A review on data fusion in multimodal learning analytics and educational data mining

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...

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Published in:Wiley interdisciplinary reviews. Data mining and knowledge discovery 2022-07, Vol.12 (4), p.e1458-n/a
Main Authors: Chango, Wilson, Lara, Juan A., Cerezo, Rebeca, Romero, Cristóbal
Format: Article
Language:English
Subjects:
Audio data
Data analysis
data fusion
Data integration
Data mining
Data science
Education
educational data science
Learning
Learning analytics
Mathematical analysis
multimodal learning
smart learning
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Summary: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. This article is categorized under: Application Areas > Education and Learning General multimodal data fusion approach for EMD/LA.
ISSN:1942-4787
1942-4795
DOI:10.1002/widm.1458