Eye tracking technologies have led to various developments in many scientific fields such as human-computer interaction, cognitive psychology and educational research in recent years [1] - [3] . These technologies essentially allow researchers to capture and analyze individuals’ visual attention by monitoring their eye movements. By examining computer-aided models of eye movements, researchers can access important information about human visual perception, attention distribution, and cognitive processes in the brain. With the rapid development of computer and Internet technologies in recent years, accessing studies in scientific research fields has become easier for many researchers and followers via scientific journal databases such as Web of Science and Scopus. In addition, it has become possible to make future predictions regarding the studies carried out by means of bibliometric analyses [4] - [6] . This study focuses on the current status and possible future trends in the intersection of eye tracking distance education, online education and machine learning. In this context, firstly, the research problem is mentioned, and then important studies from the relevant literature are summarized. Following this, the methods used in the research are mentioned and the research results are discussed and listed in the context of the research questions.

Bibliometric analyses undoubtedly provide the researchers better insights in the scientific works on e-learning [7] , mobile learning [8] , instructional design [9] , distance and higher education [10] . The studies especially in the fields like eye-tracking, distance and online learning and machine learning are rapidly produced since they benefit from many different branches of science and are gaining diversity, but bibliometric analyses shedding light on the intersection of these fields are still missing. And therefore, it seems very difficult to explore the current status of these studies and predict how these studies will evolve in the coming years. In this respect, this study focuses on the current status of the studies conducted in the last five years period and what the future situation regarding the use of eye tracking technologies in context of distance and online learning and machine learning will look like. To test whether there was a previous bibliometric analysis on that context, a simple query was first made through the Web of Science database with the keywords “eye-tracking (All Fields) AND bibliometric analysis (All Fields)” on Web of Science platform in October 2023. And only 22 results were reached as a result of the query (See Table 1 ).

Considering the results, it was determined that a total of 13 studies were conducted in 2022 and 2023, which is more than half of the total studies conducted until 2023, which means that eye tracking and bibliometric analysis studies have accelerated in recent years and the number of the studies have increased. Although there is an increase in the number of studies in the context of eye-tracking and bibliometric analysis, when the titles and contents of the studies are examined, a very limited number of studies (n = 6) are addressing topics such as eye-tracking and education [11] , reading [21] , Dyslexia [23] , Human-Computer Interaction [2] , Virtual Reality [30] . However, it was observed that no bibliometric study was conducted yet in the context of distance education, online education and machine learning.

With this result, main assumption of the research was supported, and relevant research questions were later produced in the context of “eye-tracking, distance and online learning and machine learning”, which is the focus and main subject of the research:

Eye-tracking technologies consist of advanced hardware and software components to precisely measure and record data obtained from individuals’ eye movements. The data collected includes many other information such as eye-movement metrics (e.g. fixation points, saccades, etc.) as well as other descriptive parameters such as frequencies and percentages. Eye-tracking techniques include some particular methods such electro-oculographic, scleral search coils, photo-oculography and pupil center to corneal-reflection [32] . These data points and techniques can provide researchers with important quantitative results that can be used to analyze visual behavior and draw meaningful conclusions about the cognitive processes underlying human perception. Eye-tracking technologies have received significant attention in educational research due to their potential to provide valuable insights into students’ cognitive processes, attention allocation, and engagement [11] [22] . It is also well known that eye-tracking technologies have a very important place in distance and online education research, and in recent studies, the features might be categorized under particular subheadings. This literature review aims to summarize eye tracking applications in educational research, focusing on their benefits, limitations, and future directions.

In this study, bibliometric mapping, visualization and analysis methods were used to determine the current status and future trends regarding eye tracking technologies, distance, online education and machine learning. Bibliometric analysis is a quantitative research method widely used to examine patterns, trends, and relationships in scientific literature. This approach uses bibliographic data, such as publication records, citations, and co-authorship networks, to gain insight into various aspects of scholarly communication. With bibliometric analysis, special software and statistical techniques are used to analyze large data sets. One widely used analysis is co-authorship analysis, which examines patterns of collaboration among authors. Another analysis is citation network analysis, which helps identify influential publications and key researchers in a field. In addition, it is possible to reveal the research productivity, impact and knowledge dissemination in a particular field, as well as the change between years and new emerging topics, through keyword analysis [4] - [6] .

In this part of the study, the findings obtained as a result of data mapping and visualization were discussed. The results associated with the research questions and the analysis and discussions regarding the results were included in the discussion section.

RQ-1: Which authors are most prominent in this field, and are there identifiable networks of collaboration among different researchers?

For this question, Co-Authorship-Authors analysis was carried out with VOSviewer. For this analysis, the minimum number of documents and citations value was determined as 5, and a total of 255 results were obtained for these parameters. With these results, Document, Citation and Total Link Strength (TLS) scores were calculated by VOSviewer. As a result of the analysis, the authors named “liu, sannyuya” (number of documents = 12 and number of citations = 376) and “liu, zhi” (number of documents = 11 and number of citations = 191) were listed as the top two authors with the 25 TLS score. The authors such as “yang, zongkai” (number of documents = 9 and number of citations = 109) and “ogata, hiroaki” (number of documents = 20 and number of citations = 188) were ranked as third and fourth authors with 22 and 21 TLS scores (see Figure 6 ). Regarding the network visualization created with VOSviewer, it was visualized that there occurred 6 clusters in total with the given parameters. The Co-authorship network in the middle of the visualization indicated the authors such as “liu, sannyuya”, “liu, zhi”, “peng, xian” and “yang, zongkai” with most frequent collaborations (see Figure 7 ).

RQ-2: What are the most cited authors and articles in the intersection of eye-tracking, distance/online learning, and machine learning?

Regarding this question, citation-documents analysis was performed with VOSviewer and the minimum number of citations of documents parameter was applied as 0 and 8575 results were obtained in order to visualize all possible networks. With this, network visualization has been achieved. As a result, it was observed that 25 clusters were formed in total, and it was determined that authors and articles such as Alqurashi (2019) (TLS = 31), Caskurlu (2021) (TLS = 29) and Accettone (2022) (TLS = 27) were more prominent in the analysis (See Figure 8 ). With the given parameters, the network visualization showed that there occurred at least two clusters (cluster 22 and 23), which solely focused on eye-tracking studies (See Figure 9 ). It was also observed that three (Chiu, 2022; Khalil et al., 2020; Simmons & Mistry, 2023) out of top ten authors and articles three of the most cited articles were on COVID-19, which proved that the works on Covid 19 and distance or online learning dominated this intersection when compared to the studies (See Table 4 ).

RQ-3: Is there a geographical and organization concentration of research activity in these domains, and are there any emerging countries and organizations contributing significantly to this field?

In the context of this question, first co-author and country and then co-author organization visualizations were performed with VOSviewer. It was visualized that the countries such as USA (TLS = 757), China (TLS = 739), England (TLS = 452), Australia (TLS = 410) and Germany (TLS = 308) were the top 5 countries with higher concentration of research activity (See Figure 10 ). In the density

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visualization, 9 clusters in total appeared and it was explored that the countries such as Iceland, Colombia, Argentina, Peru, Tunisia and North Macedonia were highly differentiated from the rest of the countries, and more importantly, the countries are basically divided on the east-west axis. (See Figure 11 ). The overlay visualization applied later showed that some countries, especially China, Singapore, Thailand, Philippines, Ethiopia, Bahrain United Arab Emirates, have become more prominent since 2021 (See Figure 12 ). The co-authors-organizations analysis applied later also revealed similar results and 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 such as (China) (TLS = 57) made up the top 5 organizations (See Figure 13 ). And the organizations such as Beijing Normal Univ., Tencent Ai Lab, Univ. Edinburgh, Chinese Univ. Hong Kong, King Abdulaziz Univ. and Saarland Uni. were identified as emerging organizations (See Figure 14 ).

RQ-4: What are the publication trends in articles that address eye-tracking, distance/online learning, and machine learning? How have these trends evolved over the past five years?

Considering this question, to gain a general insight into the trends emerging in the research, a word cloud was first created with keywords. Word cloud provided general information, albeit limited, and sized the prominent words according to their frequency. Accordingly, the words that stand out are words such as “learning”, “online”, “education”, “language”, “distance”, “student” and “covid” (See Figure 15 ). Then, co-occurrence - all keywords analysis was carried out with VOSviewer. The words like online learning (TLS = 5185), education (TLS = 4012), students (TLS = 3391), performance (TLS = 3040), e-learning (TLS = 2934) appeared as the most frequently occurred keywords produced in the articles (See Figure 16 ). As a result of the overlay visualization, 5 clusters were identified in total and it was clarified that the keywords such as “natural language processing”,

“machine learning”, “classification”, “feature selection”, “recommender system” started to be more visible and important among the researchers. It was also observed that the key word “eye-tracking” was linked with the words such as “attention”, “cognitive load” and “performance” (See Figure 17 ).

Then, n-grams were examined with pyBibX software and Trigrams were accordingly visualized. Meaningful results were reached in the analysis, such that keywords such as “face to face” (f = 50), “self-determination theory” (f = 29), “cognitive load theory” (f = 26), “pedagogical content knowledge” (f = 25), “higher education students” (f = 23), “higher education online” (f = 21), and “open online courses” (f = 21) were identified as the most frequently used words. And it was also observed that among these words, there occurred a word, “support vector machines”, which is frequently used in machine learning (See Figure 18 ). Although these results gave some limited insights about the researchers’ trends, this did not give more information on trends and evolution of the trends. Therefore, the analysis was continued with pyBibX to detect and visualize a more detailed insights with trends, based on authors’ keywords. As a result of the evolution analysis, the change in trends over the years was determined as in Figure 19 . According to the results of this analysis, there occurred some trends that had been constantly researched by researchers for five years, and “blended learning”, “online learning”, “higher education” and “e-learning” can be given as examples for these trends. Apart from these, it constantly maintained its importance among researchers on topics such as “machine learning” and “deep learning” for last five years. In addition, this analysis also showed that since 2020, “Covid-19” has entered the field of interest of researchers and constituted a significant part of the research. However, the topics of “Artificial Intelligence” and “Natural Language Processing” attracted more attention among researchers since 2021 and began to become a new trend. As of 2023, “Sentiment Analysis” has begun to become the focus of more attention among researchers. With the given entity parameter (value = 10) and analysis only with the authors’ key words, no direct trend was found regarding “eye-tracking” (See Figure 19 ).

Eye-tracking studies are studies that require a lot of human and computer effort, time, financial support and well-established technical infrastructures. In addition, it can be very difficult to carry out such studies without personnel specialized in eye-tracking. Nevertheless, according to [59] there are still a lot of advantages that eye-tracking technology can bring to e-learning. For example, it can support personalized and adaptive learning [60] , as demonstrated by the adaptive e-learning system [32] , which can infer certain emotions from the learner and respond appropriately. When eye tracking is used in e-learning, learners tend to be more motivated and pay closer attention to the learning interface and eventually some abilities such as reading ability [61] , response processes and adaptive problem-solving [62] can be detected. The data obtained from eye-tracking studies can be easily used for different purposes, and machine learning-based classifications or artificial intelligence for purpose of determination of students’ performance are also exercised with these data [41] [63] . Thanks to all these advantages, eye-tracking can offer opportunities and possibilities that researchers can easily apply in fields of study such as distance/online learning and machine learning.

As a result, a collaboration and network in terms of co-authorship was found among the authors. However, this seems a limited collaboration and when we look at the countries of the authors and the institutions they work in, it is seen that they collaborate most with the authors in their own region or neighboring countries. Although regional concentrations were observed, it was indicated that western countries form clusters mostly among western countries and eastern countries form clusters mostly among themselves. This means that the countries that conduct scientific studies on these issues partially conduct international studies. As understood from keyword analysis, although words such as “natural language processing”, “machine learning”, “sentiment analysis” have become more visible and turned into trends in recent years, it is estimated that it is too early to say that these topics have a more significant relationship with “eye-tracking”. Compared to other topics, the subject of eye-tracking has not yet evolved into a more dominant position regarding distance/online learning and machine learning. As of now, at the intersection of these issues, topics such as COVID-19 as one of the dominant subjects in the last three years, engagement, performance, come to the fore, and eye tracking is mostly linked to the issue of attention and performance. 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 eye-tracking technologies or eye-tracking data. In other words, it is presumed that there is a research gap in the relevant literature on this subject or context. The frequency of the keywords also supports this situation, in that only 78 studies included “eye-tracking” in their title (See Figure 20 ).

It is regarded that subjects such as eye tracking, distance/online learning, and machine learning are interdisciplinary and that more detailed studies with more data are needed to determine to what extent these fields use each other’s methods, particularly eye-tracking, and to what extent they interact. Although topics such as machine learning and artificial intelligence have been in close contact with distance/online learning as a trend in recent years, how much eye tracking or eye-tracking technologies and methods are used in the field of distance/online learning remains a question that still needs to be answered.

In this work, a comprehensive bibliometric analysis study regarding the current situation in the intersection of these studies was conducted. The bibliometric analysis of scientific and academic articles published in the last five years (2019-2023) in the context of eye tracking, distance education, online education and machine learning was carried out and discussed. The data-set obtained from the WoS platform was mapped, visualized and analyzed with VosViewer and pyBibx software. It is observed that while eye tracking is a promising field of study that can be used in different parts of the development during the distance education process, it is not yet widely used in this field. Since these types of studies require an interdisciplinary study, they are very difficult to carry out with a single perspective and infrastructure without technical support. It seems that there is still a potential gap in the literature on the subject of “eye-tracking” in the context of “distance/online learning”, and “machine learning”. Data sets containing more scientific publications from different sources (e.g. Scopus, etc.) are needed to better detect the interdisciplinary interaction in eye-tracking and other mentioned fields and to provide a more understandable prediction and insight, though. In this regard, 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.