Article Gold Open Access 2025

Immersive Learning Model for University (ILMU): A Novel VR-Based Distance Learning in Higher Education

IEEE Access
Journal · Vol. 13 · pp. 156734-156754
Abstract

The rapid adoption of Immersive Virtual Reality (IVR) distance learning in higher education necessitates cohesive frameworks to guide its effective implementation. However, existing models remain fragmented, lacking integration in pedagogy, technology, and institutions. This study addresses this gap by proposing the Immersive Learning Model for University (ILMU), a model tailored for higher education institutions adopting IVR-based distance learning, developed through the Design Research Methodology (DRM). The research systematically identifies critical success components via a scoping review of 227 studies, revealing six clusters: Learning Design, Technology, Immersion, Engagement, Interactivity, and Usability. These components were refined through Delphi verification with stakeholders (academics, developers, and users), resulting in 15 validated components. The ILMU model integrates these components into a layered structure, emphasizing institutional alignment (Standard, Policy & Curriculum), adaptive pedagogy, and immersive technological synergy. A quasi-experiment involving 80 students compared IVR-based learning (using Nusameta apps) with traditional Zoom instruction. Results demonstrated significant learning gains for the IVR group (mean post-test: 77.75 vs 72.00; p = 0.042), validated by non-parametric tests (Mann–Whitney U = 592.50). The study highlights ILMU’s capacity to increase learning effectiveness, reduce cognitive load, enhance engagement, and align with institutional policies while addressing hardware limitations and ergonomic design challenges. By bridging theoretical rigor with empirical validation, ILMU offers a scalable framework for universities transitioning to immersive, technology-enhanced education. This work contributes to the evolving discourse on IVR in academia, providing actionable insights for educators, policymakers, and developers to optimize immersive learning ecosystems. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Keywords

Author Keywords

Higher education Immersive virtual reality Immersive learning Quasi-experiment Model Design research methodology distance learning

Index Keywords

E-learning Learning systems Teaching Curricula Engineering education High educations Ergonomics scoping review Virtual reality Immersive learning Learning models Design Economic and social effects Higher education institutions Cluster learning Design research methodologies Distance-learning Immersive virtual reality Quasi-experiments
Author Affiliations
Department of Computer Science, Bina Nusantara University, Jakarta, Indonesia, Department of Information Systems, Bina Nusantara University, Jakarta, Indonesia
Department of Information Systems, Bina Nusantara University, Jakarta, Indonesia
Department of Computer Science, Bina Nusantara University, Jakarta, Indonesia
Institut Teknologi Bandung, Bandung, West Java, Indonesia
Funding & Acknowledgements
Binus University, BINUS
Funding text 1: This work was supported by Bina Nusantara University.; Funding text 2: All the authors contributed to this research. They would like to thank Prof. Jaziar Radianti from the University of Agder, Norway, for her expert guidance and invaluable feedback throughout the model development process. Finally, they also thank the readers and hope this article will be valuable for their study and work. The open data has been published with the https://zenodo.org/records/16356071.
Universitetet i Agder, UiA
Funding text 1: This work was supported by Bina Nusantara University.; Funding text 2: All the authors contributed to this research. They would like to thank Prof. Jaziar Radianti from the University of Agder, Norway, for her expert guidance and invaluable feedback throughout the model development process. Finally, they also thank the readers and hope this article will be valuable for their study and work. The open data has been published with the https://zenodo.org/records/16356071.
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