Trends at the Intersection of Eye Tracking, Machine Learning, Distance and Online Learning: A Bibliometric Analysis

In recent years, a considerable amount of scientific research has been conducted on eye tracking, distance/online learning and machine learning. However, there is no comprehensive bibliometric analysis study regarding the current situation in the intersection of these studies. In this study, the bibliometric analysis of scientific and academic articles (n = 8575) published in the last five years (2019-2023) in the context of eye tracking, distance education, online education and machine learning is discussed. During the data collection process, the data obtained from the Web of Science platform were classified on a yearly basis. Data obtained from the Web of Science platform were mapped, visualized and analyzed with VosViewer, pyBibx and Tableau software. According to the results of the research, the authors named “liu, sannyuya” and “liu, zhi” appeared as top two authors with 25 TLS and identified as the most prominent authors in this field. It was observed there occured identifiable networks of collaboration between different researchers. The works by Alqurashi (2019) (TLS = 31), Caskurlu (2021) (TLS = 29) and Accettone (2022) (TLS = 27) were more prominent as the result of citation analysis. The countries such as China, Singapore, Thailand, Philippines, Ethiopia, Bahrain United Arab Emirates, have become more prominent since 2021. Such as Nanyang Technol Univ. (Singapore) (TLS = 71), Monash Univ. (Australia) (TLS = 69), Cent China Normal Univ. (China) (TLS = 66), Chinese Acad Sci. (China) (TLS = 66), Zhejiang Univ. Organizations (China) (TLS = 57) were discovered as top organizations. While the keywords like online learning (TLS = 5185), education (TLS = 4012), students (TLS = 3391), performance (TLS = 3040), e-learning (TLS = 2934) were representing the general trends among the researchers, the trends with the phrases such as “natural language processing”, “machine learning”, “deep learning” and “sentiment analysis” started to be more visible and important among the researchers. However, no direct trend was found regarding “eye-tracking” in this work. The reason behind this can be because of that there are not yet enough studies in the context of distance/online learning and machine learning benefiting from to eye-tracking, eye-tracking technologies or eye-tracking data. It was concluded that there is potential a gap in the literature on the subject of “eye-tracking” in the context of “distance/online learning”, and “machine learning”. Therefore, it is recommended to conduct a structured or systematic literature review in order to compare the methods and data types used in the studies and visualize the relations in more detail.