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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.

In recent years, there are many naturalistic driving projects have been conducted, such as the 100-Car Project (Naturalistic Driving study in United State), EuroFOT(European Large-Scale Field Operational Tests on Vehicle Systems), SeMi- FOT(Sweden Michigan Naturalistic Field Operational Test and etc. However, those valuable naturalistic driving data hasn't been applied into Human-machine Interaction (HMI) design for Advanced Driver Assistance Systems (ADAS), a good HMI design for ADAS requires a deep understanding of drive environment and the interactions between the driving car and other road users in different situations. The results demonstrated the benefits of using naturalistic driving films as a mean for enhancing focus group discussion for better understanding driver's needs and traffic environment constraints. It provided an efficient tool for designers to have inside knowledge about drive and the needs for information presentation; The recommendations for how to apply this method is discussed in the paper. [ABSTRACT FROM AUTHOR]