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Multimodal Data Fusion in Learning Analytics: A Systematic Review.

Su Mu1, Meng Cui1, Xiaodi Huang2

  • 1School of Information Technology in Education, South China Normal University, Guangzhou 510631, China.

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|December 3, 2020
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Summary
This summary is machine-generated.

Multimodal learning analytics (MMLA) integrates diverse data types like digital, physical, and physiological to understand learning indicators such as behavior and cognition. This systematic review clarifies data fusion methods crucial for MMLA advancements.

Keywords:
data fusionlearning indicatorsmultimodal datamultimodal learning analyticsonline learning

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Area of Science:

  • Educational Technology
  • Learning Analytics
  • Data Science

Background:

  • Multimodal learning analytics (MMLA) is gaining traction for deeper insights into learning.
  • Effective integration of diverse data types within MMLA remains an area needing clarification.
  • Understanding how various data sources contribute to analyzing learning processes is essential.

Purpose of the Study:

  • To systematically review and categorize data types, learning indicators, and data fusion methods in MMLA.
  • To answer key questions regarding the relationships between multimodal data and learning indicators.
  • To identify classifications of data fusion techniques and future research directions in MMLA.

Main Methods:

  • Systematic literature review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
  • Analysis of 346 MMLA articles published over the past three years.
  • Development of a conceptual model based on data types, learning indicators, and data fusion for article review.

Main Results:

  • MMLA data encompasses digital, physical, physiological, psychometric, and environment data types.
  • Learning indicators include behavior, cognition, emotion, collaboration, and engagement.
  • Data fusion methods are classified as many-to-one, many-to-many, and multiple validations, addressing complex data-indicator relationships.

Conclusions:

  • The study clarifies the landscape of data types and learning indicators used in MMLA.
  • It provides a taxonomy of data fusion methods, highlighting their importance for complex relationships.
  • Findings offer a foundation for future research in multimodal data fusion for enhanced learning analytics.