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Review

Affective Computing in Augmented Reality, Virtual Reality, and Immersive Learning Environments

by
Georgios Lampropoulos
1,2,
Pablo Fernández-Arias
3,
Álvaro Antón-Sancho
3 and
Diego Vergara
3,*
1
Department of Applied Informatics, University of Macedonia, 54636 Thessaloniki, Greece
2
Department of Education, University of Nicosia, 1700 Nicosia, Cyprus
3
Technology, Instruction and Design in Engineering and Education Research Group (TiDEE.rg), Catholic University of Ávila, C/Canteros s/n, 05005 Ávila, Spain
*
Author to whom correspondence should be addressed.
Electronics 2024, 13(15), 2917; https://doi.org/10.3390/electronics13152917
Submission received: 29 May 2024 / Revised: 10 July 2024 / Accepted: 22 July 2024 / Published: 24 July 2024
(This article belongs to the Special Issue The Convergence of Immersive Realities and Multimodal Interfaces: A Pathway to Enhanced User Experience)
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Abstract

:
As students’ affective states can differ between learning that occurs in traditional classrooms when compared with learning that takes place in extended reality and immersive learning environments, it is important to examine the role of affective computing. Therefore, this study aims to provide an overview regarding the use of affective computing in the context of augmented reality, virtual reality, and immersive learning environments and the metaverse. Hence, it examines and presents the state of the art through a review and bibliometric analysis of 188 documents of the existing literature from Scopus and Web of Science (WoS) from 2005 to 2023. The study follows the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement to identify and select relevant to the topic documents. In addition to the analysis of the existing literature, emerging topics and themes are identified and future research directions are presented. The significant role of affective computing within augmented reality and virtual reality environments arose. Their ability to offer engaging and interactive learning experiences while also being able to recognize, monitor, and respond to students’ affective states and to consider their emotions, personalities, characteristics, knowledge, and preferences to provide adaptive and personalized learning was evident. Additionally, their potential to enrich educational activities, increase learning outcomes, and support special education emerged. When augmented reality, virtual reality, and immersive learning environments capitalize on affective computing, meaningful learning can occur and self-regulated learning can be promoted.

1. Introduction

Technological advancements have led to technology-enhanced learning being more widely used in education, which, in turn, has resulted in an increase in the research that is being conducted on this topic [1]. Technology-enhanced learning refers to the adoption and integration of technological applications in teaching and learning activities [2]. Technology-enhanced learning interventions offer the potential to create interactive and personalized learning experiences [3] and are positively viewed by educational stakeholders [4].
Extended reality technologies, that is augmented reality, virtual reality, and mixed reality, are also being increasingly used in both formal and informal educational settings to enrich teaching and learning processes [5,6,7]. Augmented reality focuses on enriching users’ physical environment with digital objects and information without cutting them off from it [8,9] while virtual reality simulates users’ physical presence within immersive virtual environments that are characterized by high levels of realism and fully engulf them [10,11]. Based on the “reality-virtuality continuum”, although it is not strictly specified, a mixed reality in which digital and physical objects interact and co-exist lies somewhere between the ends of augmented reality and virtual reality [12]. Studies have highlighted the benefits that the integration of augmented reality can yield in education in terms of increasing students’ learning motivation, engagement, and learning outcomes while also enabling them to interact and communicate with their peers and their surrounding environment in new ways [13,14,15,16]. Similarly, studies have also revealed that virtual reality can positively affect educational activities by enabling students to be immersed in fully virtual environments, experience settings and environments that would not have been possible otherwise, and acquire hands-on experiences in safe environments [17,18,19]. However, besides the potential of augmented reality and virtual reality learning experiences to bring about positive learning outcomes in all educational levels and their being positively viewed and regarded as meaningful educational tools [20], taking students’ affects, behaviors, emotions, and characteristics into account is imperative to ensure that adaptive learning experiences that lead to meaningful learning can be created.
As students grow up surrounded by technological applications and devices and have direct access to information, their educational needs and their concept of effective learning have changed [21] with students seeking for more personalized, interactive, and adaptive learning experiences [22]. To address the new educational requirements and to offer learning experiences that account for students’ characteristics and emotions in the context of technology-enhanced learning, the field of affective computing is rapidly progressing [23,24,25]. In the context of education, the multidisciplinary field of affective computing refers to computational applications that affect and are influenced by learners’ emotions and affective phenomena [23]. Affective computing is a set of techniques and approaches that enables systems or applications to perform affect recognition from data in various granularity scales and modalities [26]. Hence, by capitalizing on affective computing, applications can objectively focus on the interrelationship between cognition, motivation, and emotion and are capable of objectively identifying, processing, monitoring, responding, and adapting to learners’ affective status through the educational process in an intelligent manner in real time [23,27,28]. Although affective computing involves several technologies, artificial intelligence, machine learning, natural language processing, sentiment analysis, emotion recognition, learning analytics, and educational data mining are key elements to creating systems and applications that intelligently identify and respond to learners’ actions, affective status, knowledge, performance, emotions, and behaviors [29,30,31]. The significance of affective computing in education has been highlighted in the literature [32,33]. However, affective computing aspects differ from technology-enhanced learning interventions that take place in traditional learning settings and those occurring in extended reality and immersive learning environments. Although various studies have focused on examining and creating technologies that will be able to identify, monitor, and respond to users’ affective states [23], there is a need to further examine the role of affective computing in extended reality learning experiences. This is particularly true, since it can affect learning in both formal and informal settings and is influenced by various factors [34,35].
Augmented reality and virtual reality technologies are rapidly advancing and are being integrated into several domains including education. Simultaneously, affective computing is also gaining ground and its importance in ensuring adaptive education of high quality when adopting and integrating technology-enhanced learning is becoming more evident. Affective computing plays a vital role in understanding learners’ affective states and providing students with meaningful and personalized learning experiences within augmented reality and virtual reality environments. Hence, as this area of study is rapidly advancing and maturing, it is important to present the existing state of the literature so that future studies can build upon it. Therefore, this study aims to provide an overview of the use of affective computing in the context of augmented reality, virtual reality, and immersive learning environments in education through a bibliometric analysis and review. The study main contributions lie in its evaluation of recent research on the application of affective computing in extended reality learning environments as well as its analysis, representation, and mapping of the existing literature. Moreover, this study contributes to the existing body of knowledge by identifying research trajectories, emerging trends, and topics as well as recommending future research directions. The remainder of the study presents the method followed and the tools used (Section 2) and goes over the result analysis (Section 3) focusing on the analysis of documents, affiliations, countries, citations, sources, as well as of the document collection. Additionally, the outcomes are further discussed, implications are presented, research gaps are identified, and future research directions are provided (Section 4). Finally, conclusive remarks and suggestions are provided (Section 5).

2. Materials and Methods

This study followed a bibliometric analysis and scientific mapping approach to provide an overview about the state of the art regarding the role of affective computing in education through the use of augmented reality, virtual reality, and immersive environments. This approach is deemed as appropriate to examine specific topics based on the existing literature [36]. To carry out a valid and thorough analysis, the guidelines presented in [37,38] were adopted. Moreover, the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement [39] was used to identify, process, and select the most relevant to the topic documents while the open-source R package Bibliometrix, which was specifically created to conduct bibliometric analysis and scientific mapping studies, was utilized to analyze the data [40]. Bibliometrix version 4.2.1 and R version 4.3.3 were used.
Scopus and Web of Science (WoS) were selected as the databases to identify and retrieve the documents due to them being highly regarded, to the relevancy to the topic of the indexed documents, as well as to their high impact [41,42]. Their ability to generate data that can also be used in Bibliometrix was another reason to opt for these databases. To provide a thorough analysis of the current state of the art, all types of documents were examined and the search query used several keywords. The query used was the following: (“augmented reality” OR “AR” OR “virtual reality” OR “vr” OR “extended reality” OR “XR” OR “mixed reality” OR “MR” OR “immersive” OR “metaverse”) AND (“affective computing” OR “affective” OR “emotion” OR ”behavior” OR “behaviour” OR “mood” OR “sentiment”) AND (“education” OR “learn” OR “pupil” OR “school” OR “student” OR “teach” OR “universit” OR “instructor” OR “tutor” OR “college” OR “facult” OR “lecturer” OR “professor” OR “class”). However, it is worth noting that due to the use of some terms (e.g., learn, class, etc.) many documents that were not relevant to the topic were also identified initially. On the contrary, using such keywords ensured that all the relevant documents would be identified. As this study took place during 2024, only data up to 2023 is included. This was done so as not to depict a representation of 2024 that is based on data only from the first quarter. The final limitation set was the inclusion of only English documents. The inclusion criteria set for a study to be included within the document collection examined were for the study to focus on the educational domain, examine the use of augmented reality, virtual reality, mixed reality, or the metaverse, and analyze affective computing and affective domain in general. Hence, if a study did not meet the specific inclusion criteria, it was removed.
The detailed PRISMA flowchart is presented in Figure 1. Specifically, the search query was used on Scopus and WoS and searched within the title, abstract, and keywords of the documents in March 2024. In total, 23,932 documents were identified (Scopus: 15,849 and WoS: 8083) out of which 5664 documents were duplicates and were, thus, removed. Hence, 18,268 documents were screened. Due to the reasons explained previously, a large number of documents (n = 16,763) were excluded as they did not meet the inclusion criteria. Other studies were excluded as they either lacked the required information to analyze them (n = 204), they were proceedings books and not single documents (n = 863), or due to their being retracted (n = 32). Moreover, some documents were editorials (n = 67), erratum/corrections (n = 23), letters/notes (n = 64), data articles (n = 8), or reprints (n = 1). After the removal of the documents, a total of 243 documents were assessed for eligibility. In total, 55 additional documents were removed as they did not meet the inclusion criteria. As a result, the document collection examined in this study consisted of 188 documents from 2005 to 2023.

3. Result Analysis

To illustrate the data, descriptive statistics and graphical representations are used. The results are categorized into: (i) document collection; (ii) citation; (iii) source; (iv) affiliation; (v) country; and (vi) document analysis.

3.1. Document Collection

The descriptive statistics of the document collection are presented in Table 1. Specifically, 188 documents comprise the document collection analyzed. The documents were from 2005 to 2023 and derived from 136 sources. Moreover, the documents had an average age of 3.59 years, received 13.87 citations on average, and displayed a 25.3% annual growth rate of documents. A total of 768 unique keywords plus were used while 654 author’s keywords were utilized. Most of the documents were in the form of journal articles (freq.: 114 and perc.: 60.64%), followed by documents that were published in conferences or proceedings (freq.: 62 and perc.: 32.98%). The documents examined have been written by 580 authors from 49 countries. Of the 188 documents, 16 (8.51%) were single-authored documents. On average, there were 3.78 co-authors for each document with an overall international co-authorship rate of 13.83%.

3.2. Citations

It is worth noting that interest in this topic has increased, as the vast majority of studies have been published in the last 5 years, with the significant majority having been published in 2023 (freq.: 58 and perc.: 30.85%) and in 2022 (freq.: 39 and perc.: 20.74%). However, some studies were also published in 2005, 2007, and 2009. The high annual growth rate of documents (25.3%) is in line with the increased number of published documents which can be separated into the periods of 2005–2015 (initial phase), 2016–2019 (topic materialization phase), and 2020–2023 (breakthrough years) based on the yearly number of published documents. These outcomes are in line with the advances in the field of immersive environments and extended reality technologies which took place in recent years. The annual scientific production of the documents is displayed in detail in Figure 2. Based on the outcomes, it can be inferred that the significance of this topic will continue to increase in the coming years. The annual scientific production and citations of the documents are presented in Table 2. In particular, documents published in 2018 (n = 6, MeanTCperDoc = 94.5, and MeanTCperYear = 13.5) had by far the highest number of mean total citations per document and per year. Documents published in 2013 (n = 4, MeanTCperDoc = 67.75, and MeanTCperYear = 5.65), 2016 (n = 7, MeanTCperDoc = 37.29, and MeanTCperYear = 4.14), and 2021 (n = 23, MeanTCperDoc = 21.52, and MeanTCperYear = 5.38) also presented high numbers in terms of average citations per document and year. When taking into account the average age of the documents (3.59 years), the recency of the topic, and the increased number of published documents, it is expected that these outcomes will change in the future.

3.3. Sources

The documents were published as journal articles (freq.: 114 and perc.: 60.64%), conference/proceedings papers (freq.: 62 and perc.: 32.98%), book chapters (freq.: 6and perc.: 3.19%), and review articles (freq.: 6 and perc.: 3.19%) in 136 sources. According to the total number of published documents relevant to the topic, “Frontiers in Psychology” (8 documents), “Interactive Learning Environments” (6 documents), “Computers & Education” (5 documents), “Education and Information Technologies” (4 documents), “ETR&D-Educational Technology Research and Development” (4 documents), and “Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (4 documents)” emerged as the top sources. Figure 3 presents the list of the sources which had at least three published documents. The list of the top 10 most impactful sources, when taking into account their h-index and total number of citations on the topic, is displayed in Table 3. The top most prominent sources identified were: (i) “Computers & Education” (h-index: 4 and total citations: 408); (ii) “ETR&D-Educational Technology Research and Development” (h-index: 3 and total citations: 341); (iii) “Journal of Science Education and Technology” (h-index: 3 and total citations: 97); and (iv) “Virtual Reality” (h-index: 3 and total citations: 90).
Bradford’s law was used to cluster the documents into three categories with the first cluster referring to the most impactful sources and the second and third clusters referring to the lesser impactful ones. Based on the results, cluster 1 had 20 sources (14.71%) in which 64 documents were published, cluster 2 was composed of 54 sources (39.71%) in which 62 documents were published, and cluster 3 consisted of 62 sources (45.59%) in which 62 documents were published. According to Bradford’s law, the most impactful sources were: “Frontiers in Psychology”, “Interactive Learning Environments”, “Computers & Education”, “Education and Information Technologies”, “ETR&D-Educational Technology Research and Development”, and “Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)”. Table 4 presents the most impactful sources while Figure 4 showcases their scientific production. It is worth noting that, even among the most impactful sources, the majority of the documents were published in recent years.

3.4. Affiliations

When examining the total number of published documents for each affiliation, the most relevant and productive ones were identified and are presented in Figure 5. The top 5 affiliations whose authors contributed the most documents on this topic were: National Taiwan University of Science and Technology (17 documents), National Cheng Kung University (12 documents), Indian Institute of Technology (9 documents), Kyushu University (9 documents), and University of Massachusetts (8 documents).

3.5. Countries

The authors contributing to this document collection came from 46 countries. The scientific production of each country was assessed based on the number of published documents, as it can be seen in Figure 6. China (167 documents), the United States (79 documents), the United Kingdom (27 documents), Australia (16 documents), and Malaysia (16 documents) were the top 5 countries in terms of scientific production. Furthermore, Figure 7 presents the countries whose authors have received the largest number of citations. The top 5 countries regarding citations were China (789 citations), Denmark (446 citations), the United Kingdom (311 citations), the United States (279 citations), and Spain (136 citations). It is worth noting that the corresponding authors’ country was taken into account in this analysis.
Having only 16 (8.51%) single-authored documents, an average of 3.78 co-authors per document, and a 13.83% international co-authorship rate, 8 clusters emerged when examining the collaboration network of countries. It is worth noting that collaboration also spanned across continents. The country collaboration network and map are presented in Figure 8 and Figure 9 respectively.

3.6. Document Analysis

According to the total number of global citations received, the studies of Allcoat et al. [43], Makransky and Lilleholt [44], Makransky and Petersen [45], and Lin et al. [46] emerged as the most impactful ones. The most impactful documents based on the total number of global citations are presented in Table 5.
To obtain a better understanding on how the trends and themes of the topic, the keywords of the documents were also examined. Figure 10 presents the top keywords plus while Figure 11 depicts the top author’s keywords. Specifically, “virtual reality” (freq.: 54), “students” (freq.: 31), “augmented reality” (freq.: 25), “e-learning” (freq.: 25), and “education” (freq.: 19) emerged as the top 5 most common keywords plus, while “virtual reality” (freq.: 80), “augmented reality” (freq.: 42), “learning” (freq.: 21), “emotions” (freq.: 20), and “education” (freq.: 12) arose as the top 5 most commonly used author’s keywords. As keywords plus have been reported to yield more representative and accurate results [53], they were used to carry out the analysis. The keyword co-occurrence network presented in Figure 12 revealed the existence of 5 main areas. Specifically, these areas are associated with, the adoption and integration of technologies into education, the learning environment and students’ skills, users’ acceptance, immersive technologies, and game-like features and aspects. Moreover, the relationships among the top countries, keywords, and sources are presented in Figure 13.
The keywords were also used to identify the trend topics over the years. The trend topics that emerged during 2016–2023 are displayed in Figure 14. It is worth noting that during 2005–2015, no main trend topics emerged. Specifically, the topics identified were initially focused on users’ knowledge and acceptance, the gradual adoption of augmented reality and virtual reality technologies became more evident with the focus also shifting toward learning environments and students’ emotions, attention, and academic performance. The adoption of technology-enhanced learning and gameful approaches in recent years was also observed.
Furthermore, the conceptual structure map revealed 6 main dimensions, as it can be seen in Figure 15. The dimensions that emerged were related to: (i) learning outcomes and aspects; (ii) gameful approaches in mixed reality environments that focus on students’ skills; (iii) the effect of immersive virtual reality environments on users’ emotions and anxiety; (iv) technology-enhanced learning and computer-mediated instructional systems; (v) the effect of virtual reality on students; and (vi) the role of augmented reality in engineering education.
Moreover, the thematic map of the topic revealed 8 themes, as it can be observed in Figure 16. The themes are categorized using keywords plus into basic, motor, niche, and emerging or declining themes. Two basic themes were identified: (i) engineering education, behavioral patterns, and behavioral intention; and (ii) children, skills, and recognition. Three motor themes emerged: (i) education, environment, technology; (ii) virtual reality, students, and augmented reality; and (iii) learning process, affective learning, and brain. Two niche themes arose: (i) memory, sense, and empathy and (ii) implementation, special education, and autism. One emerging or declining theme was identified: (i) psychology and self-efficacy.
When looking at the thematic evolution of the topic, no clear themes or research directions emerged (Figure 17). However, this can be justified based on the novelty of the field and the fact that the significant majority of the documents were published in recent years. Although both augmented reality and virtual reality can be used to enrich the educational process and influence students’ affective domain, the focus on the role of virtual reality and its advancements seems to be higher than that of augmented reality. The focus on the educational domain and particularly on special education is also observed. Additionally, the potential of immersive technologies to be used as an assistive means to one’s professional development also arose.

4. Discussion

Augmented reality and virtual reality learning environments can enrich education by providing engaging and interactive learning experiences within virtual learning environments. Additionally, they enable students to interact with their peers, surrounding environment, and educational content in new ways. Based on the findings of the existing literature, when integrating these technologies into teaching and learning activities, students showcase an increase in learning motivation, engagement, interest, and performance. However, students’ affective states can differ when learning within extended reality and immersive learning environments. As a result, identifying, monitoring, and responding to students’ affective states, emotions, behaviors, preferences, and actions is imperative to ensure that meaningful education is achieved. Affective computing focuses on the creation of applications that can examine students’ affective states and intelligently adapt accordingly in real time. Therefore, affective computing is a determining factor in the realization of effective augmented reality and virtual reality learning experiences and plays a vital role in providing students with personalized and adaptive learning.
Recent studies have showcased the increasing interest in the use of augmented reality and virtual reality in education [18,54,55]. Although there are several aspects that can affect their effective adoption and integration into teaching and learning activities, affective computing constitutes a determining factor. However, the findings of this study reveal that this field of study is still in its infancy. In particular, although some related documents can be traced back to 2005, the vast majority of relevant to the topic documents were from the last four years (2020–2023) with 2023 (30.85%) and 2022 (20.74%) having the highest number of published documents. With the average number of citations received being 13.87, impactful documents were also published in previous years. The year 2018 was the one in which the documents with the highest mean total citations per document were published. The recency of the topic can also be justified with the average age of the documents being 3.59 years while the interest in the topic can be justified by the high annual growth rate (25.3%). The significance of the topic can be justified by the high number of authors (freq.: 580) who actively explored it and their having different scientific backgrounds and expertise and coming from different countries (freq.: 49). Although a small portion of the documents were single authored (8.51%), the average co-authors per document was 3.78 and the international co-authorship rate was 13.83%. It is worth noting that these collaborations spanned across disciplines and continents which highlights the interdisciplinary nature and the global interest in the topic. Nonetheless, the need to establish more international collaborations arose.
Additionally, although the documents examined were published in all types of sources, most documents were published in scientific journals (60.64%). This fact highlights the need to create communities that actively explore and help advance the topic and communicate their outcomes. From the 136 sources used in the document collection examined, 14.71% were classified as being highly relevant and a total of 64 documents were published in them. Nonetheless, the majority of documents were published over the last few years even among the most impactful sources. Taking into account the results of Bradford’s law, the total number of published documents and the h-index of the sources based on the documents of this collection, “Computers & Education”, “ETR&D-Educational Technology Research and Development”, “Journal of Science Education and Technology”, “Virtual Reality”, “Frontiers in Psychology”, “Interactive Learning Environments”, and “Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)” emerged as the most impactful sources. Furthermore, China, the United States, the United Kingdom, Australia, and Malaysia arose as the countries whose authors published the most while China, Denmark, the United Kingdom, the United States, and Spain emerged as the countries whose authors received the most citations on the topic. When taking the total number of global citations received into account, the studies of Allcoat et al. [43], Makransky and Lilleholt [44], Makransky and Petersen [45], and Lin et al. [46] arose as the most influential ones. Specifically, the study of Allcoat et al. [43] revealed the effects on learners’ engagement, emotion, and performance when learning within virtual reality environments. Makransky and Lilleholt [44] examined the emotional value of immersive virtual reality experiences in education and in a follow-up study, Makransky and Petersen [45] presented their cognitive affective model of immersive learning (CAMIL). Lin et al. [46] looked into how augmented reality simulation systems can affect learners’ behavior patterns and collaborative knowledge construction performances.
Based on the findings of this study, considering and recognizing students’ emotions, states, and affects is vital to achieve positive educational outcomes and this is particularly important within immersive learning experiences created through the use of augmented reality and virtual reality technologies. Studies which systematically examined the role of augmented reality [56,57] and virtual reality [58,59] in education highlighted the educational benefits that can be yielded through their integration in teaching and learning activities. However, to ensure the effective adoption and integration of such technologies, it is important to take students’ emotions and characteristics into account [20,60]. Recent studies, which have systematically explored the use of affective computing in education, have highlighted its key role in providing meaningful learning and ensuring improved learning outcomes in the context of technology-enhanced learning [28,32,61,62]. This study further confirms and expands upon the outcomes of previous studies, highlighting the importance of affective computing in education [28,32] and the role of augmented reality and virtual reality as effective educational tools [14,17,59,63]. Given the nature of extended reality environments, their enabling users to communicate and interact with other users and their environment in new ways, and their providing more personalized and engaging learning experiences, this study highlights the important role of affective computing in extended reality and immersive learning environments to provide more meaningful and interactive learning experiences tailored to the characteristics of each student.

5. Conclusions

Augmented reality and virtual reality learning environments are increasingly being adopted and integrated into educational settings to enrich teaching and learning activities. However, students’ affective states can significantly differ when learning in traditional classrooms when compared to learning in virtual learning environments. Hence, it is imperative to examine students’ affective states, emotions, behaviors, preferences, and actions when learning in such environments. The significance of affective computing in education is also becoming more evident as technology-enhanced learning is becoming more widely applied. In light of these advances, this study provided a bibliometric review that explored the role of affective computing in augmented reality, virtual reality, and immersive virtual reality environments. In particular, the analysis involved the examination of 188 documents from 2005 to 2023 and included the document collection specifications, citations, sources, affiliations, countries, and document analysis. Moreover, the study mapped and presented the existing literature, explored how the topic evolved throughout the years, and identified emerging trends and themes.
Based on the results, the close relation of affective computing with augmented reality and virtual reality technologies was highlighted. Their potential to enrich teaching and learning activities and to provide adaptive and personalized learning that takes students’ affective states and characteristics into account arose. Within such environments, meaningful learning can occur and self-regulated learning can be promoted. When integrating artificial intelligence in such learning environments, intelligent tutoring systems and virtual assistants that not only take students’ performance and knowledge into account but also account for their individual characteristics, personalities, preferences, and affective states can be created. Hence, by capitalizing on affective computing, augmented reality and virtual reality learning experiences can be amplified and potentially more meaningful education that yields better learning outcomes can be achieved. Nonetheless, the topic is still in its infancy with the vast majority of studies having been published in the last few years. Hence, it is expected that the interest in this topic and its significance will increase in the coming years.
When examining the evolution of the topic, although initially no main trend topics emerged, after 2016 the focus on students’ performance, acceptance, and knowledge as well as the gradual adoption and integration of augmented reality and virtual reality technologies in education became more obvious. As the field progressed, the importance of integrating technology-enhanced learning emerged. The relationship of augmented reality and virtual reality technologies with gameful approaches was also highlighted in recent years. The importance of identifying and responding to students’ affective states, emotions, performance, and attention became more evident as the field matured over the years. The dimensions emerged from the conceptual structure map were related to students’ learning outcomes and aspects, to the role of gameful approaches within augmented reality and virtual reality environments, and to the importance of focusing on learners’ emotions. Of the two technologies, virtual reality was mostly associated with its effects on learners while the role of augmented reality in enriching engineering education became clear. The need to integrate computer-mediated instructional systems and technology-enhanced learning interventions to aid students was also highlighted. According to the thematic map analysis, the influence of virtual reality and augmented reality on students, on the learning process, and on promoting affective learning was noticed. Additionally, their potential to recognize and respond to students’ behavioral patterns and behavioral intention when complemented by affective computing techniques and their ability to affect students’ empathy, trigger their senses, and influence their memory and recall arose. Their role in supporting special education and students with disabilities, in improving students’ self-efficacy, and in assisting in professional development also emerged. It is worth noting that when compared to augmented reality, the focus on the role of virtual reality in education and its relationship with affective computing is more extensively being examined.
As the topic advances, there is a clear need for more experimental studies that examine the implications and effects of affective computing within augmented reality, virtual reality, and immersive learning environments at all educational levels to be carried out. The technical aspects of extended reality technologies and affective computing should also be further examined. A more in-depth analysis regarding the areas that showcase growth would enable the identification of key aspects for the realization of affective computing in extended reality in certain domains. Additionally, there is a need to identify suitable design approaches and methods to examine how they can affect face-to-face, online, and hybrid learning, as well as how they can support special education. Future studies should also examine teachers’ competences and knowledge in designing augmented reality and virtual reality interventions and integrating them in their classrooms. Finally, it should also be examined how students’ characteristics, affective states, and personalities influence their learning outcomes and preferences within such environments.

Author Contributions

Conceptualization, G.L. and D.V.; methodology, G.L. and P.F.-A.; software, G.L.; validation, P.F.-A., Á.A.-S. and D.V.; formal analysis, G.L. and Á.A.-S.; investigation, G.L., P.F.-A., Á.A.-S. and D.V.; resources, G.L.; writing—original draft preparation, G.L.; writing—review and editing, P.F.-A., Á.A.-S. and D.V.; supervision, G.L., P.F.-A., Á.A.-S. and D.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data examined in this study is available from the first author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Shen, C.; Ho, J. Technology-enhanced learning in higher education: A bibliometric analysis with latent semantic approach. Comput. Hum. Behav. 2020, 104, 106177. [Google Scholar] [CrossRef]
  2. Kirkwood, A.; Price, L. Technology-enhanced learning and teaching in higher education: What is ‘enhanced’ and how do we know? A critical literature review. Learn. Media Technol. 2014, 39, 6–36. [Google Scholar] [CrossRef]
  3. Kori, K.; Pedaste, M.; Leijen, Ä.; Mäeots, M. Supporting reflection in technology-enhanced learning. Educ. Res. Rev. 2014, 11, 45–55. [Google Scholar] [CrossRef]
  4. Downie, S.; Gao, X.; Bedford, S.; Bell, K.; Kuit, T. Technology enhanced learning environments in higher education: A cross-discipline study on teacher and student perceptions. J. Univ. Teach. Learn. Pract. 2021, 18, 147–168. [Google Scholar] [CrossRef]
  5. Chui, M.; Roberts, R.; Yee, L. McKinsey Technology Trends Outlook 2022; McKinsey & Company: Chicago, IL, USA, 2022. [Google Scholar]
  6. Mourtzis, D.; Angelopoulos, J.; Panopoulos, N. Teaching Factory Paradigm for Personalized Perception of Education based on Extended Reality (XR). In Proceedings of the 12th Conference on Learning Factories (CLF 2022), Singapore, 11–13 April 2022. [Google Scholar] [CrossRef]
  7. Lampropoulos, G. Teaching and learning natural sciences using augmented reality in preschool and primary education: A literature review. Adv. Mob. Learn. Educ. Res. 2024, 4, 1021–1037. [Google Scholar] [CrossRef]
  8. Azuma, R.T. A survey of augmented reality. Presence Teleoper. Virtual Environ. 1997, 6, 355–385. [Google Scholar] [CrossRef]
  9. Carmigniani, J.; Furht, B.; Anisetti, M.; Ceravolo, P.; Damiani, E.; Ivkovic, M. Augmented reality technologies, systems and applications. Multimed. Tools Appl. 2011, 51, 341–377. [Google Scholar] [CrossRef]
  10. Burdea, G.C.; Coiffet, P. Virtual Reality Technology; John Wiley & Sons: Hoboken, NJ, USA, 2003. [Google Scholar]
  11. Wohlgenannt, I.; Simons, A.; Stieglitz, S. Virtual reality. Bus. Inf. Syst. Eng. 2020, 62, 455–461. [Google Scholar] [CrossRef]
  12. Milgram, P.; Kishino, F. A taxonomy of mixed reality visual displays. IEICE Trans. Inf. Syst. 1994, 77, 1321–1329. [Google Scholar]
  13. Garzón, J. An overview of Twenty-Five years of augmented reality in education. Multimodal Technol. Interact. 2021, 5, 37. [Google Scholar] [CrossRef]
  14. Bacca-Acosta, J.L.; Baldiris, S.; Fabregat, R.; Graf, S. Kinshuk Augmented reality trends in education: A systematic review of research and applications. J. Educ. Technol. Soc. 2014, 17, 133–149. [Google Scholar]
  15. Saidin, N.F.; Abd Halim, N.D.; Yahaya, N. A review of research on augmented reality in education: Advantages and applications. Int. Educ. Stud. 2015, 8, 1–8. [Google Scholar] [CrossRef]
  16. Lampropoulos, G.; Keramopoulos, E.; Diamantaras, K.; Evangelidis, G. Integrating augmented reality, gamification, and serious games in computer science education. Educ. Sci. 2023, 13, 618. [Google Scholar] [CrossRef]
  17. Hu-Au, E.; Lee, J.J. Virtual reality in education: A tool for learning in the experience age. Int. J. Innov. Educ. 2017, 4, 215–226. [Google Scholar] [CrossRef]
  18. Rojas-Sánchez, M.A.; Palos-Sánchez, P.R.; Folgado-Fernández, J.A. Systematic literature review and bibliometric analysis on virtual reality and education. Educ. Inf. Technol. 2023, 28, 155–192. [Google Scholar] [CrossRef]
  19. McGovern, E.; Moreira, G.; Luna-Nevarez, C. An application of virtual reality in education: Can this technology enhance the quality of students’ learning experience? J. Educ. Bus. 2020, 95, 490–496. [Google Scholar] [CrossRef]
  20. Lampropoulos, G.; Keramopoulos, E.; Diamantaras, K.; Evangelidis, G. Augmented reality and virtual reality in education: Public perspectives, sentiments, attitudes, and discourses. Educ. Sci. 2022, 12, 798. [Google Scholar] [CrossRef]
  21. Admiraal, W.; Huizenga, J.; Akkerman, S.; Dam, G.T. The concept of flow in collaborative game-based learning. Comput. Hum. Behav. 2011, 27, 1185–1194. [Google Scholar] [CrossRef]
  22. Zhang, L.; Basham, J.D.; Yang, S. Understanding the implementation of personalized learning: A research synthesis. Educ. Res. Rev. 2020, 31, 100339. [Google Scholar] [CrossRef]
  23. Picard, R.W. Affective Computing; MIT Press: Cambridge, MA, USA, 2000. [Google Scholar]
  24. Tao, J.; Tan, T. Affective computing: A review. In Affective Computing and Intelligent Interaction; Springer: Berlin/Heidelberg, Germany, 2005; pp. 981–995. [Google Scholar] [CrossRef]
  25. Wang, Y.; Song, W.; Tao, W.; Liotta, A.; Yang, D.; Li, X.; Gao, S.; Sun, Y.; Ge, W.; Zhang, W.; et al. A systematic review on affective computing: Emotion models, databases, and recent advances. Inf. Fusion 2022, 83–84, 19–52. [Google Scholar] [CrossRef]
  26. Poria, S.; Cambria, E.; Bajpai, R.; Hussain, A. A review of affective computing: From unimodal analysis to multimodal fusion. Inf. Fusion 2017, 37, 98–125. [Google Scholar] [CrossRef]
  27. Calvo, R.A.; D’Mello, S.; Gratch, J.M.; Kappas, A. The Oxford Handbook of Affective Computing; Oxford University Press: New York, NY, USA, 2015. [Google Scholar]
  28. Wu, C.; Huang, Y.; Hwang, J. Review of affective computing in education/learning: Trends and challenges. Br. J. Educ. Technol. 2016, 47, 1304–1323. [Google Scholar] [CrossRef]
  29. Cambria, E.; Das, D.; Bandyopadhyay, S.; Feraco, A. Affective computing and sentiment analysis. In A Practical Guide to Sentiment Analysis; Springer: Berlin/Heidelberg, Germany, 2017; pp. 1–10. [Google Scholar] [CrossRef]
  30. Amin, M.M.; Cambria, E.; Schuller, B.W. Will affective computing emerge from foundation models and general artificial intelligence? A first evaluation of ChatGPT. IEEE Intell. Syst. 2023, 38, 15–23. [Google Scholar] [CrossRef]
  31. Lampropoulos, G. Educational benefits of digital game-based learning: K-12 teachers’ perspectives and attitudes. Adv. Mob. Learn. Educ. Res. 2023, 3, 805–817. [Google Scholar] [CrossRef]
  32. Yadegaridehkordi, E.; Noor, N.F.B.M.; Ayub, M.N.B.; Affal, H.B.; Hussin, N.B. Affective computing in education: A systematic review and future research. Comput. Educ. 2019, 142, 103649. [Google Scholar] [CrossRef]
  33. Akbiyik, C. Can affective computing lead to more effective use of ICT in education. Rev. Educ. 2010, 352, 181–185. [Google Scholar]
  34. Kelly, S.; Kim, J.; Berry, I.; Goke, R. Student impressions of instructors based on Zoom backgrounds: Investigating perceived technology skills of instructors and affective learning of students. Front. Comput. Sci. 2004, 6, 1392669. [Google Scholar] [CrossRef]
  35. Politou, E.; Alepis, E.; Patsakis, C. A survey on mobile affective computing. Comput. Sci. Rev. 2017, 25, 79–100. [Google Scholar] [CrossRef]
  36. Ellegaard, O.; Wallin, J.A. The bibliometric analysis of scholarly production: How great is the impact? Scientometrics 2015, 105, 1809–1831. [Google Scholar] [CrossRef]
  37. Gusenbauer, M.; Haddaway, N.R. Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of google scholar, PubMed, and 26 other resources. Res. Synth. Methods 2020, 11, 181–217. [Google Scholar] [CrossRef]
  38. Donthu, N.; Kumar, S.; Mukherjee, D.; Pandey, N.; Lim, W.M. How to conduct a bibliometric analysis: An overview and guidelines. J. Bus. Res. 2021, 133, 285–296. [Google Scholar] [CrossRef]
  39. 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.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Int. J. Surg. 2021, 88, 105906. [Google Scholar] [CrossRef]
  40. Aria, M.; Cuccurullo, C. Bibliometrix: An r-tool for comprehensive science mapping analysis. J. Informetr. 2017, 11, 959–975. [Google Scholar] [CrossRef]
  41. Mongeon, P.; Paul-Hus, A. The journal coverage of web of science and scopus: A comparative analysis. Scientometrics 2015, 106, 213–228. [Google Scholar] [CrossRef]
  42. Zhu, J.; Liu, W. A tale of two databases: The use of Web of Science and Scopus in academic papers. Scientometrics 2020, 123, 321–335. [Google Scholar] [CrossRef]
  43. Allcoat, D.; von Mühlenen, A. Learning in virtual reality: Effects on performance, emotion and engagement. Res. Learn. Technol. 2018, 26, 1–13. [Google Scholar] [CrossRef]
  44. Makransky, G.; Lilleholt, L. A structural equation modeling investigation of the emotional value of immersive virtual reality in education. Educ. Technol. Res. Dev. 2018, 66, 1141–1164. [Google Scholar] [CrossRef]
  45. Makransky, G.; Petersen, G.B. The cognitive affective model of immersive learning (CAMIL): A theoretical Research-Based model of learning in immersive virtual reality. Educ. Psychol. Rev. 2021, 33, 937–958. [Google Scholar] [CrossRef]
  46. Lin, T.-J.; Duh, H.B.-L.; Li, N.; Wang, H.-Y.; Tsai, C.-C. An investigation of learners’ collaborative knowledge construction performances and behavior patterns in an augmented reality simulation system. Comput. Educ. 2013, 68, 314–321. [Google Scholar] [CrossRef]
  47. Cheng, K.-H.; Tsai, C.-C. A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors. Comput. Educ. 2019, 140, 103600. [Google Scholar] [CrossRef]
  48. Parong, J.; Mayer, R.E. Cognitive and affective processes for learning science in immersive virtual reality. J. Comput. Assist. Learn. 2021, 37, 226–241. [Google Scholar] [CrossRef]
  49. Hwang, W.-Y.; Hu, S.-S. Analysis of peer learning behaviors using multiple representations in virtual reality and their impacts on geometry problem solving. Comput. Educ. 2013, 62, 308–319. [Google Scholar] [CrossRef]
  50. Harley, J.M.; Poitras, E.G.; Jarrell, A.; Duffy, M.C.; Lajoie, S.P. Comparing virtual and location-based augmented reality mobile learning: Emotions and learning outcomes. Educ. Technol. Res. Dev. 2016, 64, 359–388. [Google Scholar] [CrossRef]
  51. Lugrin, J.-L.; Latoschik, M.E.; Habel, M.; Roth, D.; Seufert, C.; Grafe, S. Breaking bad behaviors: A new tool for learning classroom management using virtual reality. Front. ICT 2016, 3, 26. [Google Scholar] [CrossRef]
  52. Shen, C.; Ho, J.; Ly, P.T.M.; Kuo, T. Behavioural intentions of using virtual reality in learning: Perspectives of acceptance of information technology and learning style. Virtual Real. 2019, 23, 313–324. [Google Scholar] [CrossRef]
  53. Zhang, J.; Yu, Q.; Zheng, F.; Long, C.; Lu, Z.; Duan, Z. Comparing keywords plus of WOS and author keywords: A case study of patient adherence research. J. Assoc. Inf. Sci. Technol. 2016, 67, 967–972. [Google Scholar] [CrossRef]
  54. Karakus, M.; Ersozlu, A.; Clark, A.C. Augmented reality research in education: A bibliometric study. EURASIA J. Math. Sci. Technol. Educ. 2019, 15, em1755. [Google Scholar] [CrossRef]
  55. López-Belmonte, J.; Moreno-Guerrero, A.-J.; López-Núñez, J.-A.; Hinojo-Lucena, F.-J. Augmented reality in education. A scientific mapping in web of science. Interact. Learn. Environ. 2023, 31, 1860–1874. [Google Scholar] [CrossRef]
  56. Akçayır, M.; Akçayır, G. Advantages and challenges associated with augmented reality for education: A systematic review of the literature. Educ. Res. Rev. 2017, 20, 1–11. [Google Scholar] [CrossRef]
  57. Wu, H.-K.; Lee, S.W.-Y.; Chang, H.-Y.; Liang, J.-C. Current status, opportunities and challenges of augmented reality in education. Comput. Educ. 2013, 62, 41–49. [Google Scholar] [CrossRef]
  58. Lampropoulos, G.; Kinshuk. Virtual reality and gamification in education: A systematic review. Educ. Technol. Res. Dev. 2024, 72, 1691–1785. [Google Scholar] [CrossRef]
  59. Kavanagh, S.; Luxton-Reilly, A.; Wuensche, B.; Plimmer, B. A systematic review of virtual reality in education. Themes Sci. Technol. Educ. 2017, 10, 85–119. [Google Scholar]
  60. Gómez-Rios, M.D.; Paredes-Velasco, M.; Hernández-Beleño, R.D.; Fuentes-Pinargote, J.A. Analysis of emotions in the use of augmented reality technologies in education: A systematic review. Comput. Appl. Eng. Educ. 2023, 31, 216–234. [Google Scholar] [CrossRef]
  61. Vergara, D.; Extremera, J.; Rubio, M.P.; Dávila, L.P. Meaningful Learning Through Virtual Reality Learning Environments: A Case Study in Materials Engineering. Appl. Sci. 2019, 9, 4625. [Google Scholar] [CrossRef]
  62. Barancaccio, M.; Mirauda, D.; Patera, S.; Erra, U. Virtual Reality Laboratories in Engineering Blended Learning Environments: Challenges and Opportunities. J. E-Learn. Knowl. Soc. 2023, 19, 34–49. [Google Scholar] [CrossRef]
  63. Lampropoulos, G.; Keramopoulos, E.; Diamantaras, K.; Evangelidis, G. Augmented reality and gamification in education: A systematic literature review of research, applications, and empirical studies. Appl. Sci. 2022, 12, 6809. [Google Scholar] [CrossRef]
Electronics 13 02917 g001
Figure 1. Document processing flowchart.
Figure 1. Document processing flowchart.
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Figure 2. Annual scientific production.
Figure 2. Annual scientific production.
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Figure 3. Sources with most documents published.
Figure 3. Sources with most documents published.
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Figure 4. Top 10 sources production over time based on Bradford’s law.
Figure 4. Top 10 sources production over time based on Bradford’s law.
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Figure 5. Top affiliations based on the number of documents published.
Figure 5. Top affiliations based on the number of documents published.
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Figure 6. Countries that published the most over time.
Figure 6. Countries that published the most over time.
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Figure 7. Countries that received the most citations.
Figure 7. Countries that received the most citations.
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Figure 8. Country collaboration network.
Figure 8. Country collaboration network.
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Figure 9. Country collaboration map.
Figure 9. Country collaboration map.
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Figure 10. Most frequent keywords plus.
Figure 10. Most frequent keywords plus.
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Figure 11. Most frequent author’s keywords.
Figure 11. Most frequent author’s keywords.
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Figure 12. Keywords plus co-occurrence network.
Figure 12. Keywords plus co-occurrence network.
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Figure 13. Countries, keywords, and sources relationship.
Figure 13. Countries, keywords, and sources relationship.
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Figure 14. Trend topics based on keywords plus.
Figure 14. Trend topics based on keywords plus.
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Figure 15. Conceptual structure map.
Figure 15. Conceptual structure map.
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Figure 16. Thematic map of the topic.
Figure 16. Thematic map of the topic.
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Figure 17. Thematic evolution of the topic.
Figure 17. Thematic evolution of the topic.
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Table 1. Document collection information.
Table 1. Document collection information.
DescriptionResultsDescriptionResults
Main information about data Document types
Timespan2005:2023Article114
Sources (Journals, Books, etc.)136Book chapter6
Documents188Conference/proceedings paper62
Annual Growth Rate %25.3Review6
Document Average Age3.59Authors
Average citations per doc13.87Authors580
References5754Authors of single-authored docs14
Document contents Authors collaboration
Keywords Plus (ID)768Single-authored docs16
Author’s Keywords (DE)654Co-Authors per Doc3.78
International co-authorships %13.83
Table 2. Annual scientific production and citations.
Table 2. Annual scientific production and citations.
YearMeanTCperDocNMeanTC perYearCitable YearsYearMeanTCperDocNMeanTCperYearCitable Years
2005710.3520201637.2974.149
20071510.831820178.651.078
2009610.3816201894.5613.57
20100.520.0315201923.17123.866
20113022.141420209.74231.955
2012310.2313202121.52235.384
201367.7545.651220224.36391.453
20142722.451120232.36581.182
20151611.610
Table 3. Most impactful sources based on h-index.
Table 3. Most impactful sources based on h-index.
Sourcesh_indexg_indexm_indexTCNPPY_start
Computers & Education450.33340852013
ETR&D-Educational Technology Research and Development340.33334142016
Journal of Science Education and Technology330.2739732014
Virtual Reality330.59032019
Frontiers in Psychology360.754382021
Interactive Learning Environments3513362022
Education and Information Technologies340.1673042007
Education Sciences3311632022
Journal of Computer Assisted Learning220.510322021
British Journal of Educational Technology220.55922021
Table 4. Most impactful sources based on Bradford’s law.
Table 4. Most impactful sources based on Bradford’s law.
SourceRankFreqcumFreqCluster
Frontiers in Psychology188Cluster 1
Interactive Learning Environments2614Cluster 1
Computers & Education3519Cluster 1
Education and Information Technologies4423Cluster 1
ETR&D-Educational Technology Research and Development5427Cluster 1
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)6431Cluster 1
Communications in Computer and Information Science7334Cluster 1
Education Sciences8337Cluster 1
Journal of Science Education and Technology9340Cluster 1
Sustainability10343Cluster 1
Virtual Reality11346Cluster 1
Table 5. Most impactful documents based on the total number of citations.
Table 5. Most impactful documents based on the total number of citations.
DocumentDOITotal CitationsTotal Citations Per YearNormalized Total Citations
[43]10.25304/rlt.v26.214026037.142.75
[44]10.1007/s11423-018-9581-224635.142.6
[45]10.1007/s10648-020-09586-2196499.11
[46]10.1016/j.compedu.2013.05.01117614.672.6
[47]10.1016/j.compedu.2019.103600108184.66
[48]10.1111/jcal.1248210125.254.69
[49]10.1016/j.compedu.2012.10.005877.251.28
[50]10.1007/s11423-015-9420-7768.442.04
[51]10.3389/fict.2016.00026768.442.04
[52]10.1007/s10055-018-0348-17312.173.15
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Lampropoulos, G.; Fernández-Arias, P.; Antón-Sancho, Á.; Vergara, D. Affective Computing in Augmented Reality, Virtual Reality, and Immersive Learning Environments. Electronics 2024, 13, 2917. https://doi.org/10.3390/electronics13152917

AMA Style

Lampropoulos G, Fernández-Arias P, Antón-Sancho Á, Vergara D. Affective Computing in Augmented Reality, Virtual Reality, and Immersive Learning Environments. Electronics. 2024; 13(15):2917. https://doi.org/10.3390/electronics13152917

Chicago/Turabian Style

Lampropoulos, Georgios, Pablo Fernández-Arias, Álvaro Antón-Sancho, and Diego Vergara. 2024. "Affective Computing in Augmented Reality, Virtual Reality, and Immersive Learning Environments" Electronics 13, no. 15: 2917. https://doi.org/10.3390/electronics13152917

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