# Engagement and Attention in XR for Learning: Literature Review

Carlos Lièvano Taborda, Huyen Nguyen, Patrick Bourdot

<figure><img src="image_1.png" /></figure>

## To cite this version:

Carlos Lièvano Taborda, Huyen Nguyen, Patrick Bourdot. Engagement and Attention in XR for Learning: Literature Review. Lecture Notes in Computer Science, 2025, Lecture Notes in Computer Science, 15445, pp.207-242. 10.1007/978-3-031-78593-1_13. hal-04979614

HAL Id: hal-04979614

https://hal.science/hal-04979614v1

Submitted on 6 Mar 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Engagement and Attention in eXtended Reality for Learning: A Systematic Literature Review

Carlos Liévano Taborda<fnref n="1" />, Huyen Nguyen<fnref n="1" />, and Patrick Bourdot<fnref n="1" />

Université Paris-Saclay, CNRS, LISN,
{carlos.lievano, huyen.nguyen,
patrick.bourdot}@universite-paris-saclay.fr

**Abstract.** Engagement and attention are two crucial factors in determining the effectiveness of the learning experience, and both concepts have been studied extensively in educational sciences. However, these two aspects have not been studied sufficiently in eXtended Reality (XR) settings. Recognizing the growing role of XR in education, this review aims to study and understand this gap through a systematic literature review following the PRISMA methodology, renowned for its thoroughness and reliability. Our main objectives were to identify different methods to measure students' attention and engagement and discuss strategies to enhance them. After a comprehensive analysis, we identified the most effective methods for measuring students' engagement and attention in XR environments. Our study also makes evidence of the interest in an integrating evaluation approach gathering optimal tracking of attention and engagement during learning experiences. Finally, we discussed various approaches to enhance users' engagement and attention, highlighting other relevant elements in designing and developing learning applications, such as the user-centred design of dedicated immersive paradigms and pedagogical-based learning scenarios.

**Keywords:** Engagement · Attention · Learning · Virtual Reality · Augmented Reality · Mixed Reality · eXtended Reality · Systematic Review

## 1 Introduction

Advances in eXtended Reality (XR)<fnref n="1" /> technologies have facilitated their wide application in education. Incidentally, following the reality-virtuality continuum of Milgram and Kishino [29], we use the generic naming of XR to target both Virtual Reality (VR) and Mixed Reality (MR), the latter being Augmented Reality (AR) or Augmented Virtuality (AV) depending on the ratio of real/virtual content merging. While numerous literature studies on learning and training using immersive technologies confirm their use and demonstrate continuous growth, generally with positive results (e.g., [17,23,56]), significant work still remains

<footnote n="1">¹ Abbreviations: XR (eXtended Reality), VR (Virtual Reality), MR (Mixed Reality), AR (Augmented Reality), E/A (Engagement and Attention), HMD (Head-Mounted Display), EEG (Electroencephalography).</footnote>
to be done to be able to enhance the teaching and learning experience. Engagement and Attention (henceforth frequently short-named as E/A) are two decisive factors of the learning process that enable learners to focus their cognitive resources on the learning tasks [38]. Therefore, two main challenges concern how to accurately measure and efficiently improve learners’ E/A in XR learning environments. Since these two factors notably reveal the students’ effort dedicated to the learning process, extending to the motivation in their education [16], we aim to analyse and get insights into the current state of the E/A studied in existing immersive learning systems.

Attention, as defined by Posner and Petersen [39], is “the ability to selectively process information from the environment while ignoring other information”. This definition implies that attention is necessary for the learners to filter out distractions and focus on relevant information. Concerning the learning process, Plebanek and Sloutsky emphasise that attention is a fundamental cognitive process that enables learners to focus their cognitive resources on the task at hand, leading to better memory encoding and retention of new information [38]. Therefore, attention is essential for effective learning, as it helps the learners focus on and process relevant information while ignoring distractions.

Engagement is a concept frequently used in games and XR technologies. It “refers to a person’s involvement or interest in the content or activity of an experience, regardless of the medium” [14]. In education, engagement is defined as “the extent to which students are willing and able to take on the learning task at hand” [43]. In learning sciences, engagement is even considered a set of factors such as “the degree of attention, curiosity, interest, optimism, and passion that students show when they are learning or being taught, which extends to the level of motivation they have to learn and progress in their education” [16]. This latter definition interestingly highlights that attention is one of the engagement factors, but not the only one. Moreover, this broader definition emphasises that engagement involves active participation and a rich interplay of cognitive and emotional factors. Accordingly, engagement is vital to improve information retention, tackle educational challenges, and acquire academic success.

Understanding the two aspects of engagement and attention within the education domain and especially how they are considered in XR environments is just the first step to addressing the challenges of accurately measuring and efficiently improving learners’ E/A. Indeed, the level of engagement and attention varies in XR learning environments because each immersive system offers different levels of immersion, affordances, interaction capabilities, amongst others, which introduces diverse challenges and opportunities for managing E/A [61,42]. Attention is necessary for acquiring new information, while engagement helps sustain the learners’ motivation and interest throughout the learning process. In many studies, both concepts are closely linked and in this literature review, we chose to consider them together to unravel the dynamics between E/A and XR for teaching and learning.

Different studies have shown diverse methodologies for measuring, quantitatively and/or qualitatively, engagement, attention, or both in immersive learning
environments. There exist some approaches using eye tracking data or Electroencephalography (EEG) signals to quantify the learners' attention (e.g., [53,40,25]). According to Miller [30], eye trackers provide precise quantitative measurement of attention allocation and yield cognitive processing indices that infer learning states. Nevertheless, they do not provide direct evidence about metacognitive and motivational processes. For example, a student could be attentive but not engaged with the learning activity. Therefore, several qualitative methodologies are also employed to address this issue. For instance, some adopted questionnaires to measure the learners' engagement using different models: ICAP (Interactive, Constructive, Active or Passive) framework [55], PEEM (Positive Engagement Evaluation Model) [18], or taxonomy of student engagement [32]. In addition, O'Brien and Toms [34] developed a survey to measure user engagement by identifying attributes such as focused attention, felt involvement, novelty, and endurability.

Even though various approaches show potential for measuring E/A in XR learning environments, a significant gap lies in understanding how they can be effectively measured, taking into account the pedagogical and learning methodology implemented in such environments. Addressing this gap is crucial because pedagogical aspects of immersive learning contribute significantly to the overall teaching and learning experience. Moreover, by considering technological aspects, we can ensure the effective integration of measurement methods with immersive features, thereby maximizing their assessment effectiveness. Additionally, the development of guidelines for designing a framework to support E/A evaluation in education and learning could have broader implications for XR research, potentially benefiting other application domains. Given the rapid advancements in both technological and pedagogical research within XR for education, it is imperative to acquire clear evidence in this regard.

This paper investigates how both engagement and attention can be effectively measured in XR learning by responding to the following Research Questions (RQs):

- **RQ1:** What are the focuses of existing studies on engagement and attention in XR learning scenarios?

- **RQ2:** What are the most effective methods that have been used for measuring students' engagement and attention in immersive learning environments?

- **RQ3:** What approaches have been proposed to enhance the students' engagement and attention in XR learning? How effective are they?

Our literature review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology [36] because its validated guidelines and widespread acceptance are supposed to ensure a rigorous paper selection. We focused on papers from peer-reviewed scientific journals and conferences published in the last ten years, providing a comprehensive overview of recent developments in the field and ensuring that the review is based on the most up-to-date and relevant research works. We examined the concepts of E/A, their measurement methods in XR environments, and how they can be evaluated and enhanced in learning scenarios. By analysing the current state of the
literature and identifying research gaps, this review will provide insights into the
design of effective XR-based learning environments.

The paper is organised as follows. Section 2 provides the context for the lit-
erature review. Section 3 details the approach to address the research questions.
It encompasses investigating key factors distinguishing E/A in XR learning sce-
narios, identifying effective measurement methods, and examining approaches
for enhancing E/A. Section 4 presents the overall analysis results, including sev-
eral identified important factors. Section 5 elaborates on different measurement
methods and experimental design approaches for enhancing E/A. In Section 6,
we derive guidelines based on the findings and discuss open problems. Finally,
Section 7 presents the conclusion, summarising the main findings and their im-
plications.

## 2 Context

By exploring the current context in XR and the education domain, we aim
to establish the significance and relevance of the research questions within the
existing body of knowledge. This section presents broadly the context of XR
for learning scenarios and the need for effective measurement methods used to
assess and enhance E/A in XR learning.

Over the years, researchers have extensively examined factors influencing
E/A in traditional learning environments, as highlighted in studies such as
[4,57]. These investigations have identified instructional design, learning ma-
terials, learner characteristics, and teaching methods as crucial determinants.
However, with the emergence of XR technology and its integration into learn-
ing applications, our purpose is to assess if the factors identified in traditional
learning environments hold true for these new XR-driven learning contexts and,
more importantly, explore which of these factors are actively influencing E/A in
XR settings.

XR learning scenarios offer immersive and interactive user experiences that have the potential to enhance E/A. Researchers and educators [48,21,58,31,37,26] have recognised the potential of XR technology to create engaging and authentic learning through simulated and multisensory experiences, personalised and dynamic contents, and more. However, the specific factors contributing to E/A within XR learning environments require further exploration, which is crucial for leveraging the benefits of XR technology in education and designing effective instructional strategies.

Measuring E/A in XR learning scenarios presents unique challenges. Traditional methods, such as self-report measures and observations are not easy to apply, and may only partially capture in XR environment due to the dynamic and immersive nature of the experience it provides [10,49]. Therefore, it is necessary to identify and evaluate measurement methods that are suitable for assessing E/A in XR learning. These methods should consider the differences amongst learners and learning settings, experimental designs and specific characteristics of XR technology.
Exploring approaches to enhance students’ E/A in XR learning scenarios is
critical for optimising learning outcomes. Previous research [15,8,2] has investi-
gated various approaches, such as gamification, feedback mechanisms, and adap-
tive learning, to promote E/A in traditional learning environments. However, the
applicability and effectiveness of these approaches in XR learning contexts re-
quire further investigation. By synthesising the existing literature on this issue,
we strive to identify effective strategies and provide evidence-based recommen-
dations for educators, instructional designers, and XR developers.

3 Methodology

Our literature review was conducted following the PRISMA guidelines [36]. The
review protocol was developed to address the research questions identified in the
introduction.

<figure><img src="image_3.png" /><figcaption>Fig. 1: PRISMA Flow diagram for selection and screening process</figcaption></figure>

**3.1 Search Strategy**

We performed the systematic literature using the following electronic databases: ACM Digital Library, IEEE Xplore Digital Library, Science Direct and Springer Link. An additional search was made in Google Scholar. The search was conducted to find research articles published in English within the period between 2013 until April 2023. The search strategy used a combination of keywords or expressions and Boolean operators, to ensure comprehensive coverage of relevant literature.
1 ("Virtual Reality" OR "Augmented Reality" OR "Mixed Reality" OR "Extended Reality" OR "Immersive Technology" OR "Immersive Technologies")

2 AND

3 ("Engagement" OR "Attention")

4 AND

5 ("Education" OR "Learning")

## 3.2 Inclusion and Exclusion Criteria

Articles were included if they met the following criteria: (a) Peer-reviewed articles were published in English, (b) Articles that reported on E/A in learning environments using XR technologies, and (c) Articles that reported on methods used to measure engagement and/or attention.

Articles were excluded if they did not meet the inclusion criteria or if they were: (a) duplicates, (b) book chapters, posters, (c) literature reviews, (d) papers without formal user studies, or (e) papers containing studies on E/A which were related to clinical or psychological studies, e.g., autism, learning disabilities, attention disorders, etc.

With the above criteria, we downloaded metadata of 968 articles from four digital libraries and transformed it into CSV format files for further analysis. 51 duplicate articles were removed. From the CSV files, a single file was built in xlsx format with the most important data such as title, authors, year, abstract, keywords, and others.

## 3.3 Screening Process

We carefully screened the titles and abstracts of identified articles based on our exclusion criteria. This process resulted in the exclusion of 851 articles, leaving us with 66 that met our strict eligibility criteria for detailed analysis. Afterwards, we excluded ten papers related to medical and clinical studies, seven state-of-the-art studies, eight without user studies, and eleven not entirely associated with learning activities. Finally, we got 30 articles from the eligibility step. To complement the search outside the databases consulted, we scanned the references of selected articles and also conducted a search in Google Scholar with the criteria above and three more papers were included. We have in total 33 scientific papers for the data extraction and analysis. The full screening and eligibility process is in Figure 1. All the articles were downloaded and subsequently assessed for eligibility independently by two reviewers.

## 3.4 Data Extraction and Synthesis

From the archive with the selected articles, 12 categories were created for their classification and with the subsequent analysis: (1), Number of publications, (2), Devices, (3), Cognitive focus, (4), Educational level, (5), Learning fields, (6),
Targeted users, (7), Teacher intervention, (8), Learning context, (9), Location dependency, (10), Experiment design, (11), Methods for measurement, (12), En-hancing approaches,

A narrative synthesis was later conducted to summarise the findings from the included studies. In Section 4 we present sequentially the most important findings of the first nine categories, supplemented with figures to enhance clarity. The main findings related to experimental design and evaluation methods for E/A are presented with their proper analysis in Section 5. Complementary analyses, interpretations, and limitations will be discussed in Section 6.

# 4 Overall Results

## 4.1 Number of Publications

The analysis of scientific articles per year reveals a consistent and continuous increase in publications, particularly from 2019 to 2022 (Fig. 2). During this period, the combined percentage of works published accounts for 84.84% of the total count, indicating a significant concentration of research activity in the last five years.

<figure><img src="image_4.png" /><figcaption>Fig. 2: Number of articles published over the 2013 – 2023 period and classified by the type of XR devices used in user studies.</figcaption></figure>

## 4.2 Devices

This category classifies the devices and XR technologies used in the learning scenarios. It distinguishes between: VR CAVE-like systems [13], a setup that typically surrounds the users within a room-sized virtual environment for an all-encompassing immersive experience; VR HMD (Head-Mounted Devices), headsets that provide immersive experience directly to the user's eyes and ears with often included hand controllers; and VR Desktop, a mode where virtual environments are experienced via (stereoscopic) images displayed on a standard
computer screen. It also includes AR HMD, with which the users can see virtual
content augmented in the real world through headsets, and AR MD (Mobile De-
vices) including smartphones, tablets, etc. that bring AR experiences through
handheld devices.

The types of XR devices used in learning provide valuable insights into the technological landscape over the years. When we look at this variable with the articles' publication year (Fig. 2), VR HMD and AR MD play a significant role, representing approximately 81.82% of the total articles. Moreover, considering the HMD-based interfaces, VR HMD holds the highest percentage with 45.45% of the total number of articles, indicating a strong emphasis on immersive technologies in learning and training. In contrast, AR HMD has a relatively lower representation, with approximately 6.06% of the articles. On the other hand, AR MD accounts for about 36.36% of articles, signalling sustained interest in handheld AR for educational applications. Tablets and smartphones are predominantly used due to their affordability, versatility, mobility, and seamless integration with live camera feeds. In contrast, VR Desktop is explored in only three articles, while VR CAVE-like system is mentioned in just one. This difference may relate to the accessibility and cost-effectiveness of VR Desktops compared to complex VR CAVE-like systems. These findings highlight the growing role of XR technologies, particularly VR HMD and AR MD in enriching learners' experiences.

Globally, the use of XR devices is valuable in learning by promoting active
learning and student engagement for better outcomes [31,60]. XR technologies
can transform teaching and learning by providing interactive and dynamic edu-
cational experiences. Nincarean [33] indicated that AR started to become more
accessible and cheaper than before due to the increasing popularity of mobile
devices such as smartphones and tablets. This accessibility has opened up new
opportunities for immersive learning experiences. Moreover, recent developments
in computer graphics and hardware technology have led to the widespread use of
headsets, even with biosensors integrated like eye trackers [6]. However, despite
the increasing popularity of AR in the XR market, this effect is not yet reflected
in recent studies.

## 4.3 Cognitive Focus

This category addresses the specific cognitive focuses being investigated in each
research work: Engagement, Attention, and both (E/A). Analysing the data in
this category (Fig. 3), we observe that engagement is the primary focus in the
majority of studies, with 17 cases (48.6%). Attention follows with ten studies
(28.6%), and engagement and attention combined are addressed in six studies
(17.1%). These percentages provide insights into the distribution of cognitive
focus within the examined studies, highlighting the predominant emphasis on
Engagement.

We also analysed the correlation between cognitive focus and the type of
XR devices. It can be observed that for VR HMD-based systems, E/A have
been separately and equally studied, six and seven articles, respectively, over
<figure><img src="image_5.png" /><figcaption>Fig. 3: Number of articles classified by the XR devices used and cognitive focus.</figcaption></figure>

15 articles. However, attention was studied more in VR HMD (46,66% - seven papers out of 15) than in AR HMD+MD (14,28 % - two papers out of 14), which could suggest that methods employed to measure attention are commonly more associated with VR HMD. Conversely, engagement was studied more in AR HMD+MD (57,14% - eight papers out of 14) than in VR HMD (40% - six papers out of 15). This suggests that regarding AR technologies, the studies tended to focus first on the engagement aspect before addressing attention.

Within the cognitive focus, several interesting findings emerge from the literature. The study conducted in [55] showed that students' high levels of engagement have been evident through the quantified qualitative data of their learning processes in classrooms thanks to the use of AR devices. In [59], the result of the user study suggested that the primary predictors of achievement were cognitive attention to the static 3D visualisation and the effort to integrate information from the visualisation. Geller et al. [19] highlighted the significant improvements in student engagement and comprehension through the use of mixed reality presentation software. These studies presented affirmative results, showcasing the positive impact of cognitive attention and engagement on learning outcomes. However, it is crucial to recognise the complexity inherent in this field of research. For instance, Wang and Sun [52] indicated that the co-creation in a virtual environment did not affect significantly cognitive engagement scores. However, the learners' were still strongly engaged at the emotional level. This conclusion hinted at the complexity of the engagement aspect which denotes a whole spectrum of emotional, and psychological involvement of the learners during the learning process. This diversity in research outcomes underscores the need for a comprehensive understanding of the interplay between cognitive focus and learning achievement in XR environments. Considering that engagement, attention, and E/A are the main aspects to be considered in this review, in the following sections we will often combine them with the other factors to accentuate the relationships, if any, between cognitive engagement and attention factors and the learning scenarios in which they have been evaluated.
## 4.4 Educational Levels

<figure><img src="image_6.png" /><figcaption>Fig. 4: Number of articles classified by education levels and grouped by the type of XR devices (left) and cognitive focus (right).</figcaption></figure>

In this section, we focus on the educational levels of the students or learners whom the immersive learning systems targeted and/or recruited for their user studies. We also analysed the educational levels concerning the type of XR devices and the cognitive focus. We arranged the educational levels into four subcategories: preschool, primary and secondary education, higher education (including post-secondary education such as universities, colleges, professional schools, etc.), and others for those out of the formal education scope.

From the results in Fig. 4, there was a rise of studies evaluating E/A for immersive learning for students in higher education (18 articles or 54.55%), followed by those in primary and secondary education as the second largest group (10 articles or 30.3%). Considering the XR systems for higher education in the light of the devices used (Fig. 4 left), we found that the most significant device used was VR HMD with a total of nine papers. This data suggests a strong emphasis on advanced academic pursuits within universities, colleges and higher education settings. Similarly, from the cognitive focus in the higher education category (Fig. 4 right), more than 50% of articles primarily focused on the students' engagement or both E/A. We observed a similar trend in the primary and secondary categories with about 50% of the studies centred on engagement and around 40% focused on attention. Most studies concentrating on Engagement were likely due to their extensive examination of how the students could be engaged in such novel types of 3D interfaces.

Several papers supported the relevance of learning with XR in higher education. For instance, Slavova and Mu [45] conducted a comparative study on the learning outcomes and experience of VR, highlighting its potential to improve students' attention and social interaction. Mystakidis [32] explored the application of gamification in distance education, specifically in postgraduate courses. The study revealed that the use of gamification in a social virtual environment improved the students' engagement, addressing the challenge of sustaining interest in distance learning.
In addition, primary and secondary education also holds a substantial presence in the literature, accounting for 30.30% of the total. We found that many of the studies were directed towards young children. An example includes the work in [10] evaluating the impact of VR in primary school classrooms and examining student engagement and learning outcomes. Their study emphasised the role of interactivity and immersion in facilitating effective learning experiences for students aged 11-13. Tarng, Pan, and Ou [49] investigated the effectiveness VR on attention training for elementary school students, demonstrating that VR training was more effective than computerised training in improving attention and reducing learning anxiety.

Furthermore, we identified four articles in the category ‘others’ representing 13.3% of the total studies. The primary device used in training programs was AR MD (three out of four articles). These articles are mainly dedicated to specific training activities. Cecil and Gupta [9] discussed the potential of experiential activities involving MR to teach advanced concepts in cyber-physical systems, specifically focusing on astronaut training scenarios derived from NASA’s Moon Mission. Gangabissoon et al. [18] investigated the application of AR in aviation, particularly in improving engagement during emergency procedures training for cabin crew. And a VR disaster training system was proposed in [35] for improving disaster awareness, focused explicitly on fire disasters. In this category, it is important to clarify that the XR training scenarios contained elements mostly related to the engagement and/or attention of the trainees.

In summary, the literature underscores the value of XR integration in educational settings for improved learning outcomes. Attention is globally less studied than engagement, and most learning applications that were interested in attention were related to primary, secondary, and higher education, respectively. Furthermore, our findings highlight the XR’s dual role in enhancing E/A across educational levels, offering valuable insights into educational contexts.

## 4.5 Learning Fields

This category captures the different fields or domains of learning that were implemented and evaluated. The classification of the fields of education was done according to the International Standard Classification of Education, a statistical framework for organising information on education maintained by the United Nations Educational, Scientific and Cultural Organization (UNESCO) [51]. The classification includes various categories specific to the research context, such as STEM (Science, Technology, Engineering, and Mathematics), Education, Arts & Humanities, Social Sciences, Agriculture, Forestry, Fisheries and Veterinary, Business, Administration, Law, and other relevant categories. We also used “Others” to group those that did not fit into these predefined options (Fig. 5).

The analysis of the papers within the category revealed one prominent area of focus on STEM disciplines, with the three subcategories together making 48.48% of the articles. In particular, STEM Basic Sciences with Natural Sciences, Mathematics, and Statistics, received significant attention, representing ten articles,
which accounts for approximately 30.30%. Additionally, the Arts & Humanities categories, along with Education and STEM Engineering, Manufacturing, and Construction categories, also played a notable role, comprising a total of 13 articles, which represents approximately 39.39% of the total count. These insights emphasise the multidisciplinary nature of XR applications in learning, encompassing both STEM- and Social Sciences-related fields.

<figure><img src="image_7.png" /><figcaption>Fig. 5: Number of articles classified by learning fields and by cognitive focus.</figcaption></figure>

In STEM disciplines, an example includes Geller *et al.* [19] investigated the effectiveness of MR presentations in enhancing student engagement and reading comprehension. Their study demonstrated improvements in engagement metrics reading with MR compared to traditional portable document formats. Rebollo *et al.* [41] focused on integrating entertainment and AR to engage children in mathematics learning. Their study showed that gamified experiences through AR can make learning multiplication more enjoyable and attractive.

In Arts & Humanities as well as in Education, Wen and Lau [55] investigated the effects of AR on cognitive engagement in language learning, demonstrating significant improvements in students' cognitive engagement through the integration of AR activities. Similarly, Topsakal and Topsakal [50] explored how AR apps could motivate and engage preschool kids in foreign language learning activities. Their findings highlighted the potential of AR in enhancing engagement and learning outcomes in language education. To summarise, we found that many XR applications focused on the learning of languages, especially foreign languages, via exciting and novel activities on AR interfaces.

## 4.6 Targeted Users

In this category, we looked at the targeted learners of the selected XR systems, especially whether they were designed and developed only for one single learner
<figure><img src="image_8.png" /><figcaption>Fig. 6: Number of articles classified by the number of targeted users and grouped by the type of XR devices (left) and cognitive focus (right).</figcaption></figure>

or multiple ones. We further classified them into three categories: single user, multiple users (without specific collaboration mechanism between them), and collaborative users (the learners often worked in a group or team towards a common goal). From the results in Fig. 6, we found that the majority of the studies, comprising approximately 60.61%, focused on single user. XR applications for multiple users (without actual collaboration) represented 33.33% of studies, and systems for collaborative users were about 6.06%.

The last category of collaborative users took up a relatively small proportion (two of the total articles) but they promoted important aspects of deeper learning in groups. For instance, Southgate [46] highlighted the collaborative and engaging potential of networked virtual environments in education, where students worked together to research, create, and explain body organ models. This approach fostered teamwork and collaborative skills among the students. Kersting et al.'s study [24] identified various forms of collaborative engagement during a science festival, underscoring the potential for designing VR experiences that encourage interaction and engagement among visitors.

Crossing with the cognitive focus aspect on engagement and attention (Fig. 6 right), in the studies where multiple users have been considered, it was clear that engagement (seven out of 11 articles) has been studied significantly more than attention (only one article). On the contrary, in the studies on single users, attention was slightly more interested in compared to engagement. This result was not so surprising because XR learning applications for single users appeared earlier than for those of multiple users (Fig. 2), and thus the advances in XR technologies should have had enough maturity to allow researchers to evaluate not only the engagement aspect (mostly by using questionnaires) but also to capture the attention of the learners (via quantitative measures).

Regarding the XR devices used (Fig. 6 left), VR HMD and AR MD technologies were fairly distributed between single and multiple users. Another finding is that even for multiple user experiences, AR HMD devices were not considered for multiple and collaborative users. We hypothesise that in future research on collaborative learning, AR HMD technology could be more represented, especially for co-localised immersive learning.
To summarise, single-user experiences have been extensively studied and established. We believe that more contributions could be attributed to study the underrepresented field of collaborative immersive learning, looking at factors such as E/A in terms of cognitive focus and AR HMD in terms of devices used.

## 4.7 Teacher Intervention

We also considered whether there was any type of participation from the teachers integrated among all the selected immersive learning systems. We identified three types of intervention: without teacher, with virtual teacher(s), and with real ones. Out of the 33 studies, a substantial portion (21 studies out of 33) were conducted without a teacher, which accounts for approximately 63.64% (Fig. 7, left). This corroborates our previous observations about the fact that most of the systems provided autonomous learning activities for single users (20 out of 33 articles).

When there were teachers involved in the learning activities and user studies, they played different roles. In the study by Wen and Lau [55], the teachers selected student groups based on language proficiency, provided technical training, offered guidance during game activities, encouraged peer presentations, and actively contributed feedback on system design and pedagogical strategies. Similarly, in [50], the teachers administered the teaching sessions with the experimental group using the AR application and the control group using traditional flashcards. The researchers also collected feedback from the teachers through surveys regarding their experiences with AR and their observations of the children's learning processes and outcomes. Furthermore, the teachers shared the challenges faced and offered suggestions for enhancing the AR application's utility in the classroom. In [10], primary school teachers worked collaboratively to develop content featuring varying degrees of immersion and interactivity, exploring its impact on student engagement and learning. They also co-designed VR content for integration into their classroom curriculum.

In examining the role of teachers' intervention in XR for learning, we found limited presence (four out of 33). Notably, studies like [22] employed virtual assistants to guide the learners in AR applications, while the study in [35] utilised a character as an instructor during VR training, showcasing diverse approaches to virtual guidance. A unique contribution in [20] involved simulating a teacher-directed lesson in VR, further emphasising the potential of integrating virtual teaching roles.

Therefore, a global trend observed within this category is that teachers were strongly involved in the conception, design, and evaluation of learning activities. However, they were frequently excluded from the teaching activity itself within the immersive applications. For instance, in the study in [41], primary school teachers with over ten years of experience actively participated in designing the educational component, but the experiment itself was proceeded without direct teacher involvement. This confirms that the participation of teachers in learning activities within XR learning applications was often intentionally left out. It also raises the question of whether the lack of direct teacher intervention would
hinder the learning outcomes and if so, should the participation of real or even
virtual teacher(s) be considered more emphatically.

<figure><img src="image_9.png" /><figcaption>Fig. 7: Number of articles classified by the teacher intervention and grouped by targeted users (left). And the learning contexts grouped by devices used (right).</figcaption></figure>

## 4.8 Learning Context

In this category, we focus on the context in which the learning took place, either in the physical environment, in a virtual world, or both. Indeed, depending on the technology used for learning activities (i.e., VR or AR), the real environment could be integrated or not in the interaction. Moreover, if the real world was part of the learning activities in AR interfaces, we further considered whether the physical environment was a real classroom or just a generic environment. Solely from the design of virtual content in VR environments, we also took into account whether the researchers wanted to recreate virtual classrooms or the classroom scene was not examined. We had, therefore, four subcategories in this section: real classroom, non-classroom real context, virtual classroom, and non-classroom virtual context.

Figure 7, right, highlights some evident trends. Most of the learning tasks designed for non-classroom in the virtual world used VR HMD (11 out of 17 articles or 64.71%). Meanwhile, for the learning activities to be carried out in the non-classroom real world, the interfaces were developed mostly for AR MD (10 out of 12 articles or 83.33%). In this latter subcategory, it is noteworthy that none of these studies utilised AR HMD. This could be explained in several ways. It was mainly due to the robustness and affordability of handheld devices compared to AR HMD. Another explanation could be that the learning activities developed in these papers required the use of devices that also better supported reading and writing capability, or even multimedia playback and streaming activities.

In XR learning, there has been a constant comparison between different learn-
ing contexts, i.e., how learning in a virtual world could differ from traditional
learning in the real world. For example, the study in [35] divided participants
into two groups, one received training with real-world existing materials, while
the other used a VR system. The results indicated that this last group could start
earlier the task for which they were trained than those trained with traditional methods. User study in [11] studied the attention focus on users' performance of a physical motion task in VR and in the real world. They concluded that the group which received external focus cues (namely a vocal command) outperformed the control group (without external focus cues), in both VR and in the real world. More globally, these studies suggested that VR training had different results depending on several factors, and practitioners should be cautious when assuming that the learning performance in VR directly reflects real-world skills and knowledge.

In addition, we examined more closely the classroom context either in the real world or in virtual environments. Indeed, classroom structure was interesting to consider because it was the conventional environment that most of the students were used to going to school. Moreover, it could facilitate multiple-user interaction and collaboration. For instance, Sriworapong et al. [47] developed a 3D VR environment resembling a classroom, enabling students and educators to engage in activities such as presentations and discussions. This platform integrated features such as chat, voice, video, and whiteboards to promote student interaction and cooperation, fostering active participation and idea sharing. In [7], the students were equipped with HMDs and headphones with binaural sounds to create realistic 3D audio. They were seated in a virtual classroom, facing a blackboard displaying instructions. The collaborative experiment involved the students working in pairs or small groups to identify target voices and categorise animal names, promoting teamwork, communication, and problem-solving skills.

To conclude, the learning context in XR was constantly compared between the use of the real world and the virtual world to support the learning activities. Moreover, the design of individual and/or collective learning tasks may contribute to the final decision about the actual learning context and which XR technology should be employed.

## 4.9 Location Dependency

<figure><img src="image_10.png" /><figcaption>Fig. 8: Number of studies classified by location dependency and further grouped by the type of XR devices (left) and cognitive focus (right).</figcaption></figure>
Besides looking at the learning context designed for the real and/or virtual world, we also analysed where the XR learning experiences took place in the physical environment. We identified a distinction between location-dependent, when a physical location was necessary to carry out learning activities, and location-independent, when a specific location was not required because the experience was entirely virtual.

When being crossed with the type of XR devices (Fig. 8 left), location-independent systems that could be installed and run almost everywhere mainly used VR technologies and thus primarily focused on VR learning experiences. On the other hand, learning experiences using AR technology (mainly with AR MD) tended to be more location-dependent (e.g. classroom, laboratory, or on-site activities).

When we compare the location dependency with the cognitive focus (Fig. 8 right), it appears that the papers targeting the “Independent XR experience” and “Lab experiment area” took up to 90% of the studies about attention (nine out of ten). It was basically the “at the learning area” setting in which most of the cognitive analyses mainly focused on engagement (five out of six papers). These findings gave us a clear relationship between XR technologies, location considerations, and the cognitive aspects of engagement and attention in learning scenarios.

# 5 Experimental Design and Evaluation of Engagement and Attention

## 5.1 Experimental Design

<figure><img src="image_11.png" /><figcaption>Fig. 9: Number of studies classified by the measurements of engagement/attention done either pre-, during-, and/or post-test and grouped by cognitive focus.</figcaption></figure>

**Pre, During and Post-Test** This subsection details the specific procedures and measurements conducted at three distinct stages of the selected user studies:
pre-test, during test, and post-test. It illustrates the methods of assessment, data
collection, and the timing of each phase, offering a comprehensive view of how
the user studies were structured.

From Fig. 9, it is notable that approximately 91% (30 out of 33) of the stud-
ies incorporated post-test evaluations, emphasising the importance of assessing
the learning outcomes after the XR experiences. In the case of studies that were
focused solely on attention, 70% (7 out of 10) employed during-test evaluations,
complemented by pre and post-tests, indicating a significant emphasis on mea-
suring attention during the development of the experiment. Amongst the 17
studies measuring engagement, 88.23% (15 out of 17) included post-test evalua-
tions, while 64.70% (11 out of 17) used during-test evaluation, with or without
pre- or post-tests. This suggests a predominant focus on post-test evaluations for
engagement. However, a growing trend acknowledged the importance of assess-
ing engagement dynamics during the learning experiment, especially given the
potential impact of virtual experiences on user cognitive states. A further dis-
cussion on the measurement methods of the cognitive focus of the experiments
will be presented in Section 5.2.

**Summative-Formative Evaluation** In the assessment of XR learning activ-
ities, two distinct evaluation approaches emerged: summative evaluation, which
aims to measure the overall impact of the final systems on learning experiences,
and formative evaluation as an ongoing process that incorporates timely assess-
ments and feedback to enhance program development. This subcategory offers
valuable insights into the evaluation methods employed throughout the software
development process. Notably, 18 out of 33 studies (54.55%) exclusively em-
ployed summative evaluation techniques. We could also add five more studies
that used both approaches. This highlighted a solid preference for summative
evaluation with final products for learning activities (Fig. 10, left). This obser-
vation aligns with our previous findings in pre-, during, and post-test categories,
emphasising the prevalence of evaluations at the end of the development process.

The choice between summative and formative evaluations did not appear to
correlate with the cognitive focus of the studies. With ten studies using formative
assessment (and five with both), it was essential to acknowledge that assessing
E/A might be challenging depending on the more or less real-time nature of
these cognitive aspects.

**Within and Between Experimental Design** Another factor that implies
how participants were involved in the XR learning experiments was within-
subjects or between-subjects or mixed experimental design (Fig. 10, right). A
within-subjects design involved the same participants experiencing multiple con-
ditions or interventions, providing a basis for comparing their effects from the
same individuals. Conversely, a between-subjects design used different groups of
participants, each exposed to a single condition or intervention. A mixed exper-
imental design with both within- and between-subjects at different stages of the
<figure><img src="image_12.png" /><figcaption>Fig. 10: Number of articles grouped by the cognitive focus (E/A) and classified by the summative/formative evaluation (left), and the experimental design within- and/or between-subjects (right).</figcaption></figure>

experiments could also be used. This subcategory sheds light on the experimental design choices and how they impacted the study outcomes and findings.

Amongst the studies using between-subjects design, engagement took the lead accounting for 54.54%, and attention also received notable interest with 27.27%. In the within-subjects design category, the majority of studies (55.56%) were primarily concerned with engagement. Therefore, whatever the experimental design, engagement was more studied than attention, as we already observed in most of all the categories. On the other hand, whatever the cognitive focus of the user studies, there were more than twice as many experiments based on between-subjects than within-subjects design. This ratio tripled for studies focusing only on attention. We can conclude that most of the studies use between-subjects design with 66.67% (22 out of 33), This implies that participants tend to be exposed to fewer variables and for less time in the experiment, which is key in XR studies to reduce cyber-cikness. On the other hand, as mentioned in Sec. 4.8, the studies also tend to divide participants into at least two groups to compare the learning context in the virtual world and the real world.

**Sample Size** To enable a fair comparison of the number of participants in different user studies, we normalised each experiment's sample size by dividing it by the number of conditions in the experiment. In the case of between-subjects experiment design, this normalisation helped to yield the average sample size per condition, as each group tests only one condition. In Figure 11 right, after the normalisation, we observed that 40.9% (nine out of 22) of the between-subjects experiments gathered less than 20 subjects (13.64% before normalisation). And 45.45% (ten out of 22) of the studies had from 20 to 60 participation (40.9% before normalisation). On the other hand, we observed that 55.56% (five out of nine) of the within-subjects design experiments involved less than 20 subjects (44.44% before normalisation). Moreover, 44.44% (four out of nine) of the selected studies gathered from 20 to 60 subjects (33.33% before normalisation).

Normalising the sample size data reveals that 12 studies with a sample size of more than 60 participants were down to only three, the number with less than 20 participants increased from seven to 15 studies. However, having more than 20
participants per condition was the common target if we wanted to compare future experiments with the best sample sizing used in these previous experiments on E/A.

<figure><img src="image_13.png" /><figcaption>Fig. 11: Number of articles classified by sample size of experiments and grouped by the experimental design before the normalisation (left) and after (right).</figcaption></figure>

To provide an overview of the experimental design of the selected studies, Figure 12 takes into account different aspects of the experiments and seeks to connect the cognitive focus (E/A) of all the studies with the measurement methods and instruments. Remarkably, in the figure we can observe that most of the studies used between-subjects design (22 out of 33), summative evaluation (18 out of 33), qualitative and quantitative methods (25 out of 33), and its relation with the evaluation methods like questionnaires, Eye tracking and others.

<figure><img src="image_14.png" /><figcaption>Fig. 12: Overview of experimental design and methods used in the user studies. We considered seven main features of the user studies: cognitive focus of experimental design; sample size; within and between subject design; summative or formative evaluation; pre, during and post-test; qualitative or quantitative evaluation; and evaluations measurements during the studies.</figcaption></figure>
## 5.2 Methods and Instruments of Engagement and Attention Measurement

This category relates to the research methods and instruments used for data collection and measurement of engagement and attention during the learning activities. To identify all the methods used by the studies for measurements, Table 1 lists all the relevant methods and classifies them by whether they were used to measure engagement and/or attention. The authors and year of publication are also listed. In some cases, authors may be repeated because they used more than one method in their paper. The table starts with the objective methods, where studies using eye tracking and other biosensors stood out, and then the subjective techniques based on questionnaires. The questionnaires were divided further into those that were used to measure exclusively the two factors E/A and the questionnaires that were not dedicated to measuring them but included their measurement in some of the items. Finally, other qualitative methods, such as observations and interviews were also mentioned.

Table 2 presents various methods combined to measure either engagement or attention. Each study utilised multiple methods, often combining objective and subjective approaches for a comprehensive assessment. ODCM1 [53] employed EEG and eye tracking approaches (heatmap, gaze plot, area of interest) to measure attention. ODCM2 [27] combined eye tracking with the area of interest data to measure attention but also employed semi-structured interviews for its evaluation. ODCM3 [47] combined a questionnaire with observational and interview methods for engagement evaluation. ODCM4 [10] integrated Electrodermal Activity (EDA) and Heart Rate Variability (HRV) for engagement measurement and utilised observations (video and screen recordings) for a holistic evaluation approach. While this table highlights the use of more than one method, either objective or subjective, to measure attention and engagement, it is noteworthy that these methods were combined but only to measure one cognitive aspect (either attention or engagement) in the studies.

In Table 3 we summarised different measures employed to assess both engagement and attention. More specifically, only studies that measured these two factors in the same study were considered, regardless of the method used or whether they were objective or subjective. The table is categorised using a three-colour scale, ranging from darker to lighter shades based on the quality of measurements.

For instance, in the case of engagement, the darkest shade corresponds to the User Engagement Scale (UES) in BDCM1 [22] and BDCM2 [12], which appears to be a scientifically validated tool, as it was based on a dedicated study specifically creating a scale for measuring user engagement. In a medium shade, there was for example eye tracking using area of interest, which was used to measure engagement. However, as mentioned earlier, this approach may not be sufficient. In the lightest shade, we have BDCM6, which also relied on surveys during pre- and post-tests, making it prone to a high degree of subjectivity. Additionally, there is BDCM4, which used an insufficiently robust questionnaire.
<table><caption>Table 1: Methods for measuring engagement and/or attention in all the selected user studies.</caption><thead><tr><th>ID</th><th>Methods</th><th>Engagement Objective methods</th><th>Attention</th><th>Authors & Year</th></tr></thead><tbody><tr><td>EDA 1</td><td>Electrodermal (EDA) Activity</td><td>Electrodermal (EDA) Activity</td><td></td><td>(Chua et al., 2019)</td></tr><tr><td>HRV 1</td><td>Heart Rate Variability (HRV)</td><td>Heart Rate Variability (HRV)</td><td></td><td>(Chua et al., 2019)</td></tr><tr><td>EEG 1</td><td>Electroencephalography (EEG)</td><td></td><td>EEG signals</td><td>(Bos, 2019)</td></tr><tr><td>EEG 2</td><td>Electroencephalography (EEG)</td><td></td><td>EEG- Neural oscillations</td><td>(Krassmann et al., 2020)</td></tr><tr><td>EEG 3</td><td>Electroencephalography (EEG)</td><td></td><td>EEG - Score Attention estimation</td><td>(Wang & Chao, 2022)</td></tr><tr><td>ET 1</td><td>Eye tracking</td><td></td><td>Eye tracking - Area Of Interest</td><td>(Wang & Chao, 2022), (Ramkumar et al., 2019), (Yang, 2023), (Liu, 2022)</td></tr><tr><td>ET 2</td><td>Eye tracking</td><td></td><td>Eye tracking - Heatmap</td><td>(Wang & Chao, 2022)</td></tr><tr><td>ET 3</td><td>Eye tracking</td><td></td><td>Eye tracking - Gaze plot</td><td>(Wang & Chao, 2022)</td></tr><tr><td>ET 4</td><td>Eye tracking</td><td></td><td>Eye tracking - Objects Of Interest</td><td>(Hasenbein, 2022)</td></tr><tr><td>M1</td><td>Measurement-Metrics</td><td></td><td>Attention Score (ATNS), Maximum Attention Span Score (MASS)</td><td>(Bhanushali, 2022)</td></tr><tr><td>M2</td><td>Measurement-Metrics</td><td></td><td></td><td>(Bhanushali, 2022)</td></tr><tr><td colspan="5" style="text-align: center; font-weight: bold;">Subjective methods - Engagement-Dedicated Questionnaires (EDQ)</td></tr><tr><td>EDQ1</td><td>Questionnaire</td><td>User Engagement Scale (UES)</td><td>Some items are focusing on attention</td><td>(Katika et al., 2022), (Sriworapong et al., 2022)</td></tr><tr><td>EDQ2</td><td>Questionnaire</td><td>User Engagement Scale - Short Form (UES-SF)</td><td></td><td>(Sanchez, 2019)</td></tr><tr><td>EDQ3</td><td>Questionnaire</td><td>Perceptions of engagement (UES adapted)</td><td>Some items are focusing on attention</td><td>(Conley, 2020)</td></tr><tr><td>EDQ4</td><td>Questionnaire</td><td>The scale assessing student engagement</td><td></td><td>(Wang & Sun, 2021)</td></tr><tr><td>EDQ5</td><td>Questionnaire</td><td>ICAP (Inter-active, constructive, Active or Passive)</td><td></td><td>(Wen, 2021)</td></tr><tr><td>EDQ6</td><td>Questionnaire</td><td>Positive Engagement Evaluation Model (PEEM)</td><td></td><td>(Gangabissoon, 2020)</td></tr><tr><td>EDQ7</td><td>Questionnaire</td><td>Taxonomy of student engagement</td><td></td><td>(Mystakidis, 2020)</td></tr><tr><td colspan="5" style="text-align: center; font-weight: bold;">Subjective methods - Engagement Included in Questionnaires (EIQ)</td></tr><tr><td>EIQ1</td><td>Questionnaire</td><td>Questionnaire User experience</td><td></td><td>(Bhanushali et al., 2022)</td></tr><tr><td>EIQ2</td><td>Questionnaire</td><td>Presence Questionnaire (IICC-SOPI)</td><td></td><td>(Krassmann et al., 2020)</td></tr><tr><td>EIQ3</td><td>Questionnaire</td><td>Web-based Learning Tools (WBLT)</td><td></td><td>(Allcoat, 2018)</td></tr><tr><td colspan="5" style="text-align: center; font-weight: bold;">Subjective methods - Attention-Dedicated Questionnaires (ADQ)</td></tr></tbody></table>
<table><caption>Table 2: Combined methods for measuring engagement or attention.</caption><tbody><tr><td>ADQ1</td><td>Questionnaire</td><td>Attention scale for elementary school children</td><td>(Tarng, 2022)</td></tr><tr><td>ADQ2</td><td>Questionnaire</td><td>Questionnaire - Focused attention scale</td><td>(Wang & Sun, 2021)</td></tr><tr><td colspan="4" style="background-color: #cccccc; text-align: center; font-weight: bold;">Subjective methods - Attention Included in Questionnaires (AIQ)</td></tr><tr><td>AIQ2</td><td>Questionnaire</td><td>Instructional Materials Motivation Survey (IMMS)</td><td>(Ooi, 2019)</td></tr><tr><td colspan="4" style="background-color: #cccccc; text-align: center; font-weight: bold;">Other subjective methods</td></tr><tr><td>O1</td><td>Other</td><td>Observations, Interviews</td><td>(Sriworapong et al., 2022)</td></tr><tr><td>O2</td><td>Other</td><td>Observations (video and screen recordings)</td><td>(Chua et al., 2019)</td></tr><tr><td>O3</td><td>Other</td><td>Surveys and observations</td><td>(Topsakal & Topsakal, 2019)</td></tr><tr><td>O4</td><td>Other</td><td>Semi-structured interviews</td><td>(Liu et al., 2019)</td></tr><tr><td>O5</td><td>Other</td><td>Video recordings, screen captures, and field notes</td><td>(Wen, 2021)</td></tr></tbody></table>

<table><thead><tr><th>ID</th><th>Engagement</th><th>Attention</th><th>Authors & Year</th></tr></thead><tbody><tr><td>ODCM1</td><td></td><td>EEG - Score Attention estimation + Eye tracking - Heatmap, Gaze plot, Area of Interest.</td><td>(Wang & Chao, 2022)</td></tr><tr><td>ODCM2</td><td></td><td>Eye tracking - Area of Interest + Semi-structured interviews</td><td>(Liu et al., 2019)</td></tr><tr><td>ODCM3</td><td>User Engagement Scale (UES) + Observations and Interviews</td><td></td><td>(Sriworapong et al., 2022)</td></tr><tr><td>ODCM4</td><td>Electrodermal Activity (EDA) + Heart Rate Variability (HRV) + Observations (video - screen recordings)</td><td></td><td>((Chua et al., 2019))</td></tr></tbody></table>
<table><caption>Table 3: Combined methods for measuring both engagement and attention.</caption><thead><tr><th>ID</th><th>Methods</th><th>Engagement</th><th>Attention</th><th>Authors &amp; Year</th></tr></thead><tbody><tr><td>BDCM1</td><td>Questionnaire (EDQ1)</td><td>User Engagement Scale (UES)</td><td>Attention measured through User Engagement Scale (UES)</td><td>(Katika et al., 2022)</td></tr><tr><td>BDCM2</td><td>Questionnaires (EDQ3)</td><td>Adapted User Engagement Scale (UES)</td><td>Attention measured through Adapted User Engagement Scale (UES)</td><td>(Conley et al., 2020)</td></tr><tr><td>BDCM3</td><td>Eye tracking (ET1)</td><td>Eye tracking- Area Of Interest</td><td>Eye tracking- Area Of Interest</td><td>(Ramkumar et al., 2019)</td></tr><tr><td>BDCM4</td><td>Questionnaire (EIQ1) +<br>Measurement Metrics (M1+M2)</td><td>Questionnaire User experience</td><td>Attention Score (ATNS),<br>Maximum Attention Span Score (MASS)</td><td>(Bhanushali et al., 2022)</td></tr><tr><td>BDCM5</td><td>Questionnaires (EDQ4 + ADQ2)</td><td>Questionnaire- Scale assessing student engagement</td><td>Questionnaire - Focused attention scale</td><td>(Wang &amp; Sun, 2021)</td></tr><tr><td>BDCM6</td><td>Others (O3)</td><td>Surveys during pre- and post-test</td><td>Surveys and Observations</td><td>(Topsakal &amp; Topsakal, 2019)</td></tr></tbody></table>
On the other hand, concerning attention, within the darker category, three objective measures stood out. In BDCM3, we find Eye Tracking - Area Of Interest. BDCM4 also stands out in this category, featuring two metrics derived from Eye Tracking with the explicit goal of creating an attention scale. Moving to the medium shade, we encounter BDCM1 and BDCM5, which are both questionnaires that include questions related to attention. However, they may not be robust enough to measure attention effectively. Additionally, BDCM5 assesses attention through observations.

To conclude, even though there were methods classified in lighter colours, when combined with other approaches, they could contribute to a more comprehensive methodology for simultaneously measuring engagement and attention. This observation suggests that using a combination of two or more of these methods could be appropriate for future research.

## 5.3 Approaches for Engagement and/or Attention Enhancement

We identified and grouped the approaches used in the experiments into two main categories: using user-centred design (Table 4) and pedagogical approach (Table 5) to enhance either engagement (white), attention (light grey), or both (dark grey).

<table><caption>Table 4: Approaches to enhance engagement and/or attention using user-centred design.</caption><thead><tr><td>User-centred design</td><td>Device</td><td>Authors &amp; Year</td></tr></thead><tbody><tr><td>Whole body simulation</td><td>VR CAVE</td><td>(Lindgren et al., 2016)</td></tr><tr><td>Video see-through guide</td><td>AR MD</td><td>(Aitamurto et al., 2018)</td></tr><tr><td>Overlay information on real-world</td><td>AR MD</td><td>(Gangabissoon et al., 2020)</td></tr><tr><td>Textual and visual cues</td><td>VR HMD</td><td>(Liu et al., 2022)</td></tr><tr><td>Point-of-interest attention</td><td>VR HMD</td><td>(Bhanushali et al., 2022)</td></tr><tr><td>Visual feedback</td><td>VR HMD</td><td>(Breuer et al., 2022)</td></tr><tr><td>3D visualisations</td><td>AR MD</td><td>(Yang et al., 2023)</td></tr><tr><td>User visual behaviour</td><td>AR HMD</td><td>(Ramkumar et al., 2019)</td></tr><tr><td>Multimedia and 3D objects</td><td>AR MD</td><td>(Katika et al., 2022)</td></tr></tbody></table>

The first category involves the user-centred design approach which focuses on tailoring the immersive experience to the user's needs and preferences. A noteworthy study in [27] highlighted the effects of textual and visual cues on attention in immersive 360 videos using VR HMD. Additionally, the work in [59] explored how AR MD enhanced attention by incorporating dynamic 3D visualisations into instructional content. Another significant study in [22] showcased how XR interactions in AR MD, including object manipulation and gamification, contributed to enhancing engagement. These experiments underscored the potential of user-centred design in XR to create dynamic, interactive learning environments that significantly impacted the learners' focus and involvement.

The second category focuses more on the pedagogical approach, which plays a central role in integrating teaching strategies to create engaging and inter-
active learning experiences that are tied to specific educational levels. Wen's work [54] integrated different pedagogical designs with AR for language learning while examining cognitive outcomes. Topsakal and Topsakal [50] used language learning with AR and compared it with traditional approaches, emphasising the pedagogical impact on attention and engagement. Meanwhile, Tarng et al. [49] developed a study for attention training in primary-secondary schools with VR games based on a clinical model, highlighting the fusion of pedagogical strategies and VR for heightened attention. Collectively, these studies showcase the transformative potential of combining XR user-centred design with a pedagogical approach and the role that the use of gaming mechanisms in diverse educational contexts can play.

Indeed, in XR-based education, gamification emerges as a dynamic approach that seamlessly integrates with both user-centred design and pedagogical approaches. Games not only enhance engagement through immersive experiences but also serve as interactive teaching tools. Examining specific examples from the literature, Breuer et al. [7] incorporated gamification elements like visual feedback. Sanchez et al. [44] explored a mobile AR pervasive game in a university context, showcasing the versatility of games in diverse educational settings. Additionally, Rebollo et al. [41] piloted two mini-games designed for mobile platforms, evaluating their learning effectiveness and usability. And Wen's study [54] used an AR-supported Chinese character learning game for young learners, investigating its impact on cognitive engagement in classroom learning. Together, these examples show the potential role of games as a versatile and effective approach to enhance engagement and attention in XR learning scenarios.

<table><caption>Table 5: Approaches to enhance engagement and/or attention using pedagogical methodology.</caption><thead><tr><td>Pedagogical approach</td><td>Educational level</td><td>Authors &amp; Year</td></tr></thead><tbody><tr><td>Cognitive learning analysis</td><td>Primary-Secondary school</td><td>(Wen, 2021)</td></tr><tr><td>Educational mini-games</td><td>Primary-Secondary school</td><td>(Rebollo et al., 2022)</td></tr><tr><td>Learning with interactive activity</td><td>Primary-Secondary school</td><td>(Chua et al., 2019)</td></tr><tr><td>Learning comprehension</td><td>Primary-Secondary school</td><td>(Li et al., 2013)</td></tr><tr><td>Context-based for engagement</td><td>Higher education</td><td>(Hawes &amp; Arya, 2022)</td></tr><tr><td>University pervasive game</td><td>Higher education</td><td>(Sanchez et al., 2019)</td></tr><tr><td>Interactive learning materials</td><td>Higher education</td><td>(Allcoat &amp; Mühlenen, 2018)</td></tr><tr><td>Interactive scientific articles</td><td>Higher education</td><td>(Geller et al., 2021)</td></tr><tr><td>Experiential learning activities</td><td>Higher education</td><td>(Cecil &amp; Gupta, 2021)</td></tr><tr><td>VR at a science festival</td><td>Other</td><td>(Kersting et al., 2021)</td></tr><tr><td>Learning experiences in classroom</td><td>Primary-Secondary school</td><td>(Hasenbein et al., 2022)</td></tr><tr><td>Training games</td><td>Primary-Secondary school</td><td>(Tarng et al., 2022)</td></tr><tr><td>Remote learning methods</td><td>Higher education</td><td>(Wang &amp; Chao, 2022)</td></tr><tr><td>Training in real scenarios</td><td>Higher education</td><td>(Ooi et al., 2019)</td></tr><tr><td>Lessons plans for teaching</td><td>Preschool</td><td>(Topsakal &amp; Topsakal, 2019)</td></tr><tr><td>Co-creation of classes</td><td>Primary-Secondary school</td><td>(Wang &amp; Sun, 2021)</td></tr><tr><td>Statistical learning with AR</td><td>Higher education</td><td>(Conley et al., 2020)</td></tr></tbody></table>
# 6 Discussion

The review investigated the methods for measuring the users' E/A and some approaches to enhance them in XR learning environments. The following subsections detail our findings for the three RQs mentioned in Sec. 1.

## RQ1: What are the focuses of existing studies on engagement and attention in XR learning scenarios?

Our literature review has shown that understanding the research focuses of user E/A in XR learning scenarios was crucial for enhancing educational experiences. Most of the findings useful to answer this question are based on the analysis performed in Section 4 and 5.1.

Regarding the educational level of the participants within the studies, a clear trend emerged, with 50% of articles on higher education primarily exploring engagement and 33.33% delving into attention. This pattern was consistent in the primary-secondary school category, highlighting an emphasis on the exploration of engagement over attention. For comparison within learning fields, STEM categories were dominated by engagement-centred studies (66.67%), and only 26.67% of studies dedicated to attention. In terms of the devices used across the experiments, VR HMD was most often used for attention studies, whereas AR (HMD+MD) was prioritised in engagement studies. Notably, the limited works addressing both E/A in AR HMD for educational purposes deserve more research attention.

Exploring the targeted users, studies involving multiple users prioritised engagement (seven out of 11), revealing a noteworthy gap in attention-focused studies in scenarios with multiple or collaborative users. In contrast, scenarios with single learners exhibited a balanced consideration of both cognitive focuses. The data shows that a large number of research studies (20 out of 33 papers) were related to single-user activities, and it was the same number of studies within the systems that did not involve any teacher, which means doing individual learning. When considering the teacher-intervention factor, half of the papers without teacher intervention exhibited a pronounced interest in attention (ten papers out of 20). It was aligned with the observed slight bias towards attention in studies on single learners, emphasising the intricate interplay between teacher intervention and the detailed examination of the users' attention and engagement.

Answering RQ1, our analysis reveals a clear focus on engagement, especially in higher education and STEM fields. The popularity of VR HMD for attention studies and the scarcity of research on attention in collaborative settings [28] suggest more future exploration. From the perspective of cognitive states, the studies on attention suggest that it is closely linked to perception. It plays an important role as a fundamental cognitive process that enables learners to concentrate their cognitive resources on their tasks to improve attention to new information, as emphasized by Plebanek and Sloutsky [38]. Engagement, on the other hand, results from a cognitive situation favouring the motivation of the subjects, such
as games, scenarios, and interaction, similar to Dow's statement [14]. This clarification enriches our understanding of the factors influencing the users' E/A in XR learning scenarios, answering RQ1 and setting the stage for further discussions on effective methods for measuring learner E/A (RQ2) and approaches that improve these aspects (RQ3). The interconnectedness of these topics adds depth to our understanding of XR learning scenarios.

**RQ2: What are the most effective methods that have been used for measuring students' engagement and attention in immersive learning environments?**

Exploring different methods for measuring the learners' E/A in XR learning scenarios revealed diverse approaches. The findings used to answer this question are based on the results collected in Section 5.2.

In the context of the moment when the E/A were evaluated, 91% of studies included post-test evaluations, highlighting a common practice of measuring the E/A after the end of learning activities. Attention-only evaluations (70% out of 10) during the experiment, supplemented with pre-and/or post-tests, underscored the dynamic nature of attention during learning activities. A growing trend leaned towards incorporating real-time observations of engagement during the learning experiment. Another instrument to evaluate these cognitive states from the learning sciences approach was the summative and/or formative evaluation. 54.55% of studies exclusively favoured summative evaluation techniques. This preference for post-experience assessment aligned with the observed emphasis on post-test evaluations, reinforcing the vital role of summative evaluations in XR learning scenarios.

Regarding other issues of the experiment design, in the configuration of within- and between-subjects studies, engagement took the lead, commanding 54.54% of between-subjects studies, attention also gained notable interest with 27.27% of them, this pattern persisted similarly in within-subjects experimental design. It has a clear relation with the participants and its division in the variables of the studies, given only a few multiple-user studies and collaborative experiences (just 6.06%), it might be due to the challenge of measuring E/A during collaborative learning.

The deployment of methods to measure either engagement or attention reflects a limited approach. Also, various combined methods targeted either attention or engagement, utilising diverse technologies and questionnaires in the same study [53,27,10]. Notably, these methods contributed to the growing options for assessing learners' attention and engagement, but a gap remains, few studies integrated both cognitive states simultaneously.

In contrast, exploring combined methods to evaluate both engagement and attention presented promising avenues. Scientifically validated tools such as the User Engagement Scale (UES) [22,12] and the integration of multiple physiological signals emphasised a more holistic approach [40,5]. However, challenges persisted when some methods still lacked robustness in effectively measuring both, especially in real-time, which makes it difficult to find a correlation between
them. The quest for optimal tools that comprehensively capture both attention and engagement remains a focal point for future research in XR learning scenarios. These findings lay the foundation for addressing the broader question of how these methods contributed to improving the learners' E/A (RQ3).

**RQ3: What approaches have been proposed to enhance the students' engagement and attention in XR learning? How effective are they?**

We explored different strategies to enhance the learners' engagement and attention in XR learning scenarios and two primary approaches emerged: user-centred designs and pedagogical approaches. The findings are based on the results collected in Section 5.3.

The user-centred design approach employed a diverse set of strategies to enhance students' engagement and attention in XR learning environments. Techniques such as the use of whole-body simulation and video see-through guides focused on immersively and physically engaging experiences [26,1]. Overlaying information in the real world, incorporating textual and visual cues, and utilising point of interest attention quantification demonstrated a concerted effort to guide learners' attention effectively [18,27]. The integration of 3D visualisation and multimedia elements (e.g. audio feedback), added depth and interactivity to the learning experience [7,59,22]. It is crucial to note that the effectiveness of these approaches was influenced by the XR device employed, emphasising the need for tailored strategies based on the specific device used in XR experiments.

The pedagogical approach highlighted teaching strategies designed to elevate the students' engagement and attention in XR learning scenarios. To enhance these E/A cognitive states of learners, the main strategies we found are: (i) the utilization of educational XR games to expand the horizon and emphasise immersive learning experiences beyond traditional classrooms, injecting elements of play into the learning experience [41,44,49]; (ii) the use of interactive learning materials that provided dynamic content to captivate learners [3,19]; and (iii) incorporating lesson plans for teaching ensured a structured approach to XR integration into learning [50,52]. These examples demonstrate the versatility of XR to diverse educational needs, with careful consideration of target audiences and educational levels.

Both approaches underscored the role of technology in shaping engaging XR learning experiences. The user-centered design prioritizes personalized, immersive content delivery, while the pedagogical approach emphasizes interactive and informative teaching strategies. This intersection holds promise for creating immersive, effective, and engaging learning experiences across diverse educational settings. Furthermore, incorporating gamification elements, as observed in various XR learning applications, adds elements to both approaches. Moving forward, it is crucial to propose avenues of research not only to enhance attention and engagement but also to recover them. For instance, exploring methods to reduce distractions or mitigate the impact of distractors during learning can significantly contribute to attention improvement. As we delve into these approaches,
future research should continue exploring synergies between user-centered design elements and pedagogical strategies. This convergence offers the potential for optimizing XR learning environments further, ensuring their effectiveness and engagement across various educational contexts.

# 7 Conclusion

The literature review explored the dynamics of users' engagement and attention within eXtended Reality (XR) learning scenarios. Our analysis revealed several key findings that revealed the predominant focus on engagement, particularly in higher education educational level and STEM fields. Notably, the use of Virtual Reality Head-Mounted Displays (VR HMD) gained prominence in attention-centred studies, while Augmented Reality Head-Mounted Displays and Mobile Devices (AR HMD+MD) setups were important in engagement-focused investigations. The interplay of teacher intervention and the detailed examination of user attention and engagement underscored their diverse landscape in educational settings. Furthermore, our exploration of research designs, sample sizes, and evaluation methods provided a comprehensive understanding of the current trends in XR learning research. These findings are relevant to educators, researchers, and developers seeking to optimize XR technologies for enhanced educational outcomes, thus contributing to the ongoing discourse on immersive learning environments.

The significance of our findings lies in the analysis of varied methods employed for measuring engagement and attention. The prevalence of post-test evaluations and summative assessment techniques indicates a common practice, emphasizing the need for comprehensive evaluations after the XR learning experience. However, the diverse approaches to research designs, including within-subjects and between-subjects studies, highlighted the dominance of engagement studies. Identifying combined methods for attention or engagement contributes to understanding learners' cognitive states. It is essential to reflect on how engagement impacts attention and vice versa. Additionally, suggesting research directions for improving attention and engagement, like reducing distractions while learning, aligns with the identified research gap. This emphasizes the need for experiments evaluating both engagement and attention, considering the pedagogical and technological aspects of the learning experience and guiding our future research.

While our study provided valuable insights, it is essential to acknowledge its limitations. Our focus on English-only and computer science-focused outlets may have overlooked valuable perspectives from pure educational papers published in other languages and journals. From the results, the concentration on higher education and STEM fields, and studies targeted for single users may limit the generalisability of our findings to other educational domains and for collaborative settings. Additionally, the diverse methods used for measurement, coupled with the limited integration of both attention and engagement in the same study, suggests a need for more holistic approaches. Future research should
aim to bridge these gaps and explore the effectiveness of interventions, such as user-centred design and pedagogical approaches, in enhancing learner attention and engagement across various educational contexts. By addressing these limitations, future studies can contribute to refining and optimising XR learning environments, ensuring their effectiveness in fostering cognitive engagement and learning outcomes.

**Acknowledgments.** This work was supported by French government funding managed by the National Research Agency (ANR) under the Investments for the Future program (PIA) with the grant ANR-21-ESRE-0030 (CONTINUUM project) and the grant ANR-23-CE33-0008-01 (MuseoXR project).

## References

1. Aitamurto, T., Boin, J.B., Chen, K., Cherif, A., Shridhar, S.: The impact of augmented reality on art engagement: Liking, impression of learning, and distraction. In: Chen, J.Y., Fragomeni, G. (eds.) Virtual, Augmented and Mixed Reality: Applications in Health, Cultural Heritage, and Industry. pp. 153–171. Springer International Publishing, Cham (2018)

2. Al Breiki, M., Wan Yahaya, W.A.J.: Using Gamification to Promote Students’ Engagement While Teaching Online During COVID-19. In: Teaching in the PostCOVID-19 Era, pp. 443–453. Springer (2022)

3. Allcoat, D., von Mühlenen, A.: Learning in virtual reality: Effects on performance, emotion and engagement **26**(0) (2018)

4. Azevedo, R.: Defining and Measuring Engagement and Learning in Science: Conceptual, Theoretical, Methodological, and Analytical Issues. Educational Psychologist **50**(1), 84–94 (2015)

5. Bhanushali, J., John, A.S., Muniyadi, M.: Attention score: Objective measure of attentioniveness in immersive omnidirectional videos. In: 2022 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR). pp. 163–170 (2022)

6. Bozkir, E., Stark, P., Gao, H., Hasenbein, L., Hahn, J.U., Kasneci, E., Göllner, R.: Exploiting Object-of-Interest Information to Understand Attention in VR Classrooms. In: 2021 IEEE Virtual Reality and 3D User Interfaces (IEEE VR). pp. 597–605 (2021)

7. Breuer, C., Loh, K., Leist, L., Fremerey, S., Raake, A., Klatte, M., Fels, J.: Examining the Auditory Selective Attention Switch in a Child-Suited Virtual Reality Classroom Environment. International Journal of Environmental Research and Public Health **19**(24), 1–20 (2022)

8. Carroll, M., Lindsey, S., Chaparro, M.: Integrating Engagement Inducing Interventions into Traditional, Virtual and Embedded Learning Environments. In: Adaptive Instructional Systems. pp. 263–281. Springer (2019)

9. Cecil, J., Gupta, A.: An Experiential Approach to Support Learning of Cyber Physical Systems Concepts involving Mixed Reality Platforms. In: 2021 IEEE Frontiers in Education Conference (FIE). pp. 1–6 (2021)

10. Chua, Y., Sridhar, P.K., Zhang, H., Dissanayake, V., Nanayakkara, S.: Evaluating IVR in Primary School Classrooms. In: 2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). pp. 169–174. Auckland, New Zealand (2019)
11. Cochran, S.M., Aiken, C.A., Rhea, C.K., Raisbeck, L.D.: Effects of an External Focus of Attention and Target Occlusion on Performance in Virtual Reality. Human Movement Science **76**, 1–8 (2021)

12. Conley, Q., Atkinson, R.K., Nguyen, F., Nelson, B.C.: MantarayAR: Leveraging Augmented Reality to Teach Probability and Sampling. Computers & Education **153**, 1–22 (2020)

13. Cruz-Neira, C., Sandin, D.J., DeFanti, T.A., Kenyon, R.V., Hart, J.C.: The Cave - Audio Visual Experience Automatic Virtual Environment. Communications of the ACM **35**(6), 64–72 (1992)

14. Dow, S., Mehta, M., Harmon, E., MacIntyre, B., Mateas, M.: Presence and Engagement in an Interactive Drama. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. pp. 1475–1484. CHI ’07, ACM (2007)

15. El-Sabagh, H.A.: Adaptive e-Learning Environment based on Learning Styles and its Impact on Development Students’ Engagement. International Journal of Educational Technology in Higher Education **18**(1), 53 (2021)

16. Fredricks, J.A., Blumenfeld, P.C., Paris, A.H.: School Engagement: Potential of the Concept, State of the Evidence. Review of Educational Research **74**(1), 59–109 (2004)

17. Freina, L., Ott, M.: A Literature Review on Immersive Virtual Reality in Education: State of the Art and Perspectives. In: Proceedings of the 11th International Scientific Conference on eLearning and Software for Education (eLSE). pp. 133–141. Bucharest, Romania (2015)

18. Gangabissoon, T., Bekaroo, G., Moedeen, W.: Application of Augmented Reality in Aviation: Improving Engagement of Cabin Crew during Emergency Procedures Training. In: Proceedings of the 2nd International Conference on Intelligent and Innovative Computing Applications. ICONIC ’20, ACM (2020)

19. Geller, P., Stein, J., Du, D., et al.: Impact of Mixed Reality Presentation on STEM Engagement and Comprehension: A Pilot Study on Adult Scientists. Biomedical Engineering Education **1**, 277–290 (2021)

20. Hasenbein, L., Stark, P., Trautwein, U., Queiroz, A.C.M., Bailenson, J., Hahn, J.U., Göllner, R.: Learning with Simulated Virtual Classmates: Effects of Social-related Configurations on Students’ Visual Attention and Learning Experiences in an Immersive Virtual Reality Classroom **133**, 1–22 (2022)

21. Juanes, J.A., Hernández, D., Ruisoto, P., García, E., Villarrubia, G., Prats, A.: Augmented Reality Techniques, Using Mobile Devices, for Learning Human Anatomy. In: Proceedings of the Second International Conference on Technological Ecosystems for Enhancing Multiculturality. pp. 7–11. TEEM ’14, ACM (2014)

22. Katika, T., Karaseitanidis, I., Tsiakou, D., et al.: Augmented Reality (AR) Supporting Citizen Engagement in Circular Economy. Circular Economy and Sustainability **2**, 1077–1104 (2022)

23. Kavanagh, S., Luxton-Reilly, A., Wuensche, B., Plimmer, B.: A Systematic Review of Virtual Reality in Education. Themes in Science and Technology Education **10**(2), 85–119 (Dec 2017)

24. Kersting, M., Steier, R., Venville, G.: Exploring Participant Engagement during an Astrophysics Virtual Reality Experience at a Science Festival. International Journal of Science Education, Part B **11**(1), 17–34 (2021)

25. Krassmann, A.L., Herpich, F., Tarouco, L.M.R., Bercht, M.: Investigating the Relation Between Sense of Presence, Attention and Performance: Virtual Reality Versus Web. In: Stephanidis, C., et al. (eds.) HCI International 2020 – Late Breaking Papers: Cognition, Learning and Games. Lecture Notes in Computer Science, vol. 12425, pp. 107–114. Springer (2020)
26. Lindgren, R., Tscholl, M., Wang, S., Johnson, E.: Enhancing Learning and Engagement through Embodied Interaction within a Mixed Reality Simulation. Computers & Education **95**, 174–187 (2016)

27. Liu, R., Xu, X., Yang, H., Li, Z., Huang, G.: Impacts of Cues on Learning and Attention in Immersive 360-Degree Video: An Eye-Tracking Study. Frontiers in Psychology **12**, 1–11 (2022)

28. Marques, B., Silva, S., Alves, J., Araújo, T., Dias, P., Santos, B.S.: A conceptual model and taxonomy for collaborative augmented reality. IEEE Transactions on Visualization and Computer Graphics **28**(12), 5113–5133 (2022)

29. Milgram, P., Kishino, F.: A Taxonomy of Mixed Reality Visual Displays. IEICE Transactions on Information and Systems **E77-D**(12), 1321–1329 (1994)

30. Miller, B.W.: Using Reading Times and Eye-Movements to Measure Cognitive Engagement. Educational Psychologist **50**(1), 31–42 (2015)

31. Murrell, S., Wang, F., Aldrich, E., Xu, X.: MeteorologyAR: A Mobile AR App to Increase Student Engagement and Promote Active Learning in a Large Lecture Class. In: 2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW). pp. 848–849 (2020)

32. Mystakidis, S.: Distance Education Gamification in Social Virtual Reality: A Case Study on Student Engagement. In: 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA). pp. 1–6. Piraeus, Greece (2020)

33. Nincarean, D., Alia, M.B., Halima, N.D.A., Rahmana, M.H.A.: Mobile Augmented Reality: The Potential for Education. Procedia - Social and Behavioral Sciences **103**, 657–664 (2013)

34. O'Brien, H.L., Toms, E.G.: The Development and Evaluation of a Survey to Measure User Engagement. Journal of the American Society for Information Science and Technology **61**(1), 50–69 (2010)

35. Ooi, S., Tanimoto, T., Sano, M.: Virtual Reality Fire Disaster Training System for Improving Disaster Awareness. In: Proceedings of the 2019 8th International Conference on Educational and Information Technology. pp. 301–307. ICEIT 2019, ACM (2019)

36. Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., Chou, R., Glanville, J., Grimshaw, J.M., Hrobjartsson, A., Lalu, M.M., Li, T., Loder, E.W., Mayo-Wilson, E., McDonald, S., McGuinness, L.A., Stewart, L.A., Thomas, J., Tricco, A.C., Welch, V.A., Whiting, P., Moher, D.: The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. International Journal of Surgery **88**, 1–9 (2021)

37. Perçuku, A., Minkovska, D.: The Application of eXtended Reality for Learning in Higher Education. In: 2022 10th International Scientific Conference on Computer Science (COMSCI). pp. 1–4 (2022)

38. Plebanek, D.J., Sloutsky, V.M.: Selective Attention, Filtering, and the Development of Working Memory. Developmental Science **22**(1), 1–11 (2019)

39. Posner, M.I., Petersen, S.E.: The Attention System of the Human Brain **13**, 25–42 (1990)

40. Ramkumar, N., Fereydooni, N., Shaer, O., Kun, A.L.: Visual Behavior during Engagement with Tangible and Virtual Representations of Archaeological Artifacts. In: Proceedings of the 8th ACM International Symposium on Pervasive Displays. PerDis '19, ACM (2019)

41. Rebollo, C., Remolar, I., Rossano, V., et al.: Multimedia Augmented Reality Game for Learning Math. Multimedia Tools and Applications **81**, 14851–14868 (2022)
42. Roizin, E., Wang, M.: X-Reality (XR) and Immersive Learning: Theories, Use Cases, and Future Development. In: 2021 IEEE International Conference on Engineering, Technology & Education (TALE). pp. 751–754 (2021)

43. Rotgans, J.I., Schmidt, H.G.: Cognitive Engagement in the Problem-based Learning Classroom 16(4), 465–479 (2011)

44. Sánchez-Francisco, M., Díaz, P., Fabiano, F., Aedo, I.: Engaging Users with an AR Pervasive Game for Personal Urban Awareness. In: Proceedings of the 2019 International Conference on Human Computer Interaction. pp. 1–7. Interacción ’19, ACM (2019)

45. Slavova, Y., Mu, M.: A Comparative Study of the Learning Outcomes and Experience of VR in Education. In: 2018 IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR). pp. 685–686. Tuebingen/Reutlingen, Germany (2018)

46. Southgate, E.: Virtual Reality for Deeper Learning: An Exemplar from High School Science. In: 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). pp. 1633–1639 (2019)

47. Sriworapong, S., Pyae, A., Thirasawasd, A., Keereewan, W.: Investigating Students’ Engagement, Enjoyment, and Sociability in Virtual Reality-Based Systems: A Comparative Usability Study of Spatial.io, Gather.town, and Zoom 1626 (2022)

48. Suryodiningrat, S.P., Prabowo, H., Meyliana, Hidayanto, A.N.: Mixed Reality System for Teaching and Learning: A Systematic Literature Review. In: 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE). pp. 387–392 (2021)

49. Targ, W., Pan, I.C., Ou, K.L.: Effectiveness of Virtual Reality on Attention Training for Elementary School Students. Systems 10(4), 104 (2022)

50. Topsakal, E., Topsakal, : Augmented Reality to Engage Preschool Children in Foreign Language Learning 11614 (2019)

51. UNESCO Institute for Statistics: International Standard Classification of Education Fields of Education and Training 2013 (ISCED-F 2013): Detailed Field Descriptions. UIS, Montreal (2015)

52. Wang, H.Y., Sun, J.C.Y.: Real-time Virtual Reality Co-creation: Collective Intelligence and Consciousness for Student Engagement and Focused Attention within Online Communities. Interactive Learning Environments 31(6), 3422–3435 (2023)

53. Wang, Y.P., Chao, Y.P.: To Evaluate the Learning Attention and Effectiveness in Three Remote Learning Approaches using EEG, Eyetracking and Traditional Exam pp. 255–259 (2022)

54. Wen, Y.: Augmented Reality Enhanced Cognitive Engagement: Designing Classroom-based Collaborative Learning Activities for Young Language Learners. Educational Technology Research and Development 69, 843–860 (2021)

55. Wen, Y., Lau, S.Y.: Investigating Pupil’s Cognitive Engagement in Augmented Reality-Supported Second Language Learning Classrooms. In: Expanding Global Horizons Through Technology Enhanced Language Learning. pp. 3–17 (2021)

56. Won, M., Ungu, D.A.K., Matovu, H., Treagust, D.F., Tsai, C.C., Park, J., Morcerino, M., Tasker, R.: Diverse Approaches to Learning with Immersive Virtual Reality Identified from a Systematic Review. Computers & Education 195(C) (apr 2023)

57. Wulf, G., Prinz, W.: Directing Attention to Movement Effects Enhances Learning: A Review. Psychonomic Bulletin & Review 8(4), 648–660 (2001)

58. Xu, N., Li, Y., Lin, J., Yu, L., Liang, H.N.: User Retention of Mobile Augmented Reality for Cultural Heritage Learning. In: 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). pp. 447–452 (2022)
59. Yang, F.Y., Wang, H.Y.: Tracking Visual Attention during Learning of Complex Science Concepts with Augmented 3D Visualizations. Computers & Education **193**, 104659 (2023)

60. Youssef, M.M., Mousa, S.A., Baloola, M.O., Fouda, B.M.: The Impact of Mobile Augmented Reality Design Implementation on User Engagement. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Valentino, G. (eds.) Advances in Computing and Data Sciences, Communications in Computer and Information Science, vol. 1244. Springer, Singapore (2020)

61. Ziker, C., Truman, B., Dodds, H.: Cross Reality (XR): Challenges and Opportunities Across the Spectrum. In: Ryoo, J.H., Winkelmann, K. (eds.) Innovative Learning Environments in STEM Higher Education, pp. 43–56. SpringerBriefs in Statistics, Springer (2021)