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.

摘要

多模态学习分析 (MMLA) 具有巨大的潜力，可以将工作扩展到传统学习分析之外，以利用多种数据模式（例如生理数据、数字跟踪数据）。为了阐明其应用和学术发展，本文进行了系统的文献计量分析。具体来说，我们考察以下几个方面：（1）分析2010年以来的年度发表量和被引量趋势；(2) 表彰该领域最多产的国家、机构和作者；(3) 分别确定国家、机构和作者之间的合作模式；(4)追溯近十年来应用关键词的演变过程和研究主题的发展。这些分析任务是对 2010 年以来发表的 194 篇精心挑选的文章进行的。分析结果揭示了出版物和引用数量的增长趋势，确定了对该领域做出重大学术贡献的著名机构和学者，并发现了主题（例如表征使用多模式数据的学习过程，实施无处不在的学习平台）受到了最多的关注。最后，我们还强调了当前的研究热点，试图启动潜在的跨学科合作，以促进 MMLA 领域的进一步进展。并发现最受关注的主题（例如，使用多模式数据描述学习过程、实施无处不在的学习平台）。最后，我们还强调了当前的研究热点，试图启动潜在的跨学科合作，以促进 MMLA 领域的进一步进展。并发现最受关注的主题（例如，使用多模式数据描述学习过程、实施无处不在的学习平台）。最后，我们还强调了当前的研究热点，试图启动潜在的跨学科合作，以促进 MMLA 领域的进一步进展。