Scalability, Sustainability, and Ethicality of Multimodal Learning Analytics

Abstract

Multimodal Learning Analytics (MMLA) innovations are commonly aimed at supporting learners in physical learning spaces through state-of-the-art sensing technologies and analysis techniques. Although a growing body of MMLA research has demonstrated the potential benefits of sensor-based technologies in education, whether their use can be scalable, sustainable, and ethical remains questionable. Such uncertainty can limit future research and the potential adoption of MMLA by educational stakeholders in authentic learning situations. To address this, we systematically reviewed the methodological, operational, and ethical challenges faced by current MMLA works that can affect the scalability and sustainability of future MMLA innovations. A total of 96 peer-reviewed articles published after 2010 were included. The findings were summarised into three recommendations, including i) improving reporting standards by including sufficient details about sensors, analysis techniques, and the full disclosure of evaluation metrics, ii) fostering interdisciplinary collaborations among experts in learning analytics, software, and hardware engineering to develop affordable sensors and upgrade MMLA innovations that used discontinued technologies, and iii) developing ethical guidelines to address the potential risks of bias, privacy, and equality concerns with using MMLA innovations. Through these future research directions, MMLA can remain relevant and eventually have actual impacts on educational practices.