Learning Analytics from Virtual Reality (LAVR)

19th March 2024, 13:30-17:00 JST, Kyoto, as part of LAK24 conference

The workshop aims to establish first conversations and bring together researchers and practitioners working on topics on the intersection of Learning Analytics (LA) and (immersive) Virtual Reality (VR) in educational settings. Overall, it aims to advance research on the potential and challenges of rich sensory data generated from VR for learning purposes. Ultimately, we strive to better understand how LA can improve the future design of educational VR applications. Therefore, we call for contributions on the role of LA in foundational research about the VR infrastructure and its multimodal analytics; VR for asynchronous learning experiences; and VR for synchronous teaching in the metaverse.

Topics of interest

  • Objective vs subjective data analysis
  • Multiple sensor merging
  • Effective visualizing of the data coming from the VR
  • Data preparation and challenges of VR for LA
  • Student/teacher acceptance and perception of using VR for LA
  • Privacy and security concerns of using LA from VR in education
  • LA for the design of VR environments and learning experiences
  • LA for performance measurement and evaluation of VR learning
  • LA for improving inclusion, equity, and diversity in VR learning environments
  • LA for supporting individualized learning processes in VR environments
  • LA for enabling and enhancing collaborative learning in VR environments
  • LA for supporting integration of VR in hybrid learning environments
  • Challenges of algorithmic biases and unintended consequences of LA in VR
  • Human-centered explainable LA for VR
  • LA for empowering instructors in VR learning environments
  • Scalability, availability, and shareability aspects of LA for VR
  • General info

    For a long time, virtual reality with head-mounted displays was something you would most likely only find in a research lab. However, thanks to advances in technology and falling prices (Goswami, R., 2023, March 4), it has now become affordable (the head-mounted display can be purchased for a similar price to a mobile phone), allowing a significant increase in the number of people using VR.

    Following the general trends, there is a growing interest in education to explore the possibilities of VR in the classroom (McGrath et al., 2023), especially in STEM education (Kukulska-Hulme, A. et al., 2023). There is also recent evidence that VR environments can have an impact on applied learning domains (Radianti, Majchrzak et al., 2020). These settings are most suitable for medical surgeries (Iop, A. et al., 2022), intelligent manufacturing (Lei, Y. et al., 2023), teaching arts (Cabero-Almenara, J. et al., 2022), and language acquisition (Dhimolea, T. K. et al., 2022).

    The data collected from a variety of sensors from these devices present a rich source of information to be used for learning analytics. Yet, despite the growing interest in VR and its convergence with learning analytics, the number of papers reporting its opportunities for learning analytics is very scarce. A few examples include (Santamaría-Bonfil, 2020) and (Heinemann et al., 2023).

    However, it is evident that an increasing number of VR applications, as well as VR experiences integrated with learning analytics, are emerging from technology companies specializing in VR development (Dwivedi et al., 2022). Claims about the benefits of combining VR and learning analytics lack details of standards, best practices, and academic rigor. Such reports are also almost non-existent (Hwang & Chien, 2022).

    As (Kukulska-Hulme, A. et al, 2023) mention, apart from the potential of VR, the challenges in education include technical and accessibility issues, together with privacy and security concerns. These concerns also apply to the analytics. The data generated from VR sensors is more complicated than clicks from virtual learning environments (VLE) and poses additional challenges to data engineering to have good quality data for the analysis. The richness of the sensory data poses new challenges for privacy. For example, a recent study on 55,000+ users found out that the motion data from the 100 seconds in a game could identify a user with 94.33% accuracy (Nair, V. et al, 2023).

    In addition to the lack of rigorous and public studies, the fact that most research is reported by companies raises some additional ethical issues about the unethical use of learning analytics. These include automated decision-making (performance) enabled by massive data collection without critical evaluation of the underlying collected training data used to develop these models. (Carter & Egliston, 2023).

    The workshop aims to create a Learning Analytics for Virtual Reality (LAVR) forum for bringing together researchers and practitioners working on topics on the intersection of learning analytics (LA) and (immersive) virtual reality (VR) in educational settings. Overall, the LAVR workshop aims to advance research on the potential and challenges of rich sensory data generated from VR for learning purposes. Ultimately, we strive to better understand how LA can improve the future design of educational VR applications. Therefore, we call for contributions on the role of LA in foundational research about the VR infrastructure and its multimodal analytics; VR for asynchronous learning experiences; and VR for synchronous teaching in the metaverse. Although this workshop is primarily focused on VR, we are encouraging submission of other eXtended Reality (XR) technologies such as Augmented Reality (AR), Mixed Reality (MR), Haptics, Wearables, etc.

    Workshop goals

    The objectives of the workshop are as follows:
  • bring for the first time together researchers and practitioners to discuss what possibilities and challenges enable VR for learning analytics,
  • uncover the emerging trends for this research through both the discussion and a keynote presentation by Marcus Specht (Technical University of Delft),
  • establish links between the VR for education and the Learning Analytics community.
  • Submissions

    • The accepted papers will be submitted to CEUR Workshop proceedings (CEUR-WS), as this year the LAK organisers decided that workshop papers will not be published in the Companion proceedings. As such, authors have to comply with the CEUR template.
      • Use the one-column version in the zip archive - CEUR-Template-1col.odt for Word based submission and sample-1col.tex for LaTeX.
      • There is also a CEUR template for Overleaf.
    • Participants are asked to submit the following paper types:
      • Full papers (10-12 pages) describing research with stated methodology and positioned in state-of-the art.
      • Short papers (5-9 pages) more novel work, might be narrower in scope or work-in-progress that is likely to generate discussions at the workshop between the participants.
      • Position papers (5-6 pages) introducing new points of view in the workshop topics or summarising the experience of a group in the field, also welcome practitioner reports describing experience or challenges of using and implementing analytics in virtual reality in education.
    • The submissions should be done using EasyChair system using the link below.
    • Each of the submitted papers will be reviewed by at least two members of the Program Committee. The review process is single-blind (i.e., reviewers' identities are not disclosed to the authors). There is no need to anonymise your submission.

    Submit paper

    *All submissions should be made through Easychair

    Important dates

    • 25 Oct 2023: Workshop calls for participation announced
    • 16 Dec 2023: Workshop papers submission deadline
    • 13 Jan 2024: Notifications sent out
    • 29 Jan 2024: Camera-ready version of papers


    Martin Hlosta  

    IFeL, Swiss Distance University of Applied Science, Switzerland

    Ivan Moser  

    IFeL, Swiss Distance University of Applied Science, Switzerland

    Amir Winer  

    Open University of Israel, Israel

    Nitza Geri  

    Open University of Israel, Israel

    Umesh Ramnarain  

    University of Johannesburg, South Africa

    Christo van der Westhuizen  

    University of Johannesburg, South Africa

    Programme Committee.

    • Geoffray Bonnin, Université Lorraine, France
    • Jean-Michel Boucheix, Université de Bourgogne, LEAD-CNRS, France
    • Herman Myburgh, University of Johannesburg
    • Tanya Nazaretsky, EPFL, Swiss Federal Institute of Technology Lausanne, Switzerland
    • Mafor Penn, University of Johannesburg
    • Ofir Turel, University of Melbourne, Australia
    • Sina Shahmoradi, PH Bern, Switzerland
    • Mamta Shah, Elsevier, USA
    • Egon Werlen, IFeL, Swiss Distance University of Applied Science, Switzerland
    • Qi Zhou, University College London, UK
    • be added continuously


    • 13:30 - 13:40 Ice-breaker and Introductions
    • 13:40 - 14:20 Keynote “Perspectives on LA in and from VR: from tracking guitar chords to discussing airplane design.” by Marcus Specht + Q&A
    • 14:20 - 15:00 Presentation of the papers (round 1)
      • 14:20 - 14:40 “A Learning Analytics Dashboard to Investigate the Influence of Interaction in a VR Learning Application” Birte Heinemann, Sergej Görzen, Ana Dragoljić, Lars Meiendresch, Marc Troll and Ulrik Schroeder
      • 14:40 - 15:00 “Towards Learning Analytics for Student Evaluation in the Metaversity” by Amir Winer and Nitza Geri
    • 15:00 - 15:30 Break
    • 15:30 - 16:10 Presentation of the papers (round 2)
      • 15:30 - 15:50 “Approximating eye gaze with head pose in a virtual reality microteaching scenario for pre-service teachers.” Ivan Moser, Martin Hlosta, Per Bergamin, Umesh Ramnarain, Christo Van Der Westhuizen, Mafor Penn, Noluthando Mdlalose, Koketso Pila and Ogegbo Ayodele
      • 15:50 - 16:10 “Towards the automatization of integrating Learning Analytics into Virtual Reality using xAPI” Sergej Görzen, Birte Heinemann and Ulrik Schroeder
    • 16:10 - 16:50 Group activity - Group discussion activity
    • 16:50 - 17:00 Summary & Conclusions


    The registration for the workshop is managed via the LAK24 registration system. You need to select LAVR during the registration process, listed in the pre-conference program as a half-day workshop for Tuesday 19th March.



    In case of any questions regarding the workshop, please contact : Martin Hlosta   and Amir Winer   and mark the subject as LAVR24.


    The workshop will be in conjunction with the 14th Learning Analytics and Knowledge Conference Conference.


    Cabero-Almenara, J., Llorente-Cejudo, C., & Martinez-Roig, R. (2022). The use of mixed, augmented and virtual reality in history of art teaching: A case study. Applied System Innovation, 5(3), 44.

    Dhimolea, T. K., Kaplan-Rakowski, R., & Lin, L. (2022). A systematic review of research on high-immersion virtual reality for language learning. TechTrends, 66(5), 810-824.

    Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., ... & Wamba, S. F. (2022). Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 66, 102542.

    Heinemann, B., Görzen, S., & Schroeder, U. (2023). Teaching the basics of computer graphics in virtual reality. Computers & Graphics, 112, 1-12.

    Hwang, G. J., & Chien, S. Y. (2022). Definition, roles, and potential research issues of the metaverse in education: An artificial intelligence perspective. Computers and Education: Artificial Intelligence, 3, 100082.

    Iop, A., El-Hajj, V. G., Gharios, M., de Giorgio, A., Monetti, F. M., Edström, E., ... & Romero, M. (2022). Extended reality in neurosurgical education: A systematic review. Sensors, 22(16), 6067.

    Kukulska-Hulme, A., Bossu, C., Charitonos, K., Coughlan, T., Deacon, A., Deane, N., Ferguson, R., …, & Whitelock, D. (2023). Innovating Pedagogy 2023: Open University Innovation Report 11. Milton Keynes: The Open University.

    Lei, Y., Su, Z., He, X., & Cheng, C. (2023). Immersive virtual reality application for intelligent manufacturing: Applications and art design. Mathematical Biosciences and Engineering, 20(3), 4353-4387.

    Nair, V., Guo, W., Mattern, J., Wang, R., O'Brien, J. F., Rosenberg, L., & Song, D. (2023). Unique identification of 50,000+ virtual reality users from head & hand motion data. arXiv preprint arXiv:2302.08927.

    McGrath, O., Hoffman, C., & Dark, S. (2023). Future Prospects and Considerations for AR and VR in Higher Education Academic Technology. Owen McGrath, Chris Hoffman, and Shawna Dark, 'Future Prospects and Considerations for AR and VR in Higher Education Academic Technology, 'EDUCAUSE Review.

    Melo, M., Coelho, H., Gonçalves, G., Losada, N., Jorge, F., Teixeira, M. S., & Bessa, M. (2022). Immersive multisensory virtual reality technologies for virtual tourism: A study of the user’s sense of presence, satisfaction, emotions, and attitudes. Multimedia Systems, 28(3), 1027-1037.

    Radianti, J., Majchrzak, T. A., Fromm, J., & Wohlgenannt, I. (2020). A systematic review of immersive virtual reality applications for higher education: Design elements, lessons learned, and research agenda. Computers & Education, 147, 103778.

    Goswami, R. (2023, March 4). Meta announces big price cuts for its VR headsets. CNBC.

    Santamaría-Bonfil, G., Ibáñez, M. B., Pérez-Ramírez, M., Arroyo-Figueroa, G., & Martínez-Álvarez, F. (2020). Learning analytics for student modeling in virtual reality training systems: Lineworkers case. Computers & Education, 151, 103871.

    Carter, M., & Egliston, B. (2023). What are the risks of virtual reality data? Learning analytics, algorithmic bias and a fantasy of perfect data. New media & society, 25(3), 485-504.