（LA）与教育数据挖掘（EDM）中的数据融合，并阐述这些数据融合技术如何在智慧学习中应用。通过评述该领域主要文献、融合教育数据类型、EDM/LA中采用的数据融合方法与技术，以及该特定研究领域的主要开放性问题、趋势与挑战，本文展现了当前研究现状。