The Review on Eye Tracking Technology Application in Digital Learning Environments

Baltic J. Modern Computing, Vol. 9 (2021), No. 1, 1-24 https://doi.org/10.22364/bjmc.2021.9.1.01 The Review on Eye Tracking Technology Application in Digital Learning Environments Aleksandrs GORBUNOVS Distance Education Study Centre, Faculty of E-Learning Technologies and Humanities, Riga Technical University, Kronvalda Blvd.1, Riga, LV-1010, Latvia [email protected] ORCID: 0000-0003-3770-7168 Abstract. New technologies arrive in our daily lives that actualize an urgent issue of preparing of specialists in specific technological fields. Hence, the necessity in providing of relevant education and training opportunities, appropriate digital learning tools, learning management systems and new learning approaches become more pressing. An understanding of students learning needs, their learning experience and activity peculiarities can help a lot to ensure learning process more efficient. And eye tracking technology could be considered as a significant instrument to be used in such studies. This paper provides a brief review on eye tracking technology employed for the findings on an analysis of learning management system user experience, as well as discusses prospective model development directions in this field. Keywords: eye-tracking, gaze, interaction, learning management system, user experience. 1. Introduction People are different, each of them has own specific individuality. This is reflected in their activities, behaviours, individual perceptions of things, affairs and processes. Education professionals and researchers try to understand learners’ individual behaviour and activity peculiarities in order to improve the efficiency of the learning process. These individualities allow researchers to provide appropriate findings with respect to people behaviour patterns looking for personalized adaptive solutions (Zuga et al., 2015). This approach is used in different areas of interest: starting from Web browsing usage experience and e-commerce advertisements till social collaboration and education in digital learning environments. Moreover, in the rapid changing world which brings us new technologies, digital tools and systems, our knowledge society is forced to look for the necessary solutions to improve learning process, which can be achieved by assessing the specific characteristics of students' learning experience within learning management systems (LMS). In digital learning environments it could be useful to find an effective way to divide learners in groups with regards to their learning activities and learning styles. There were numerous attempts to adapt user learning styles (Danielson, 1997; Gilbert and Han, 2 Gorbunovs 1999), however, the question about the most appropriate classification of learning activities to be reckoned with during modelling of adaptive learning/tutoring solutions, is still not solved yet (Brusilovsky, 2001). Thus, it was concluded that there was the necessity to work on predefining of learning objects depending on students’ learning styles and behaviour patterns, bearing in mind that insufficiency of such learning object or their groups might adversely affect adaptive tutoring systems (Graf, 2010). At the same time, we can find that during recent years information system users’ individuality, their learning, social collaboration, trading, internet browsing experience and preferences are being studied also by implication of biosensor technologies and eye tracking tools. Human bio-signal data, in particular gaze data, can give researchers and adaptive IS designers extra new data which would be used to understand users’ cognitive issues during exploration of a particular website, as well as an involvement level in the content (McNamara et al., 2015). Eye tracking tools embedded into adaptive tutoring systems might be considered as the powerful effcious instruments to personalize learning content in accordance to particular student needs, thereby providing effective knowledge delivery and acquisition (Lai et al., 2013). An analysis and proper interpretation of gaze data could help us in developing of new digital learning environment which would promote more efficient learning process. Eye tracking tools and systems provide researchers with a valuable access to IS users’ behavioural data (Meisner and Oll, 2019) which, in their turn, give an additional comprehension about cognitive side of human behaviour processes (Ashby et al., 2016). Previous applications of eye trackers were intended to track and verify user involvement in cognitive processes and the ability to follow the e-materials offered. In addition, it was considered that the student follows all the materials placed on the LMS webpages according to the imaginary plan of the instructional designer. But this is not the case (Jarodzka et al., 2017). Given that each student has their own unique learning style, subject perception, cognitive abilities, and other behavioral characteristics, it is very important to elucidate the peculiarities of user activities and experience within digital learning environment. To do so, it might be identified several categories that ought to be taken into account when developing e-learning objects, managing and guiding learning process in the digital learning environment: culture, gender, literacy level, very young learners, foreign language acquisition. The following sections provide an overview of what and how we see on a computer display, how eye trackers can help evaluate learners’ experience, including different categories of learners, as well as provide support for necessary improvements within LMS related to e-learning objects and instructional design. The paper also proposes and discusses prospective model research directions in eye tracking application in digital learning environments. A huge amount of research papers, related to "Eye-tracking AND Learning", are published in scientific journals and conference proceedings. Just in the Web of Science database we can find more than 1900 such sources. But the thing is that majority of them are related to the “learning” issues only. Some of the pure “learning” papers might be linked to this review. However, the aim of the paper is to show the digital learning environment user (i.e. current or potential student) experience, activity and behaviour patterns, which can be assessed by eye trackers. Consequently, research questions related to the learning ought to be addressed to another study. On the other hand, eye tracking technologies, discussed in many other reports, are used also without particular linkage to The Review on Eye Tracking Technology Application in Digital Learning Environments 3 learning process, and such research results are found in many and many articles. The first main criteria for the source inclusion into this review was the relevance of the study/article to the aim of this paper. Next, particular institution’s and scientist’s work activity impact on developments in eye tracking field was consisred as well. The number of citations in recognizable databases, as well as publications in a high-impact scientific journals and conference proceedings, also enhanced an inclusion of these sources in the reference list. 2. How and what we see on the computer display Even when we think that our vision is stable, that is not correct. During awaking period our eyes are constantly moving, dividing this movement process into saccades (i.e. our eyes are moving and pausing, thus generating a number of saccades; so, it could be said that the saccades are movements between fixations) and fixations (i.e. extremely short time periods, usually 100 to 600 milliseconds, in which our eyes are fixed on a specific area or object). As the fixations usually are very short, our eyes continue movements, giving us a picture of test participant’s sight direction and areas of interest (AOI). People move their eyes in order to see better. This is caused by limitations of human eye where only fovea area can ensure a clear high resolution picture (Carpenter, 1988; Leigh and Zee, 2006). Respectively, our main attention is devoted to areas within foveal vision, which is only less than eight per cent of human visual field (Tobii Technology, 2010). Other major parts of our eyes have parafoveal and peripheral vision which just give us a sense or imagination about things and processes around us, which can provide us only low resolution picture, motion, colour (Rayner, 1998). However, this feature brings a new opportunity for researchers dealing with eye tracking, interface design and user experience studies. Despite small area of the fovea, other parts of human vision, parafoveal and peripheral vision, provides the person a common general picture of the objects and process flows, which gives a perception of the surroundings, and thereby impacts person’s decision process, e.g. choosing of vision object. Therefore, at the same time it might be said that if the eye movements were not fixed at the particular point, it would not be meant that the person did not aware of it (Bergstrom and Schall, 2014). Eye tracking can provide researchers with clear picture about test participant’s eye movements. Heat maps, gaze plots, and gaze video segments are usually employed for gathered gaze data visualisation. There are three main attributes which compose an understanding about eye movements: location, duration, and movement. The position of the eyes can be mapped on the grid at a specified time between their movements. The duration of fixations could be shown as the dots with variable sizes: as bigger is the dot, the more time was spent by system user to this location. Fig.1 illustrates information system user’s eye movements, location and duration of fixations. It should be noted that the gaze data do not necessarily depict user’s mind and particular attention. At the moment, eye tracking systems can provide only data about eyes specific fixations which might be an accidental blink of the eyes that the user may not even remember (Bergstrom and Schall, 2014). Gaze data can provide valuable information about system users interests and cognitive processes. Measuring their activity, behaviour and eye-catching patterns during Web browsing, eye tracking researchers can make some predictions about users’ further website visits. An analysis of gathered gaze data can lead for creating or updating 4 Gorbunovs website design in order to meet website visitors’ expectations, and, as a result, make it more efficient and profitable. In the case of learning management systems, it can induce also redesign of e-learning modules and learning objects (location, types, formats, etc.). Fig. 1. Mapping of fixations (Bergstrom and Schall, 2014). One of approaches in order to find out IS users’ behaviour patterns and their preferences is based on Visual hierarchy theory which explains website search pathway through a two-phase cognitive process: website scanning phase and inspection phase. During scanning phase, the user scrolls through the page till the entry point is found. Then, within inspection phase the user looks around the entry point for corresponding information. This two-phase process repeats again and again, going through many and many of entry points, where the user has a possibility to search for the information he/she is interested in (Faraday, 2000). It ought to be noted that website characteristics, such as objects type, size, style, colour, location, etc. influence both these phases (Djamasbi and Hall-Phillips, 2014). The common dominant tendency is that usually users start look at the top and left parts of the website (Faraday, 2000). This tendency has also been called the golden triangle or F-shaped pattern of viewing. It also should be noted that website visitors might lose their attention to some important objects on the bottom and right parts of the website, thereby missing them (Buscher et al., 2009; Nielsen, 2017). Information system user viewing experience could be illustrated by exploiting heatmaps. Fig. 2 shows an example of the user’s website scanning experience, where viewed locations are coloured in different tones: red – the most visited, yellow – moderate attended, blue – infrequently viewed, and grey – have not interested in (Nielsen, 2006). The Review on Eye Tracking Technology Application in Digital Learning Environments 5 Fig. 2. Website scan-paths – gaze heat-maps (Nielsen, 2006). 3. Eye-tracking technology in digital learning environments 3.1. Types of eye tracking systems used in digital learning environments Basic function of eye tracking systems is to find the link between the system user and the system interface by measuring user's interest in the particular content. They track eye position and movement in real time in order to determine and evaluate system user’s visual attention. Human-computer interactions produce a big amount of data about information system user’s activities within the system. Learning analytics can provide necessary conclusions regarding user’s preferences and habits during computer interaction sessions, including number of log-files, type of used files, and so on. More and more popular eye tracking systems allow monitoring and analysing users’ preferences with regards to digital content search and interface preferences. Eye tracking has significant advantages over more traditional experimental techniques: it might be done without a secondary task, such as a button press in self-paced reading, allowing for natural reading or viewing; besides, it provides a very rich record of gaze behaviour which would represented by various visual forms (Raschke et al, 2014). Eye tracking systems are also used to investigate human cognition processes (Clay et al., 2019). Speaking about prospective ideal eye tracking system, it would be useful to specify appropriate requirements considered in previous studies (Boff et al., 1986; Scott and Findlay, 1993; Glenstrup, 1995). However, we ought to bear in mind that not of all existing technologies can completely support them, e.g. such questions might be raised regarding an accuracy, resolution, detection of even minor eyes movements, free unattached user access to the display and unobstructed field of view, improvements of real-time response, competitively wide dynamic range for eye position and eye velocity, and so on (Mohamed et al., 2008). Eye movement activity assessment devices usually are divided into three main types: computer (laptop) screen mount devices (also called – desktop eye trackers), eye movement activity assessment glasses (also called – mobile eye trackers), and eye trackers embedded within virtual reality headsets (Farnsworth, 2018). In the case of onscreen equipment, the person must be in front of the computer screen and the equipment monitors the movements of the user's eyes, assessing their activity and interaction with 6 Gorbunovs the content displayed on the screen. Although there are limits to the amount of head and eye rotation that modern eye goggles have, it does not interfere with the on-screen monitoring of user attention points, providing an accurate overview of the person's interests displayed. In the educational process, it would be important to identify those learning objects which are more attractive for the students, and where the text, video, image or exercise units would be better placed on the screen. Nowadays the most widely employed eye trackers use video-based design. At the same time the market holds out eye trackers that integrate ordinary low-resolution webcams and use no infrared light. Video-based eye tracking relies on off-the-shelf components and video cameras, and therefore can be used for developing eye-aware or attentive user interfaces that do not strictly require accurate point of gaze tracking (Hansen and Pece, 2005; Hansen and Majaranta, 2012). While these systems are less expensive and seem to be an ideal solution for smaller budgets, the lack of a controlled light source along with a rather weak camera performance will reduce measurement accuracy significantly (Blascheck et al., 2014, 2017). According to scientific publications indexed in SCOPUS, IEEE Xplore Digital Library, ScienceDirect, ACM Digital Library, and others, it might be said that following main eye tracking systems are academically approved: Tobii Technology, myGaze, SensoMotoric Instruments (SMI), Eyetech Digital Systems, Smart Eye, TheEye Tribe, and GazePoint. They all offer wide range of eye tracking possibilities; however, only SMI provides completely functioning system, but in the case of Tobii and myGaze a customer is asked to obtain Morae software allowing to record, observe, and analyse usability studies, focus groups, field research, and product testing. The program allows watching interactions remotely, taking notes, analysing results, and instantly sharing observation results with colleagues and other students. Several universities, industrial leaders in eye tracking system development and appropriate research institutions have an expertise in eye tracking in their labs, but are less knowledgeable about education-focused research. They focus mainly on studies in neuroscience, marketing, visualization research (Toker et al., 2013), as well an accuracy in medical diagnostics graph reading (Davies et al., 2016) and Webpage usability (Eraslan et al., 2016). Despite studies where gaze data of both group and individual users were collected also in virtual learning environment (Brajnik et al., 2016; Kaššák et al., 2016), there are no sufficient signs and clear directions on how to apply such data in order to enhance users’ self-discipline, improve learning outcomes, learners’ engagement and motivation. It might be also noted that the belonging to different nations and cultures have an impact on information system users’ behaviour traits. At the same time, the studies, fulfilled in human computer interaction field with respect to cultural and gender aspects, ought to be continued and extended. 3.2. Eye tracking technology: cultural impact Cultural issues play significant role in studies on human-computer interaction (HCI). Regarding cross-cultural issues, the book “International User Interfaces” (Fernandes, 1995) remains the classical one for many researchers till now. Research activities on possible impact of differrent cultures on particular nation and culture behaviour features during interaction with digital systems are usually implemented by multinational research teams (Heimgärtner, 2007; Schoper and Heimgärtner, 2013). It should be noted that working in multinational environment shows The Review on Eye Tracking Technology Application in Digital Learning Environments 7 significant cultural behaviour differences which are based on different, and sometimes – totally distinctive, thinking process, e.g. abstract thinking ability differences, reporting deadlines, hierarchy levels, willingness to work on the project only if its results bring tangible benefits to the customers, and so on (Hofstede and Hofstede, 2010). This all influences HCI project team’s common outcomes. At the same time, it allows to get know persons’ behaviour features and cultural individualities (Heimgärtner, 2013; Heimgärtner and Solanki, 2014). It could be said that this aspect not only may cause delays in some cases, but also could help to develop products which suit particular nation and culture the most. Information system service and product developers also ought to think about their customers, bearing in mind cultural, national and gender diversity issues (Plocher et al., 2012). Product’s user interface engineering process is culturally influenced (Honold, 1999); so, user interfaces ought to be developed depending on particular customer group cultural and behavioural traits (Röse, 2005; Heimgärtner, 2014). In order to succeed, cross-cultural user interface engineers ought to know at least basic proved statements and principles on cultural diversity issues (Thomas et al., 2010). An influence of culture on product usability, besides cultural imprints, such as religion, prosperity, gender attitude, education, age proportion, customs, etc. (Honold, 1999), could be characterized by several key influencing parameters, e.g. levels of individualism or collectivism in particular nation or cultural group, masculinity, distance of power, indulgence, pragmatism, avoidance of uncertainty, etc. (Hofstede and Hofstede, 2010). These cultural differences are really immense and ought to be considered during information system user interface designing. Some of cultural models could be more influential (for example, individualism vs. collectivism), others – less (Vöhringer-Kuhnt, 2002). However, irrespective of whether these criteria are more or less influential, interactive information systems should suit not only overall usability requirements but also take into consideration cultural aspects (Heimgartner, 2013). Interesting eye tracking studies were performed recently, where attentional and communicative parameters of the United Kingdom and Hong Kong teachers were compared. One of the aims was to investigate how teachers from different countries (i.e. cultures) behave while giving lessons and providing answers and information to students, as well as asking students. Inter alia, it was found that experienced teachers, regardless of culture, have shown prioritisation of learners, however regarding communication processes Hong Kong teachers have shown prioritisation of instructional area, but UK teachers – non-instructional ones (McIntyre et al., 2019). Cross-cultural findings in HCI have been continuing in further projects emphasising an importance of the design of User Interfaces (UI) and User Experience (UX) (Heimgärtner et al., 2018). 3.3. Eye tracking technology: gender considerations Partially gender issue might be seen as the continuation of cultural imprint diversity influence on human-computer interaction (HCI). Scientists are very cautious regarding HCI research on gender differences. Though, some findings could be found and discussed. Eye tracking tools are also used in these projects. Rather early studies already investigated eye movement differences between genders in different age groups. It was found that eye movements differ by gender only in the adult phase (Miyahira et al., 2000). 8 Gorbunovs In 2014 Ministry of Science and Technology, Taiwan, disseminated research results regarding gender differences in epistemic beliefs and the reading process. Students’ (both females and males) gaze data were used to record their science-text reading process. It was found that female students showed less mental effort in understanding scientific explanations. Male students had more interests in studying scientific matters. On the other hand, female students preferred to attend more data and micro-view graphic learning themes. It was also found that female students, rather than male ones, showed better results on tasks related to processing of textual information (Yang et al., 2016). Vilnius University conducted the study on learners’ book cover colour preferences. It was observed that the book cover colour influenced such choice. Research results have shown that women in the age group from 18 till 35 preferred cool colours; however, in older women’s age group this preference gradually disappeared. In turn, men in the age group after 56 preferred warm cover colours. There was not observed the choice of such a warm colour in the younger men group. Researchers have also found that in women’s group there was a positive correlation between the time spent for choosing a book with preferred cover colour and time looking at this book; in turn, no any correlations were found in men’s group (Gudinavičius and Šuminas, 2018). These findings could suggest instructional designers to develop frames and graphics of digital learning objects coloured in tones which are accepted by particular learners’ group. 3.4. Eye tracking technology: very young readers How often we imagine reading material acquisition and comprehension process among young readers? Do we think enough about complexity of materials for reading, even in the cases of rather basic, entry level reading texts? What could be said about readers’ progress? In many studies researchers assume that all learning management system users read the text similarly, so, text perception is seemed as the similar letter and word recognition process for all (Reichle et al., 2003; Engbert et al., 2002; Reichle et al., 1998). However, it could be noted that there are differences in reading skills among people and this aspect should be taken into consideration in order to adjust models of eye movement control in reading (Ashby et al., 2005). Children's reading, perception, comprehension, behaviour and attitude issues have been discussed in a number of research papers on psychology and cognition. They ought to be taken into account while performing eye tracking research. One of the most comprehensive studies on the textual material perception and reading issues occurring at the first reading steps could be reached in Keith Rayner and other colleagues’ publications (for example, Rayner et al., 2012; Rayner et al., 2006; Rayner, 2009; 1998; White and Liversedge, 2006). The length and complexity of words affect eye fixation (Rayner, 1998). Long words require more time from a young reader (Rayner et al., 2012) and might be omitted by them (White and Liversedge, 2004; Rayner et al., 1996). It was also found that refixations depend on linguistic characteristics of the word (White and Liversedge, 2006), so, the learners returned to rather complex words as more trained and experienced they became. It could be also noted that children’s reading experience and reading skills improvement led to shifting of their eyes concentration from separate letters to the whole word and the sentence (Rayner et al., 2012). Parafoveal visual information is also used by very young readers. This guides children’s saccades during reading process, and no any significant differences from adult The Review on Eye Tracking Technology Application in Digital Learning Environments 9 readers is found despite some limitations of children’s parafoveal vision (Blythe and Joseph, 2011). In studies on reading fluency, both oral and silent, oculomotor data confirmed that all children achieved learning, i.e. reading speed, progress from the first testing time to the second one. Moreover, better reading progress was noticed among children who had worse results during initial test. Besides, it was also found that the oral reading required more mental concentration in order to understand reading material (Vorstius et al., 2015). 3.5. Eye tracking technology: people with low literacy Low literacy abilities mean that such persons have particular difficulties with recognizing of words in text sentences. This impact their abilities to learn due to required much longer time to read and understand textual and graphical materials. Besides, the text they read may be misinterpreted (White, 2003). On the other hand, low literacy should be considered not only as the reading problem. This group of population also have problems to perceiving mutual information. They may have thinking and problem solving ways which differ from other people approaches (Canadian Association of Chiefs of Police, 2008). During usability tests their participants might be asked to give feedback comments on processes doing by them. This approach, called “think-aloud” method, provides valuable information for interface developers and instructional designers. Despite undeniable benefits, the method has also some constraints. For example, it overloads test takers by asking them not only fulfil particular tests but also describe mutually things they are doing and thinking (Brinkman, 1992). Moreover, by forcing test takers “think aloud”, this may change their task accomplishment pathway. As a result, activity and behavioural patterns of the participants might be changed, but this is not what the researchers do want to get know (Nagle and Zietlow, 2012). Bearing in mind that cognitive impairments also affect reading abilities, it might be said that “think aloud” methods becomes less useful for people with cognitive impairments (Johnstone, 2006). On the other hand, eye trackers and gaze data can display the picture of test taker’s real usability experience within information system (IS) and task accomplishment steps. Eye tracking does not set additional cognitive pressure, thus, it could be considered as the more useful instrument for usability test implementation among persons with low literacy skills (Colter and Summers, 2014). It might be suggested that combination of eye tracking and other human bio-signal processing systems can provide more efficient instruments and data while testing persons with other types of cognitive impairments. Very often persons with low literacy are unable to recognize themselves being difficult to read and correctly perceive the information. Moreover, it was ascertained that the persons try to mislead other people: colleagues, friends and even relatives (Kirsch, 2002). Eye tracking tools can help educational practitioners to identify such persons and give them appropriate teaching support. It is obvious that complex textual and graphical structures in many cases may impact comprehension and perceptual abilities among skilled readers (Romano and Chen, 2011). Hence, complicated text structure and unusual arrangement may even despair persons with low literacy. It is underlined that improved IS interfaces and organized, easy-to-read structured text can assist not only skilled readers but also low literacy readers, giving them giving them an opportunity to understand particular material faster 16 Gorbunovs From instructional design perspective it could be useful to find the ways for adapting digital learning environment, i.e. learning management systems, to the learner’s abilities which suit their needs the most. That might include findings related to learning content improvement, possible adaptive solutions of preferred content types, direct or linked location on the display and accessibility. Determination of user experience (UX), i.e. learner’s attention and engagement into learning process could play significant role here. Information system user’s learning preferences, activities, and behaviour patterns could lead not only to knowing about learner’s engagement level, interest in particular content (i.e. learning object), attention, but also might signalize about possible boredom. IS user’s activities ought to be observed, appropriate gaze and other learning analytics data gathered, processed and analysed at each step of the learning process (module). These data would allow better understand learner’s study progress, any problems with learning content acquisition. They are also aimed to render necessary educational assistance to particular learner if he/she has faced with any problem related to learning (both pedagogical and technical ones). 5. Conclusions Eye tracking systems and their generated gaze data can provide researchers with a clear picture about test participant’s eye movements. Heat maps, gaze plots, and gaze video segments are usually employed to visualise user experience gathered by eye tracking technology. Three main attributes compose an understanding about eye movements: location, duration, and movement. An important thing is that eye tracking system records all de facto eye movements, including unknowingly made. This feature makes it possible to analyse possible side effects which impact information system user’s decisions. Information system user experience could be effectively studied by involving visual hierarchy theory which explains website search pathway through a two-phase cognitive process: website scanning phase, i.e. corresponding to the exploratory search behaviour type, and inspection phase, i.e. corresponding to the goal-directed behaviour type. Interactive information systems designers ought to take into account not only general design and implementation criteria, but also particular cultural issues. Cultural differences, such as masculinity, distance of power, indulgence, pragmatism, avoidance of uncertainty, etc., are really immense and ought to be considered during IS user interface designing. It could be also noted that the F-shaped pattern of viewing is considered as the most frequent dominant pattern, however, it depends on cultural traits regarding text reading direction. When designing an adaptive e-learning management system based on eye movement research, certain features should be accounted for. Proper consideration of factors, such as culture, gender, age, literacy, and cognitive styles, can result in development of efficacious digital learning environment. However, succeed advantages might have also possible side effects, e.g. limitation of these systems in distribution and application area. During learning process, we mostly perceive the subject matter through the eyes. Because learning process is based on where, what, for how long, and in what order we are looking at, an eye tracker can help determine that. By knowing the characteristics of users’ behaviour and activities, i.e. learning preferences, we can improve instructional design in technology enhanced learning. The Review on Eye Tracking Technology Application in Digital Learning Environments 17 In order to improve an effectiveness of information system user experience research, it would be useful to consider an application of eye tracking technology in combination with other biosensor systems. Additional tools and analytical data might explore hidden patterns in user behaviour and activities. Especially it ought to be kept in mind while working on development of adaptive tutoring systems. List of abbreviations used AAR - after action review AOI – area of interest AR – augmented reality EMME – eye movement modelling example HCI – human-computer interaction IEEE – Institute of Electrical and Electronics Engineers IS – information system LI – lessons identified LL – lessons learned LMS – learning management system LO – learning object POI – point of interest TEL – technology enhanced learning UI – user interfaces UX – user experience VR – virtual reality Acknowledgements This work has been supported by the European Regional Development Fund within the Activity 1.1.1.2 “Post-doctoral Research Aid” of the Specific Aid Objective 1.1.1 “To increase the research and innovative capacity of scientific institutions of Latvia and the ability to attract external financing, investing in human resources and infrastructure” of the Operational Programme “Growth and Employment” (No.1.1.1.2/VIAA/1/16/042). References Alemdag, E., Cagiltay, K. (2018). 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