Academic development of multimodal learning analytics: a bibliometric analysis

ABSTRACT

Multimodal Learning Analytics (MMLA) has huge potential for extending the work beyond traditional learning analytics for the capabilities of leveraging multiple data modalities (e.g. physiological data, digital tracing data). To shed a light on its applications and academic development, a systematic bibliometric analysis was conducted in this paper. Specifically, we examine the following aspects: (1) Analyzing the yearly publication and citation trends since the year 2010; (2) Recognizing the most prolific countries, institutions, and authors in this field; (3) Identifying the collaborative patterns among countries, institutions, and authors, respectively; (4) Tracing the evolving procedure of the applied keywords and development of the research topics during the last decade. These analytic tasks were conducted on 194 carefully selected articles published since 2010. The analytical results revealed an increasing trend in the number of publications and citations, identified the prominent institutions and scholars with significant academic contributions to the area, and detected the topics (e.g. characterizing learning processes using multimodal data, implementing ubiquitous learning platforms) that received the most attention. Finally, we also highlighted the current research hotspots attempting to initiate potential interdisciplinary collaborations to promote further progress in the area of MMLA.

KEYWORDS:

• Multimodal learning analytics
• bibliometric analysis
• learning analytics
• social network analysis
• topic modeling

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Bo Pei

Bo Pei is a PhD candidate in School of Teaching & Learning, College of Education at University of Florida. His research interests are multimodal learning analytics, educational data visualization and machine learning.

Wanli Xing

Dr. Wanli Xing is an Assistant Professor of Educational Technology at University of Florida. His research interests are artificial intelligence, learning analytics, STEM education and online learning.

Minjuan Wang

Dr. Minjuan Wang is Professor of Learning, Design, and Technology at San Diego State University (California, USA). Her work has been highly interdisciplinary, covering the field of education, technology, computer science, and STEM education. Her research specialties focus on online learning, mobile learning, and interactive learning environments. Minjuan has served as Editor-in-Chief and invited editor for several highly ranked journals. She is also recognized as one of the high impact authors in blended and mobile learning. She has published in SCI, SSCI, and EI indexed journals, such as Educational Technology Research and Development, IEEE Transactions on Education, British Journal of Educational Technology, and Journal of Ambient Intelligence and Humanized Computing.