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

TL;DR

This paper reviews how data fusion techniques are applied in multimodal learning analytics and educational data mining to enhance understanding of learning processes in smart educational environments.

Contribution

It provides a comprehensive overview of current data fusion methods, modalities, and challenges in the context of educational data mining and learning analytics.

Findings

Identification of key data modalities used in MLA

Overview of data fusion techniques applied in education

Discussion of open problems and future trends in data fusion

Abstract

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.