Proceedings of International Conference on Applied Innovation in IT  ·  2026/06/12  ·  Vol. 14  ·  Issue 3  ·  pp. 441–455
Neurocognitive Efficiency of Immersive Learning in Virtual Reality Environments: An Empirical Eeg-Based Analysis
Bakhrom Urolov, Kalandar Samandarov, Shakhlo Kurbanova, Jumabeka Smetova, Rayyona Abduganieva, Axmat Beknazarov, Yulduzxon Kushnazarova, Orif Latipov, Shirin Khamrabaeva and Koshmamat uulu Kalysbek
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Abstract
In the context of the digital transformation of education, immersive virtual reality (VR) technologies are gaining increasing importance as tools for enhancing the cognitive efficiency of learning processes. Despite the rapid integration of VR platforms into educational environments, the neurophysiological mechanisms underlying their effectiveness remain insufficiently explored. The present study aims to empirically evaluate the neurocognitive efficiency of immersive learning using electroencephalography (EEG) as an objective method for measuring brain activity. The study involved 120 participants (n = 120) divided into a control group (traditional learning) and an experimental group (VR-based learning). EEG analysis was conducted across the θ (4-7 Hz), α (8-12 Hz), β (13-30 Hz), and γ (30-45 Hz) frequency bands. Spectral power analysis, coherence analysis, and functional connectivity measures were applied. The results demonstrate a statistically significant increase in β and γ activity in the dorsolateral prefrontal cortex in the VR group (p < 0.01), indicating enhanced attention and cognitive engagement. Simultaneously, a reduction in θ activity, associated with cognitive fatigue, was observed. Functional connectivity measures reveal stronger integration of frontoparietal networks during immersive learning conditions. These findings suggest that VR-based learning environments facilitate enhanced neurocognitive activation, improved information processing efficiency, and the formation of stable cognitive patterns. The results expand the theoretical understanding of neuroeducation and provide empirical foundations for optimizing educational technologies. The proposed integrated neurocognitive efficiency index demonstrated high predictive validity and showed a strong correlation with academic performance outcomes. Furthermore, machine learning models achieved high classification accuracy, confirming that spectral and network markers derived from EEG signals can serve as objective indicators of educational quality.
Keywords
Virtual Reality Eeg Neurocognitive Efficiency Immersive Learning Cognitive Load Functional Connectivity.
References
  1. M. Slater and M. V. Sanchez-Vives, “Enhancing our lives with immersive virtual reality,” Frontiers in Robotics and AI, vol. 3, Art. no. 74, 2016.
  2. J. J. Cummings and J. N. Bailenson, “How immersive is enough? A meta-analysis of immersive technology on user presence,” Media Psychology, vol. 19, no. 2, pp. 272-309, 2016.
  3. C. J. Bohil, B. Alicea, and F. A. Biocca, “Virtual reality in neuroscience research and therapy,” Nature Reviews Neuroscience, vol. 12, pp. 752-762, 2011.
  4. G. Riva, B. K. Wiederhold, and F. Mantovani, “Neuroscience of virtual reality: From virtual exposure to embodied medicine,” Cyberpsychology, Behavior, and Social Networking, vol. 22, no. 1, pp. 82-96, 2019.
  5. G. Makransky and L. Lilleholt, “A structural equation modeling investigation of the emotional value of immersive VR,” Computers & Education, vol. 121, pp. 114-126, 2018.
  6. A. C. Neubauer and A. Fink, “Intelligence and neural efficiency,” Neuroscience & Biobehavioral Reviews, vol. 33, pp. 1004-1023, 2009.
  7. J. Sweller, “Cognitive load theory,” Psychology of Learning and Motivation, vol. 55, pp. 37-76, 2011.
  8. W. Klimesch, “EEG alpha and theta oscillations reflect cognitive processes,” Brain Research Reviews, vol. 29, pp. 169-195, 1999.
  9. J. J. Foxe and A. C. Snyder, “The role of alpha-band oscillations in sensory suppression,” Frontiers in Psychology, vol. 2, Art. no. 154, 2011.
  10. A. K. Engel and P. Fries, “Beta-band oscillations-signaling the status quo?,” Current Opinion in Neurobiology, vol. 20, pp. 156-165, 2010.
  11. G. Buzsáki and X. J. Wang, “Mechanisms of gamma oscillations,” Annual Review of Neuroscience, vol. 35, pp. 203-225, 2012.
  12. J. Parong and R. E. Mayer, “Learning science in immersive virtual reality,” Journal of Educational Psychology, vol. 110, no. 6, pp. 785-797, 2018, [Online]. Available: https://doi.org/10.1037/edu0000241.
  13. M. Corbetta and G. L. Shulman, “Control of goal-directed and stimulus-driven attention,” Nature Reviews Neuroscience, vol. 3, pp. 201-215, 2002.
  14. P. Fries, “Rhythms for cognition: Communication through coherence,” Neuron, vol. 88, pp. 220-235, 2015.
  15. S. Hanslmayr et al., “Beta oscillations and cognitive control,” Cerebral Cortex, vol. 24, pp. 154-165, 2014.
  16. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, pp. 273-297, 1995.
  17. G. Pfurtscheller and F. H. Lopes da Silva, “Event-related synchronization/desynchronization,” Clinical Neurophysiology, vol. 110, pp. 1842-1857, 1999.
  18. S. S. Shapiro and M. B. Wilk, “An analysis of variance test for normality,” Biometrika, vol. 52, pp. 591-611, 1965.
  19. S. J. Luck, An Introduction to the ERP Technique, MIT Press, 2014.
  20. E. Wascher et al., “Frontal theta activity reflects mental fatigue,” Biological Psychology, vol. 96, pp. 39-44, 2014.
  21. F. Paas and P. Ayres, “Cognitive load theory in education,” Educational Psychology Review, vol. 26, pp. 1-11, 2014.
  22. C. S. Herrmann et al., “Gamma oscillations and memory,” Trends in Cognitive Sciences, vol. 8, pp. 347-355, 2004.
  23. B. Spitzer and S. Haegens, “Beyond the status quo: Beta oscillations,” Current Opinion in Neurobiology, vol. 47, pp. 42-48, 2017.
  24. N. Axmacher et al., “Gamma oscillations in memory,” Journal of Neuroscience, vol. 26, pp. 9494-9502, 2006.
  25. M. Vinck et al., “Improved index of phase-synchronization,” NeuroImage, vol. 55, pp. 1548-1565, 2011.
  26. J. Cohen, Statistical Power Analysis, Lawrence Erlbaum, 1988.
  27. D. J. Watts and S. H. Strogatz, “Collective dynamics of small-world networks,” Nature, vol. 393, pp. 440-442, 1998.
  28. E. Maris and R. Oostenveld, “Nonparametric statistical testing of EEG data,” Journal of Neuroscience Methods, vol. 164, pp. 177-190, 2007.
  29. T. E. Nichols and A. P. Holmes, “Nonparametric permutation tests,” Human Brain Mapping, vol. 15, pp. 1-25, 2002.
  30. G. Makransky and G. B. Petersen, “The cognitive affective model of immersive learning (CAMIL): A theoretical research-based model of learning in immersive virtual reality,” Educational Psychology Review, vol. 33, pp. 937-958, 2021, [Online]. Available: https://doi.org/10.1007/s10648-020-09586-2.
  31. G. Makransky and G. B. Petersen, “Investigating VR learning mechanisms,” Computers & Education, vol. 168, Art. no. 104211, 2021.
  32. G. Makransky, T. S. Terkildsen, and R. E. Mayer, “Adding immersive virtual reality to a science lab simulation causes more presence but less learning,” Learning and Instruction, vol. 60, pp. 225-236, 2019, [Online]. Available: https://doi.org/10.1016/j.learninstruc.2017.12.007.
  33. E. K. Miller and J. D. Cohen, “Integrative theory of prefrontal cortex function,” Annual Review of Neuroscience, vol. 24, pp. 167-202, 2001.
  34. M. W. Cole et al., “The brain’s frontoparietal network,” Trends in Cognitive Sciences, vol. 17, pp. 114-126, 2013.
  35. E. Bullmore and O. Sporns, “Complex brain networks,” Nature Reviews Neuroscience, vol. 10, pp. 186-198, 2009.
  36. D. S. Bassett et al., “Dynamic reconfiguration of brain networks,” Proceedings of the National Academy of Sciences, vol. 108, pp. 7641-7646, 2011.
  37. M. Rubinov and O. Sporns, “Complex network measures,” NeuroImage, vol. 52, pp. 1059-1069, 2010.
  38. C. J. Stam, “Modern network science of neurological disorders,” Nature Reviews Neuroscience, vol. 15, pp. 683-695, 2014.
  39. X. D. Arsiwalla and P. F. Verschure, “The global dynamical complexity of the human brain network,” Applied Network Science, vol. 1, Art. no. 16, 2016, [Online]. Available: https://doi.org/10.1007/s41109-016-0018-8.
  40. E. Krokos, C. Plaisant, and A. Varshney, “Virtual memory palaces: Immersion aids recall,” Virtual Reality, vol. 23, pp. 1-15, 2019, [Online]. Available: https://doi.org/10.1007/s10055-018-0346-3.
  41. L. Breiman, “Random forests,” Machine Learning, vol. 45, pp. 5-32, 2001.
  42. T. K. Koo and M. Y. Li, “Guideline of ICC reliability,” Journal of Chiropractic Medicine, vol. 15, pp. 155-163, 2016.
  43. T. Donoghue et al., “Parameterizing neural power spectra,” Nature Neuroscience, vol. 23, pp. 1655-1665, 2020.
  44. J. Radianti, T. A. Majchrzak, J. Fromm, and I. Wohlgenannt, “A systematic review of immersive virtual reality applications for higher education,” Education and Information Technologies, vol. 25, pp. 1-38, 2020.
  45. R. P. N. Rao and D. H. Ballard, “Predictive coding in visual cortex,” Nature Neuroscience, vol. 2, pp. 79-87, 1999.
  46. E. J. Wagenmakers et al., “Bayesian inference for psychology,” Psychonomic Bulletin & Review, vol. 25, pp. 35-57, 2018.
  47. O. Jensen, J. Kaiser, and J. P. Lachaux, “Human gamma-frequency oscillations,” Trends in Neurosciences, vol. 30, pp. 317-324, 2007.
  48. K. Kilteni et al., “Sense of embodiment in virtual reality,” Frontiers in Human Neuroscience, vol. 6, Art. no. 204, 2012.

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