Towards Collaborative and Intelligent Learning Environments Based on Eye Tracking Data and Learning Analytics: A Survey

Abstract

The current pandemic has significantly impacted educational practices, modifying many aspects of how and when we learn. In particular, remote learning and the use of digital platforms have greatly increased in importance. Online teaching and e-learning provide many benefits for information retention and schedule flexibility in our on-demand world while breaking down barriers caused by geographic location, physical facilities, transportation issues, or physical impediments. However, educators and researchers have noticed that students face a learning and performance decline as a result of this sudden shift to online teaching and e-learning from classrooms around the world. In this paper, we focus on reviewing eye-tracking techniques and systems, data collection and management methods, datasets, and multi-modal learning data analytics for promoting pervasive and proactive learning in educational environments. We then describe and discuss the crucial challenges and open issues of current learning environments and data learning methods. The review and discussion show the potential of transforming traditional ways of teaching and learning in the classroom, and the feasibility of adaptively driving learning processes using eye-tracking, data science, multimodal learning analytics, and artificial intelligence. These findings call for further attention and research on collaborative and intelligent learning systems, plug-and-play devices and software modules, data science, and learning analytics methods for promoting the evolution of face-to-face learning and e-learning environments and enhancing student collaboration, engagement, and success.

Keywords

• Artificial intelligence
• attention measurement
• data science
• human–computer interaction
• learning
• multimodal learning analytics