journal article·2025-01-20Open access

Learning Analytics and Educational Data Mining in Augmented Reality, Virtual Reality, and the Metaverse: A Systematic Literature Review, Content Analysis, and Bibliometric Analysis

Georgios Lampropoulos, Georgios Evangelidis

Applied Sciences· Volume 15· Issue 2

Abstract

This study aims to examine the combination of educational data mining and learning analytics with virtual reality, augmented reality, mixed reality, and the metaverse, its role in education, and its impact on teaching and learning. Therefore, a systematic literature review, a bibliometric and scientific mapping analysis, and a content analysis are carried out based on 70 relevant documents identified from six databases, namely, ACM, ERIC, IEEE, ScienceDirect, Scopus, and Web of Science (WoS) following the PRISMA framework. The documents were separated into the following three categories, (i) Theoretical and Review studies, (ii) Proposal and Showcase studies, and (iii) Experimental and Case studies and were examined from different dimensions through an in-depth content analysis using both quantitative and qualitative approaches. The documents were further analyzed using scientometric tools, such as Bibliometrix and VOSviewer and topic modeling through Latent Dirichlet Allocation (LDA). The most prominent topics, areas, and themes were revealed and the outcomes regarding the influence of this combination on learning and teaching were summarized. Based on the results, this combination can effectively enrich education, positively affect learning and teaching, offer deep and meaningful learning, and support both students and teachers. Additionally, it can support different educational approaches and strategies, various learning styles, and special education and be utilized in both formal and informal learning environments. The real-time identification, tracking, monitoring, analysis, and visualization of multimodal learning data of students’ behavior, emotions, cognitive and affective states and the overall learning and teaching processes emerged as a significant benefit that contributes greatly to the realization of adaptive and personalized learning. Finally, it was revealed that the combination of extended reality technologies with learning analytics and educational data mining can support collaborative learning and social learning, improve students’ self-efficacy and self-regulated learning, and increase students’ learning gains, academic achievements, knowledge retention, motivation, and engagement.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Online Learning and Analytics · Virtual Reality Applications and Impacts · Advanced Technologies in Various Fields

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Software and Data Engineering Laboratory, Department of Applied Informatics, School of Information Sciences, University of Macedonia, 54636 Thessaloniki, Greece

Department of Education, School of Education, University of Nicosia, 2417 Nicosia, Cyprus

References (100+)

Show source-provided references
1. Williamson, B. (2017). Big Data in Education: The digital Future of Learning, Policy and Practice, SAGE Publications Ltd.10.4135/9781529714920 ↗
2. Lampropoulos, G. (2023). Educational data mining and learning analytics in the 21st century. Encyclopedia of Data Science and Machine Learning, IGI Global.10.4018/978-1-7998-9220-5.ch098 ↗
3. Educational data mining and learning analytics: An updated survey10.1002/widm.1355 ↗
4. Data mining in education10.1002/widm.1075 ↗
5. Educational Data Mining: A Review of the State of the Art10.1109/TSMCC.2010.2053532 ↗
6. Baradwaj, B.K., and Pal, S. (2012). Mining Educational Data to Analyze Students’ Performance. arXiv.
7. Educational Data Mining: A Bibliometric Analysis of an Emerging Field10.1109/ACCESS.2022.3160457 ↗
8. A bibliometric perspective of learning analytics research landscape10.1080/0144929X.2018.1467967 ↗
9. Educational data mining & students’ performance prediction
10. Bienkowski, M., Feng, M., and Means, B. (2012). Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief, Office of Educational Technology, US Department of Education.
11. Challenges for the future of educational data mining: The baker learning analytics prizes
12. Siemens, G., and Baker, R.S.d. (May, January 29). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, Vancouver, BC, Canada.
13. Educational data mining and learning analytics: Differences, similarities, and time evolution
14. Learning analytics: Drivers, developments and challenges10.1504/IJTEL.2012.051816 ↗
15. Using learning analytics to scale the provision of personalised feedback10.1111/bjet.12592 ↗
16. Bakharia, A., Corrin, L., De Barba, P., Kennedy, G., Gašević, D., Mulder, R., Williams, D., Dawson, S., and Lockyer, L. (2016, January 25–29). A conceptual framework linking learning design with learning analytics. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, Edinburgh, UK.10.1145/2883851.2883944 ↗
17. Lang, C., Siemens, G., Wise, A., and Gasevic, D. (2017). Handbook of Learning Analytics, SOLAR, Society for Learning Analytics Research.
18. Learning analytics: The emergence of a discipline10.1177/0002764213498851 ↗
19. An overview of learning analytics10.1080/13562517.2013.827653 ↗
20. Learning analytics: Ethical issues and dilemmas10.1177/0002764213479366 ↗
21. Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning10.1109/TLT.2018.2868673 ↗
22. Educational data mining and learning analytics for 21st century higher education: A review and synthesis10.1016/j.tele.2019.01.007 ↗
23. Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review10.24059/olj.v20i2.790 ↗
24. The efficacy of learning analytics interventions in higher education: A systematic review
25. Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence
26. Leitner, P., Khalil, M., and Ebner, M. (2017). Learning Analytics in Higher Education—A Literature Review. Learning Analytics: Fundaments, Applications, and Trends, Springer International Publishing.10.1007/978-3-319-52977-6_1 ↗
27. International Educational Data Mining Society (2024). Educational data mining. International Educational Data Mining Society, Available online: https://educationaldatamining.org/.
28. A Systematic Review on Educational Data Mining10.1109/ACCESS.2017.2654247 ↗
29. Educational Data Mining: A Review10.1016/j.sbspro.2013.10.240 ↗
30. The State of Educational Data Mining in 2009: A Review and Future Visions
31. What is XR? Towards a framework for augmented and virtual reality10.1016/j.chb.2022.107289 ↗
32. Augmented reality in education and training10.1007/s11528-012-0559-3 ↗
33. Carmigniani, J., and Furht, B. (2011). Augmented reality: An overview. Handbook of Augmented Reality, Springer.10.1007/978-1-4614-0064-6_1 ↗
34. Augmented reality technologies, systems and applications10.1007/s11042-010-0660-6 ↗
35. Burdea, G.C., and Coiffet, P. (2003). Virtual Reality Technology, John Wiley & Sons.10.1162/105474603322955950 ↗
36. Anthes, C., Garcia-Hernandez, R.J., Wiedemann, M., and Kranzlmuller, D. (2016, January 5–12). State of the art of virtual reality technology. Proceedings of the 2016 IEEE Aerospace Conference, Big Sky, MT, USA.10.1109/AERO.2016.7500674 ↗
37. Sherman, W.R., and Craig, A.B. (2018). Understanding Virtual Reality: Interface, Application, and Design, Morgan Kaufmann.10.1016/B978-0-12-800965-9.00010-6 ↗
38. Immersive virtual reality technology
39. Immersive training systems: Virtual reality and education and training10.1007/BF00896880 ↗
40. Ryan, M.-L. (2015). Narrative as Virtual Reality 2: Revisiting Immersion and Interactivity in Literature and Electronic Media, JHU Press.
41. Understanding virtual reality—Interface, application, and design10.1162/105474603322391668 ↗
42. The impact of gender on the use of augmented reality and virtual reality in students with ASD
43. Blascovich, J., and Bailenson, J. (2011). Infinite Reality: Avatars, Eternal Life, New Worlds, and the Dawn of the Virtual Revolution, William Morrow & Co.
44. A taxonomy of mixed reality visual displays
45. Advantages and challenges associated with augmented reality for education: A systematic review of the literature10.1016/j.edurev.2016.11.002 ↗
46. Chen, P., Liu, X., Cheng, W., and Huang, R. (2017). A review of using augmented reality in education from 2011 to 2016. Innovations in Smart Learning, Springer.10.1007/978-981-10-2419-1_2 ↗
47. Garzón, J. (2021). An overview of Twenty-Five years of augmented reality in education. Multimodal Technol. Interact., 5.10.3390/mti5070037 ↗
48. Lampropoulos, G., Keramopoulos, E., Diamantaras, K., and Evangelidis, G. (2022). Augmented reality and gamification in education: A systematic literature review of research, applications, and empirical studies. Appl. Sci., 12.10.3390/app12136809 ↗
49. Freina, L., and Ott, M. (2015, January 25–26). A literature review on immersive virtual reality in education: State of the art and perspectives. Proceedings of the International Scientific Conference Elearning and Software for Education, Bucharest, Romania.10.12753/2066-026X-15-020 ↗
50. A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda10.1016/j.compedu.2019.103778 ↗
51. A systematic review of virtual reality in education
52. Kinshuk Virtual reality and gamification in education: A systematic review10.1007/s11423-024-10351-3 ↗
53. Lampropoulos, G. (2023). Augmented reality and artificial intelligence in education: Toward immersive intelligent tutoring systems. Augmented Reality and Artificial Intelligence, Springer.10.1007/978-3-031-27166-3_8 ↗
54. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews10.1016/j.ijsu.2021.105906 ↗
55. Latent dirichlet allocation
56. Bibliometrix: An r-tool for comprehensive science mapping analysis10.1016/j.joi.2017.08.007 ↗
57. Software survey: VOSviewer, a computer program for bibliometric mapping10.1007/s11192-009-0146-3 ↗
58. Guidance on conducting a systematic literature review10.1177/0739456X17723971 ↗
59. Higgins, J.P., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M.J., and Welch, V.A. (2019). Cochrane Handbook for Systematic Reviews of Interventions, John Wiley & Sons.10.1002/9781119536604 ↗
60. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: Explanation and elaboration10.7326/0003-4819-151-4-200908180-00136 ↗
61. Analyzing the past to prepare for the future: Writing a literature review
62. The journal coverage of Web of Science and Scopus: A comparative analysis10.1007/s11192-015-1765-5 ↗
63. A tale of two databases: The use of Web of Science and Scopus in academic papers10.1007/s11192-020-03387-8 ↗
64. Poitras, E., Kee, K., Lajoie, S.P., and Cataldo, D. (2013). Towards Evaluating and Modelling the Impacts of Mobile-Based Augmented Reality Applications on Learning and Engagement. Artificial Intelligence in Education, Springer.10.1007/978-3-642-39112-5_133 ↗
65. Son, J., and Cho, Y. (2017, January 4–8). A study on capturing learning data from virtual and mixed reality contents through data collection API. Proceedings of the Workshop Proceedings of the 25th International Conference on Computers in Education, Christchurch, New Zealand.
66. Secretan, J., Wild, F., and Guest, W. (2019, January 10–13). Learning Analytics in Augmented Reality: Blueprint for an AR/xAPI Framework. Proceedings of the 2019 IEEE International Conference on Engineering, Technology and Education (TALE), Yogyakarta, Indonesia.10.1109/TALE48000.2019.9225931 ↗
67. Christopoulos, A., Pellas, N., and Laakso, M.-J. (2020). A Learning Analytics Theoretical Framework for STEM Education Virtual Reality Applications. Educ. Sci., 10.10.20944/preprints202010.0176.v1 ↗
68. Christopoulos, A., and Pellas, N. (2020, January 15–17). Theoretical Foundations of Virtual and Augmented Reality-Supported Learning Analytics. Proceedings of the 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA), Piraeus, Greece.10.1109/IISA50023.2020.9284410 ↗
69. Na, K.S., Mohamed, F., Isham, M.I.M., Siang, C.V., Tasir, Z., and Abas, M.A. (2020, January 17–19). Virtual Reality Application Integrated with Learning Analytics for Enhancing English Pronunciation: A Conceptual Framework. Proceedings of the 2020 IEEE Conference on e-Learning, e-Management and e-Services (IC3e), Kota Kinabalu, Malaysia.10.1109/IC3e50159.2020.9288478 ↗
70. What are the risks of Virtual Reality data? Learning Analytics, Algorithmic Bias and a Fantasy of Perfect Data10.1177/14614448211012794 ↗
71. Christopoulos, A., Mystakidis, S., Pellas, N., and Laakso, M.-J. (2021). ARLEAN: An Augmented Reality Learning Analytics Ethical Framework. Computers, 10.10.3390/computers10080092 ↗
72. Kazanidis, I., Pellas, N., and Christopoulos, A. (2021). A Learning Analytics Conceptual Framework for Augmented Reality-Supported Educational Case Studies. Multimodal Technol. Interact., 5.10.3390/mti5030009 ↗
73. Kurilovas, E. (2021). On Top-Down Versus Bottom-up Personalisation and Evaluation of Augmented Reality Learning Systems. Springer Proceedings in Complexity, Springer International Publishing.10.1007/978-3-030-62066-0_25 ↗
74. Singh, M., Bangay, S., and Sajjanhar, A. (2022, January 17–21). An Architecture for Capturing and Presenting Learning Outcomes using Augmented Reality Enhanced Analytics. Proceedings of the 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), Singapore.10.1109/ISMAR-Adjunct57072.2022.00126 ↗
75. Hensen, B. (2023, January 10–13). A Modularity Approach in Design Science Research for Mixed Reality Learning Artifacts. Proceedings of the 2023 IEEE International Conference on Advanced Learning Technologies (ICALT), Orem, UT, USA.10.1109/ICALT58122.2023.00080 ↗
76. Sulema, Y., Pester, A., Laforge, B., and Andres, F. (2023). Augmented Reality User’s Experience: AI-Based Data Collection, Processing and Analysis. Springer Series on Cultural Computing, Springer Nature.10.1007/978-3-031-27166-3_2 ↗
77. Görzen, S., Heinemann, B., and Schroeder, U. (2024, January 18–22). Towards using the xAPI specification for learning analytics in virtual reality. Proceedings of the LAK Workshops, Kyoto, Japan.
78. Virtual, augmented reality and learning analytics impact on learners, and educators: A systematic review10.1007/s10639-024-12602-5 ↗
79. Klamma, R., Ali, R., and Koren, I. (2019). Immersive Community Analytics for Wearable Enhanced Learning. Lecture Notes in Computer Science, Springer International Publishing.10.1007/978-3-030-21817-1_13 ↗
80. MAGIS: Mobile augmented reality games for instructional support10.1080/10494820.2018.1504305 ↗
81. Papagiannakis, G., Zikas, P., Lydatakis, N., Kateros, S., Kentros, M., Geronikolakis, E., Kamarianakis, M., Kartsonaki, I., and Evangelou, G. (2020, January 17). MAGES 3.0: Tying the knot of medical VR. Proceedings of the ACM SIGGRAPH 2020 Immersive Pavilion, Virtual Event.10.1145/3388536.3407888 ↗
82. Learning Analytics: Virtual Reality for Programming Course in Higher Education10.1016/j.procs.2020.05.095 ↗
83. Farella, M., Arrigo, M., Chiazzese, G., Tosto, C., Seta, L., and Taibi, D. (2021, January 12–15). Integrating xAPI in AR applications for Positive Behaviour Intervention and Support. Proceedings of the 2021 International Conference on Advanced Learning Technologies (ICALT), Tartu, Estonia.10.1109/ICALT52272.2021.00129 ↗
84. Towards design guidelines for virtual reality training for the chemical industry10.1016/j.ece.2021.01.014 ↗
85. Web-based 3D and 360-degree VR materials for IoT security education and test supporting learning analytics10.1016/j.iot.2021.100424 ↗
86. Shi, W., Haga, A., and Okada, Y. (2021). Web-Based 3D and 360 VR Materials for IoT Security Education Supporting Learning Analytics. Lecture Notes on Data Engineering and Communications Technologies, Springer International Publishing.10.1007/978-3-030-70639-5_34 ↗
87. Zikas, P., Kamarianakis, M., Kartsonaki, I., Lydatakis, N., Kateros, S., Kentros, M., Geronikolakis, E., Evangelou, G., Apostolou, A., and Catilo, P.A.A. (2021, January 9–13). COVID-19—VR Strikes Back: Innovative medical VR training. Proceedings of the ACM SIGGRAPH 2021 Immersive Pavilion, Virtual Event.10.1145/3450615.3464546 ↗
88. Antzoulatos, G., Mavropoulos, T., Tzionis, G., Karakostas, A., Gonzalez Costas, A., González Burgos, M., Vrochidis, S., and Kompatsiaris, I. (2022). Usage of Visual Analytics to Support Immigration-Related, Personalised Language Training Scenarios. Lecture Notes in Networks and Systems, Springer International Publishing.10.1007/978-3-030-96296-8_61 ↗
89. Heinemann, B., Gorzen, S., and Schroeder, U. (2022, January 1–4). Systematic Design for Effective Learning in Virtual Reality. Proceedings of the 2022 International Conference on Advanced Learning Technologies (ICALT), Bucharest, Romania.10.1109/ICALT55010.2022.00107 ↗
90. Singh, M., Bangay, S., and Sajjanhar, A. (2022, January 17–21). Augmented Reality Enhanced Analytics to Measure and Mitigate Disengagement in Teaching Young Children. Proceedings of the 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), Singapore.10.1109/ISMAR-Adjunct57072.2022.00166 ↗
91. Heinemann, B., and Schroeder, U. (2023). Learning Analytics and Classroom Management in Specialized Environments: Enhancing the VR Classroom for CS Teacher Education. Immersive Learning Research Network, Springer Nature.10.1007/978-3-031-47328-9_3 ↗
92. Teaching the basics of computer graphics in virtual reality10.1016/j.cag.2023.03.001 ↗
93. cleAR: An interoperable architecture for multi-user AR-based school curricula10.1007/s10055-023-00764-5 ↗
94. ShengKai, L., Mouri, K., Okamoto, M., and Matsubara, Y. (2023, January 20–22). Learning analytics in VR: What if we can collect learning logs in VR classroom. Proceedings of the 2023 International Conference on Artificial Intelligence and Education (ICAIE), Kobe, Japan.10.1109/ICAIE56796.2023.00024 ↗
95. Viol, R.S., Fernandes, P.C., Lacerda, I.I., Melo, K.G.F., Nunes, A.J.D.A., Pimenta, A.S.G.M., Carneiro, C.M., Silva, M.C., and Delabrida, S. (2023, January 16–20). EduVR: Towards an Evaluation Platform for User Interactions in Personalized Virtual Reality Learning Environments. Proceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems, Maceió, Brazil.10.1145/3638067.3638131 ↗
96. Heinemann, B., Görzen, S., Dragoljic, A., Meiendresch, L., Troll, M., and Schroeder, U. (2024, January 18–22). A learning analytics dashboard to investigate the influence of interaction in a VR learning application. Proceedings of the LAK Workshops, Kyoto, Japan.
97. Winer, A., and Geri, N. (2024, January 18–22). Towards learning analytics for student evaluation in the metaversity. Proceedings of the LAK Workshops, Kyoto, Japan.
98. Salman, F.H., and Riley, D.R. (2016, January 6–7). Augmented reality crossover gamified design for sustainable engineering education. Proceedings of the 2016 Future Technologies Conference (FTC), San Francisco, CA, USA.10.1109/FTC.2016.7821781 ↗
99. Evaluating a mixed reality 3D virtual campus with big data and learning analytics: A transversal study
100. Krstulovic, R., Boticki, I., and Ogata, H. (2017, January 12–14). Analyzing heterogeneous learning logs using the iterative convergence method. Proceedings of the 2017 IEEE 6th International Conference on Teaching, Assessment, and Learning for Engineering (TALE), Hong Kong, China.10.1109/TALE.2017.8252384 ↗