综述多模态学习分析中的数据融合技术，助力智能教育研究。
A review on data fusion in multimodal learning analytics and educational data mining
- 系统梳理多模态数据融合在智能学习中的应用方法
- 涵盖音频、视频、生理信号等多源数据融合现状
- 适合教育科技与数据分析领域研究人员参考
智能学习环境利用数字和情境感知设备促进学习过程。在此新教育场景下，可从多种来源捕获、融合并分析大量多模态学生数据，为研究者和教育者提供发现新知识的契机，以更好理解学习过程并在必要时进行干预。然而，需正确应用数据融合方法与技术，整合多模态学习分析（MLA）中的各类数据源。这些模态包括音频、视频、皮肤电活动数据、眼动追踪、用户日志与点击流数据，以及学习成果和手势、凝视、语音、书写等更自然的人类信号。本综述介绍学习分析（LA）与教育数据挖掘（EDM）中的数据融合，回顾主要研究成果、融合数据类型、使用的技术及方法，并总结该领域的当前挑战、趋势与开放问题。
原文摘要 · Abstract (English)
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.
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