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.
Highlights
The COVID-19 pandemic is an ongoing disruptive vent across our world
This study found that eye tracking assessments of cognitive progress in multimedia learning include selecting, organizing, and integrating information about the learning process
We present a comprehensive survey by emphasizing the existing eye-tracking enabled learning systems and analytics and combining them with data science, multimodal learning, and artificial intelligence
Summary
The COVID-19 pandemic is an ongoing disruptive vent across our world. It has changed education entirely and resulted in many primary and higher education institutions switching to in-person classes in 2020 and Spring 2021. Basic machine learning classifiers were tested, and it was shown that they are potentially able to predict these types of cognitive measures from eye-tracking data along and automatically, with enough accuracy to be used as a model of the users to help design better decision making systems As another example, some researchers are interested in predicting the performance levels of subjects from sensor data. The authors showed that there were statistical differences in various derived measures of where and how novice and expert map readers used a program to search for and answer questions about information from a map This type of real-time prediction of the user level on task-based eye-tracking or other gathered sensor data is obviously a very powerful tool. While much of the research has demonstrated the possible advantages of being able to model high-level student understanding of concepts, it is still very difficult to integrate this type of modeling into working learning systems
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