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.

中文翻译：

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多模态学习分析和教育数据挖掘中的数据融合综述

新的教育模式，如智能学习环境，使用数字和情境感知设备来促进学习过程。在这种新的教育场景中，可以捕获、融合和分析来自各种不同来源的大量多模态学生数据。它为研究人员和教育工作者提供了一个独特的机会，使他们能够发现新知识以更好地理解学习过程并在必要时进行干预。但是，有必要正确应用数据融合方法和技术，以便结合多模态学习分析 (MLA) 的各种来源。MLA 中的这些来源或模式包括音频、视频、皮肤电活动数据、眼动追踪、用户日志和点击流数据，还包括学习人工制品和更自然的人类信号，如手势、注视、语音、或写作。本调查介绍了学习分析 (LA) 和教育数据挖掘 (EDM) 中的数据融合，以及这些数据融合技术如何应用​​于智能学习。它通过回顾主要出版物、融合教育数据的主要类型、EDM/LA 中使用的数据融合方法和技术，以及该特定领域的主要开放问题、趋势和挑战，展示了当前的技术水平研究领域。