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Cited by 119Augmented Reality ApplicationsSentiment Analysis and Opinion MiningVirtual Reality Applications and Impacts

Augmented Reality and Virtual Reality in Education: Public Perspectives, Sentiments, Attitudes, and Discourses

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Lampropoulos, Georgios, Keramopoulos, Euclid, Diamantaras, Konstantinos, Evangelidis, Georgios·2022·Education Sciences
DOI 10.3390/educsci12110798
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Abstract

This study aims to understand the public’s perspectives, sentiments, attitudes, and discourses regarding the adoption, integration, and use of augmented reality and virtual reality in education and in general by analyzing social media data. Due to its nature, Twitter was the selected platform. Over 17 million tweets were retrieved from January 2010 to December 2020 and four datasets were created. Two of them referred to the general use of these technologies and two to their educational use. The data was analyzed using text mining, sentiment analysis (e.g., polarity and emotion detection), and topic modeling methods. TextBlob, Word-Emotion Association Lexicon (EmoLex), Valence Aware Dictionary for Sentiment Reasoning (VADER), and Latent Dirichlet Allocation (LDA) were some of the tools used. Based on the results, the majority of the public were positively disposed toward the general and the educational use of both augmented reality and virtual reality and mostly expressed positive emotions (e.g., anticipation, trust, and joy) when referring to them. In total, 11 topics emerged that were related to education, new technologies, digital and social media use, marketing and advertising, the industrial domain, the health domain, gaming, fitness and exercising, devices, the travel and tourism domain, and software development kits. The educational benefits of augmented reality and virtual reality, their ability to enrich both teaching and learning activities, and their role as effective educational means were evident.

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  • The results of existing studies suggest that the integration of gamification, serious games, and augmented reality in educational settings is positively viewed and has the potential to yield positive learning outcomes.

    “the results of existing studies suggest that their integration in educational settings is positively viewed and has the potential to yield positive learning outcomes”

    From Integrating Augmented Reality, Gamification, and Serious Games in Computer Science Education (2023) · cites this paper as [30]

    The Discussion reports that tweets about augmented and virtual reality in education were predominantly positive, and the Introduction says immersive technologies can improve learning outcomes. The paper does not establish those points for the integration of gamification, serious games, and augmented reality together; its gaming-related findings concern a topic in the tweet analysis, not educational outcomes. (LLM, conf=0.87)

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  • The results validate those of a previous study which demonstrated that the public mostly expresses positive emotions (e.g., anticipation, trust, and joy) and has a positive attitude toward the general and educational use of augmented reality.

    “The results also validate those of a previous study that focused on the public perspectives, sentiments, and attitudes regarding the use of augmented reality in education and demonstrated that the public mostly expresses positive emotions”

    From Integrating Augmented Reality, Gamification, and Serious Games in Computer Science Education (2023) · cites this paper as [30]

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  1. DiLullo, C.; McGee, P.; Kriebel, R.M. Demystifying the Millennial student: A reassessment in measures of character and engagement in professional education. Anat. Sci. Educ. 2011, 4, 214-226. [CrossRef] · DOI
  2. Alexander, B.; Ashford-Rowe, K.; Barajas-Murph, N.; Dobbin, G.; Knott, J.; McCormack, M.; Pomerantz, J.; Seilhamer, R.; Weber, N. Horizon Report 2019 Higher Education Edition. Technical Report, EDUCAUSE. 2019. Available online: https: //www.learntechlib.org/p/208644/ (accessed on 6 November 2022).
  3. López-Belmonte, J.; Moreno-Guerrero, A.J.; Marín-Marín, J.A.; Lampropoulos, G. The Impact of Gender on the Use of Augmented Reality and Virtual Reality in Students with ASD. Educ. Knowl. Soc. (EKS) 2022, 23, eks.28418. [CrossRef] · DOI
  4. Robinson, R.; Molenda, M.; Rezabek, L. Facilitating learning. In Educational Technology; Routledge: London, UK, 2013; pp. 27-60.
  5. Lampropoulos, G.; Anastasiadis, T.; Siakas, K. Digital Game-based Learning in Education: Significance of Motivating, Engaging and Interactive Learning Environments. In Proceedings of the 24th International Conference on Software Process Improvement- Research into Education and Training (INSPIRE) , Southampton, UK, 16 April 2019; pp. 117-127.
  6. 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. [CrossRef] · DOI · Paper page
  7. 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.
  8. 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. [CrossRef] · DOI · Paper page
  9. López-Belmonte, J.; Pozo-Sánchez, S.; Lampropoulos, G.; Moreno-Guerrero, A.J. Design and validation of a questionnaire for the evaluation of educational experiences in the metaverse in Spanish students (METAEDU). Heliyon 2022, 8, e11364. [CrossRef] · DOI
  10. Suh, A.; Prophet, J. The state of immersive technology research: A literature analysis. Comput. Hum. Behav. 2018, 86, 77-90. [CrossRef] · DOI · Paper page
  11. Rupp, M.A.; Odette, K.L.; Kozachuk, J.; Michaelis, J.R.; Smither, J.A.; McConnell, D.S. Investigating learning outcomes and subjective experiences in 360-degree videos. Comput. Educ. 2019, 128, 256-268. [CrossRef] · DOI
  12. Chang, H.Y.; Hsu, Y.S.; Wu, H.K. A comparison study of augmented reality versus interactive simulation technology to support student learning of a socio-scientific issue. Interact. Learn. Environ. 2016, 24, 1148-1161. [CrossRef] · DOI
  13. Calvet, L.; Bourdin, P.; Prados, F. Immersive Technologies in Higher Education. In Proceedings of the 2019 3rd International Conference on Education and E-Learning, Barcelona, Spain, 5-7 November 2019. [CrossRef] · DOI
  14. Beck, D. Special Issue: Augmented and Virtual Reality in Education: Immersive Learning Research. J. Educ. Comput. Res. 2019, 57, 1619-1625. [CrossRef] · DOI
  15. 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. 2022, 1-38. [CrossRef] [PubMed] · DOI
  16. Hincapie, M.; Diaz, C.; Valencia, A.; Contero, M.; Güemes-Castorena, D. Educational applications of augmented reality: A bibliometric study. Comput. Electr. Eng. 2021, 93, 107289. [CrossRef] · DOI
  17. Freina, L.; Ott, M. A literature review on immersive virtual reality in education: State of the art and perspectives. In Proceedings of the International Scientific Conference Elearning and Software for Education, Bucharest, Romania, 25-26 April 2015; pp. 1-8.
  18. 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. [CrossRef] · DOI · Paper page
  19. Chen, P.; Liu, X.; Cheng, W.; Huang, R. A review of using Augmented Reality in Education from 2011 to 2016. In Innovations in Smart Learning; Springer: Berlin, Germany, 2017; pp. 13-18. [CrossRef] · DOI
  20. Radianti, J.; Majchrzak, T.A.; Fromm, J.; Wohlgenannt, I. A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Comput. Educ. 2020, 147, 103778. [CrossRef] · DOI · Paper page
  21. Neubaum, G.; Krämer, N.C. Monitoring the Opinion of the Crowd: Psychological Mechanisms Underlying Public Opinion Perceptions on Social Media. Media Psychol. 2017, 20, 502-531. [CrossRef] · DOI
  22. Perrin, A. Social media usage. Pew Res. Cent. 2015, 125, 52-68.
  23. Milgram, P.; Kishino, F. A taxonomy of mixed reality visual displays. Ieice Trans. Inf. Syst. 1994, 77, 1321-1329.
  24. Lampropoulos, G.; Keramopoulos, E.; Diamantaras, K. Enhancing the functionality of augmented reality using deep learning, semantic web and knowledge graphs: A review. Vis. Inform. 2020, 4, 32-42. [CrossRef] · DOI
  25. Azuma, R.T. A Survey of Augmented Reality. Presence Teleoperators Virtual Environ. 1997, 6, 355-385. [CrossRef] · DOI · Paper page
  26. Garzón, J.; Pavón, J.; Baldiris, S. Systematic review and meta-analysis of augmented reality in educational settings. Virtual Real. 2019, 23, 447-459. [CrossRef] · DOI · Paper page
  27. Goff, E.E.; Mulvey, K.L.; Irvin, M.J.; Hartstone-Rose, A. Applications of augmented reality in informal science learning sites: A review. J. Sci. Educ. Technol. 2018, 27, 433-447. [CrossRef] · DOI
  28. Schuemie, M.J.; van der Straaten, P.; Krijn, M.; van der Mast, C.A. Research on Presence in Virtual Reality: A Survey. Cyberpsychol. Behav. 2001, 4, 183-201. [CrossRef] · DOI
  29. Anthes, C.; Garcia-Hernandez, R.J.; Wiedemann, M.; Kranzlmuller, D. State of the art of virtual reality technology. In Proceedings of the 2016 IEEE Aerospace Conference, Big Sky, MO, USA, 5-12 March 2016. [CrossRef] · DOI · Paper page
  30. Lampropoulos, G.; Barkoukis, V.; Burden, K.; Anastasiadis, T. 360-degree video in education: An overview and a comparative social media data analysis of the last decade. Smart Learn. Environ. 2021, 8, 1-24. [CrossRef] · DOI
  31. Sherman, W.R.; Craig, A.B. Understanding virtual reality-Interface, application, and design. Presence Teleoperators Virtual Environ. 2003, 12, 441-442. [CrossRef] · DOI
  32. Pozo-Sánchez, S.; Lampropoulos, G.; López-Belmonte, J. Comparing Gamification Models in Higher Education Using Face-to-Face and Virtual Escape Rooms. J. New Approaches Educ. Res. 2022, 11, 307. [CrossRef] · DOI · Paper page
  33. Huang, H.M.; Rauch, U.; Liaw, S.S. Investigating learners' attitudes toward virtual reality learning environments: Based on a constructivist approach. Comput. Educ. 2010, 55, 1171-1182. [CrossRef] · DOI
  34. Gil de Zúñiga, H.; Diehl, T.; Huber, B.; Liu, J. Personality Traits and Social Media Use in 20 Countries: How Personality Relates to Frequency of Social Media Use, Social Media News Use, and Social Media Use for Social Interaction. Cyberpsychol. Behav. Soc. Netw. 2017, 20, 540-552. [CrossRef] [PubMed] · DOI
  35. Makkonen, P.; Lampropoulos, G.; Siakas, K. Security and privacy issues and concerns about the use of social networking services. In Proceedings of the E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education; Association for the Advancement of Computing in Education (AACE): San Diego, CA, USA, 2019 ; pp. 457-466.
  36. Zolkepli, I.A.; Kamarulzaman, Y. Social media adoption: The role of media needs and innovation characteristics. Comput. Hum. Behav. 2015, 43, 189-209. [CrossRef] · DOI
  37. Özgüven, N.; Mucan, B. The Relationship between Personality Traits and Social Media Use. Soc. Behav. Personal. Int. J. 2013, 41, 517-528. [CrossRef] · DOI
  38. Narangajavana Kaosiri, Y.; Callarisa Fiol, L.J.; Moliner Tena, M.Á.; Rodríguez Artola, R.M.; Sánchez García, J. User-Generated Content Sources in Social Media: A New Approach to Explore Tourist Satisfaction. J. Travel Res. 2019, 58, 253-265. [CrossRef] · DOI
  39. Boyd, D.M.; Ellison, N.B. Social Network Sites: Definition, History, and Scholarship. J. Comput. Mediat. Commun. 2007, 13, 210-230. [CrossRef] · DOI · Paper page
  40. Rehm, M.; Notten, A. Twitter as an informal learning space for teachers!? The role of social capital in Twitter conversations among teachers. Teach. Teach. Educ. 2016, 60, 215-223. [CrossRef] · DOI
  41. Lampropoulos, G.; Siakas, K.; Makkonen, P.; Siakas, E. A 10-year Longitudinal Study of Social Media Use in Education. Int. J. Technol. Educ. 2021, 4, 373-398 . [CrossRef] · DOI
  42. Carr, C.T.; Hayes, R.A. Social Media: Defining, Developing, and Divining. Atl. J. Commun. 2015, 23, 46-65. [CrossRef] · DOI
  43. Domenico, G.D.; Sit, J.; Ishizaka, A.; Nunan, D. Fake news, social media and marketing: A systematic review. J. Bus. Res. 2021, 124, 329-341. [CrossRef] · DOI
  44. Shu, K.; Sliva, A.; Wang, S.; Tang, J.; Liu, H. Fake News Detection on Social Media. ACM Sigkdd Explor. Newsl. 2017, 19, 22-36. [CrossRef] · DOI · Paper page
  45. Wu, L.; Morstatter, F.; Carley, K.M.; Liu, H. Misinformation in Social Media. ACM Sigkdd Explor. Newsl. 2019, 21, 80-90. [CrossRef] · DOI
  46. Davis, C.A.; Varol, O.; Ferrara, E.; Flammini, A.; Menczer, F. BotOrNot: A System to Evaluate Social Bot. In Proceedings of the 25th International Conference Companion on World Wide Web-WWW '16 Companion, Montreal, QC, Canada, 11-15 May 2016. [CrossRef] · DOI · Paper page
  47. Orabi, M.; Mouheb, D.; Al Aghbari, Z.; Kamel, I. Detection of Bots in Social Media: A Systematic Review. Inf. Process. Manag. 2020, 57, 102250. [CrossRef] · DOI
  48. Kanakaris, V.; Lampropoulos, G.; Siakas, K. A Survey and a Case-Study Regarding Social Media Security and Privacy on Greek Future IT Professionals. Int. J. Hum. Cap. Inform. Technol. Prof. 2019, 10, 22-37. [CrossRef] · DOI
  49. Senthil Kumar, N.; Saravanakumar, K.; Deepa, K. On Privacy and Security in Social Media -A Comprehensive Study. Procedia Comput. Sci. 2016, 78, 114-119. [CrossRef] · DOI
  50. Ellison, N.B.; Vitak, J.; Steinfield, C.; Gray, R.; Lampe, C. Negotiating Privacy Concerns and Social Capital Needs in a Social Media Environment. In Privacy Online; Springer: Berlin, Germany, 2011; pp. 19-32. [CrossRef] · DOI
  51. Si, S. Social Media and Its Role in Marketing. Bus. Econ. J. 2015, 7, 1000203. [CrossRef] · DOI
  52. Shareef, M.A.; Mukerji, B.; Dwivedi, Y.K.; Rana, N.P.; Islam, R. Social media marketing: Comparative effect of advertisement sources. J. Retail. Consum. Serv. 2019, 46, 58-69. [CrossRef] · DOI
  53. Mirrlees, T.; Alvi, S. EdTech Inc.: Selling, Automating and Globalizing Higher Education in the Digital Age; Routledge: London, UK, 2019. [CrossRef] · DOI
  54. Savage, N. Gaining wisdom from crowds. Commun. ACM 2012, 55, 13-15. [CrossRef] · DOI
  55. Bruns, A.; Highfield, T.; Lind, R.A. Blogs, Twitter, and breaking news: The produsage of citizen journalism. Prod. Theory Digit. World: Intersect. Audiences Prod. Contemp. Theory 2012, 80, 15-32.
  56. Java, A.; Song, X.; Finin, T.; Tseng, B. Why we twitter. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis-WebKDD/SNA-KDD 07 , San Jose, CA, USA, 12 August 2007; ACM Press: New York, NY, USA, 2007. [CrossRef] · DOI
  57. Phuvipadawat, S.; Murata, T. Breaking News Detection and Tracking in Twitter. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Toronto, ON, Canada, 31 August-3 September 2010. [CrossRef] · DOI · Paper page
  58. Nazir, F.; Ghazanfar, M.A.; Maqsood, M.; Aadil, F.; Rho, S.; Mehmood, I. Social media signal detection using tweets volume, hashtag, and sentiment analysis. Multimed. Tools Appl. 2018, 78, 3553-3586. [CrossRef] · DOI
  59. Wu, S.; Hofman, J.M.; Mason, W.A.; Watts, D.J. Who says what to whom on twitter. In Proceedings of the 20th international Conference on World Wide Web-WWW'11, Hyderabad, India, 28 March-1 April 2011; ACM Press: New York, NY, USA, 2011. [CrossRef] · DOI
  60. Balaguer, P.; Teixidó, I.; Vilaplana, J.; Mateo, J.; Rius, J.; Solsona, F. CatSent: A Catalan sentiment analysis website. Multimed. Tools Appl. 2019, 78, 28137-28155. [CrossRef] · DOI
  61. Lee, S.W.; Jiang, G.; Kong, H.Y.; Liu, C. A difference of multimedia consumer's rating and review through sentiment analysis. Multimed. Tools Appl. 2020, 80, 34625-34642. [CrossRef] · DOI
  62. Medhat, W.; Hassan, A.; Korashy, H. Sentiment analysis algorithms and applications: A survey. Ain Shams Eng. J. 2014, 5, 1093-1113. [CrossRef] · DOI
  63. Chen, Z.; Zhang, R.; Xu, T.; Yang, Y.; Wang, J.; Feng, T. Emotional attitudes towards procrastination in people: A large-scale sentiment-focused crawling analysis. Comput. Hum. Behav. 2020, 110, 106391. [CrossRef] · DOI
  64. Kastrati, Z.; Dalipi, F.; Imran, A.S.; Pireva Nuci, K.; Wani, M.A. Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study. Appl. Sci. 2021, 11, 3986. [CrossRef] · DOI
  65. Loria, S.; Keen, P.; Honnibal, M.; Yankovsky, R.; Karesh, D.; Dempsey, E.; et al. Textblob: simplified text processing. Second Textblob Simpl. Text Process. 2014, 3, 2014.
  66. Mohammad, S.; Turney, P. Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon. In Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, Los Angeles, CA, USA , 5 June 2010 ; pp. 26-34.
  67. Li, X.; Lei, L. A bibliometric analysis of topic modelling studies (2000-2017). J. Inf. Sci. 2021, 47, 161-175. [CrossRef] · DOI
  68. Nikolenko, S.I.; Koltcov, S.; Koltsova, O. Topic modelling for qualitative studies. J. Inf. Sci. 2017, 43, 88-102. [CrossRef] · DOI
  69. Vayansky, I.; Kumar, S.A. A review of topic modeling methods. Inf. Syst. 2020, 94, 101582. [CrossRef] · DOI
  70. Ramage, D.; Rosen, E.; Chuang, J.; Manning, C.D.; McFarland, D.A. Topic modeling for the social sciences. In Proceedings of the NIPS 2009 Workshop on Applications for Topic Models: Text and Beyond, Whistler, BC, Canada, 10-11 December 2009 ; Volume 5, pp. 1-4.
  71. Wallach, H.M. Topic modeling. In Proceedings of the 23rd International Conference on Machine Learning-ICML '06, Pittsburgh, PA, USA, 25-29 July 2006. [CrossRef] · DOI
  72. Teh, Y.; Jordan, M.; Beal, M.; Blei, D. Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. In Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada, 13-18 December 2004;
  73. Saul, L., Weiss, Y., Bottou, L., Eds.; MIT Press: Cambridge, MA, USA, 2004; Volume 17.
  74. Hu, Y.; Boyd-Graber, J.; Satinoff, B.; Smith, A. Interactive topic modeling. Mach. Learn. 2014, 95, 423-469. [CrossRef] · DOI
  75. Hong, L.; Davison, B.D. Empirical study of topic modeling in Twitter. In Proceedings of the First Workshop on Social Media Analytics -SOMA '10, Washington, DC, USA, 25 July 2010. [CrossRef] · DOI
  76. Ferner, C.; Havas, C.; Birnbacher, E.; Wegenkittl, S.; Resch, B. Automated Seeded Latent Dirichlet Allocation for Social Media Based Event Detection and Mapping. Information 2020, 11, 376. [CrossRef] · DOI
  77. Jelodar, H.; Wang, Y.; Yuan, C.; Feng, X.; Jiang, X.; Li, Y.; Zhao, L. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed. Tools Appl. 2019, 78, 15169-15211. [CrossRef] · DOI
  78. Xue, J.; Chen, J.; Chen, C.; Zheng, C.; Li, S.; Zhu, T. Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter. PLoS ONE 2020, 15, e0239441. [CrossRef] [PubMed] · DOI
  79. Blei, D.M.; Ng, A.Y.; Jordan, M.I. Latent dirichlet allocation. J. Mach. Learn. Res. 2003, 3, 993-1022.
  80. Muñoz-Saavedra, L.; Miró-Amarante, L.; Domínguez-Morales, M. Augmented and Virtual Reality Evolution and Future Tendency. Appl. Sci. 2020, 10, 322. [CrossRef] · DOI
  81. Tondeur, J.; van Braak, J.; Sang, G.; Voogt, J.; Fisser, P.; Ottenbreit-Leftwich, A. Preparing pre-service teachers to integrate technology in education: A synthesis of qualitative evidence. Comput. Educ. 2012, 59, 134-144. [CrossRef] · DOI
  82. Uerz, D.; Volman, M.; Kral, M. Teacher educators' competences in fostering student teachers' proficiency in teaching and learning with technology: An overview of relevant research literature. Teach. Teach. Educ. 2018, 70, 12-23. [CrossRef] · DOI
  83. Tondeur, J.; Scherer, R.; Baran, E.; Siddiq, F.; Valtonen, T.; Sointu, E. Teacher educators as gatekeepers: Preparing the next generation of teachers for technology integration in education. Br. J. Educ. Technol. 2019, 50, 1189-1209. [CrossRef] · DOI
  84. Ashtari, N.; Bunt, A.; McGrenere, J.; Nebeling, M.; Chilana, P.K. Creating Augmented and Virtual Reality Applications: Current Practices, Challenges, and Opportunities. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, 25-30 April 2020. [CrossRef] · DOI
  85. Rauschnabel, P.A.; Felix, R.; Hinsch, C.; Shahab, H.; Alt, F. What Is XR? Towards a Framework for Augmented and Virtual Reality. Comput. Hum. Behav. 2022, 133, 107289. [CrossRef] · DOI
  86. Avila-Garzon, C.; Bacca-Acosta, J.; Kinshuk.; Duarte, J.; Betancourt, J. Augmented Reality in Education: An Overview of Twenty-Five Years of Research. Contemp. Educ. Technol. 2021, 13, ep302. [CrossRef] · DOI · Paper page
  87. Hamilton, D.; McKechnie, J.; Edgerton, E.; Wilson, C. Immersive virtual reality as a pedagogical tool in education: A systematic literature review of quantitative learning outcomes and experimental design. J. Comput. Educ. 2020, 8, 1-32. [CrossRef] · DOI
  88. Damiani, L.; Demartini, M.; Guizzi, G.; Revetria, R.; Tonelli, F. Augmented and virtual reality applications in industrial systems: A qualitative review towards the industry 4.0 era. IFAC-PapersOnLine 2018, 51, 624-630. [CrossRef] · DOI
  89. Ma, D.; Gausemeier, J.; Fan, X.; Grafe, M. Virtual Reality & Augmented Reality in Industry; Springer: Berlin, Germany, 2011.
  90. Loureiro, S.M.C.; Guerreiro, J.; Ali, F. 20 years of research on virtual reality and augmented reality in tourism context: A text-mining approach. Tour. Manag. 2020, 77, 104028. [CrossRef] · DOI
  91. Yung, R.; Khoo-Lattimore, C. New realities: a systematic literature review on virtual reality and augmented reality in tourism research. Curr. Issues Tour. 2019, 22, 2056-2081. [CrossRef] · DOI
  92. Moro, C.; Štromberga, Z.; Raikos, A.; Stirling, A. The effectiveness of virtual and augmented reality in health sciences and medical anatomy. Anat. Sci. Educ. 2017, 10, 549-559. [CrossRef] [PubMed] · DOI
  93. Sim, K.W.; Baker, B.; Amin, K.; Chan, A.; Patel, K.; Wong, J. Augmented and virtual reality in surgery-The digital surgical environment: applications, limitations and legal pitfalls. Ann. Transl. Med. 2016, 4. [CrossRef] 100. Alcañiz, M.; Bigné, E.; Guixeres, J. Virtual Reality in Marketing: A Framework, Review, and Research Agenda. Front. Psychol. 2019, 10, 01530. [CrossRef] 101. Wedel, M.; Bigné, E.; Zhang, J. Virtual and augmented reality: Advancing research in consumer marketing. Int. J. Res. Mark. 2020, 37, 443-465. [CrossRef] 102. Alturki, R.; Gay, V. Augmented and Virtual Reality in Mobile Fitness Applications: A Survey. In Applications of Intelligent Technologies in Healthcare; Springer International Publishing: Berlin/Heidelberg, Germany, 2018; pp. 67-75. [CrossRef] 103. Ng, Y.L.; Ma, F.; Ho, F.K.; Ip, P.; wa Fu, K. Effectiveness of virtual and augmented reality-enhanced exercise on physical activity, psychological outcomes, and physical performance: A systematic review and meta-analysis of randomized controlled trials. Comput. Hum. Behav. 2019, 99, 278-291. [CrossRef] 104. Amin, D.; Govilkar, S. Comparative study of augmented reality SDKs. Int. J. Comput. Sci. Appl. 2015, 5, 11-26. [CrossRef] 105. Jensen, L.; Konradsen, F. A review of the use of virtual reality head-mounted displays in education and training. Educ. Inf. Technol. 2018, 23, 1515-1529. [CrossRef] · DOI · Paper page

Cited by

Sorted by citations

Authors
Lampropoulos, G., Keramopoulos, E., Diamantaras, K., & Evangelidis, G.
Affiliations
  • Department of Information and Electronic Engineering, International Hellenic University, 57400 Thessaloniki, Greece
  • International Hellenic University (GR)
  • University of Macedonia (GR)
ORCID
Venue
Education Sciences
Publisher
MDPI AG
Published
2022-11-10
Volume
12
Issue
11
Pages
798
ISSN
2227-7102 (ISSN-L)
DOI
10.3390/educsci12110798
OpenAlex
W4308870983
Cited by
119
References
6
Citations by year
2026: 21 · 2025: 39 · 2024: 40 · 2023: 18 · 2022: 1
FWCI
7.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Education Sciences (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Hellenic Foundation for Research and Innovation (HFRI) under the 3rd Call for HFRI PhD Fellowships (6454)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Augmented Reality Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
Keywords
augmented reality, virtual reality, educational technology adoption, public attitudes, public sentiment, social media discourse, Twitter, teaching-learning process

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