The growing adoption of digital twins, reality capture, virtual reality (VR), and additive manufacturing in industry has created a need for engineering education approaches that integrate these technologies within authentic learning experiences. This study investigates the educational effectiveness of a Physical-to-Digital-to-Physical (PDP) learning framework that engages students in the complete workflow of reality capture, digital twin creation, immersive visualization, and physical fabrication. A quasi-experimental study was conducted with 68 undergraduate engineering students enrolled in an introductory engineering course. Students in the PDP group (n = 46) participated in a semester-long experiential learning workflow involving LiDAR scanning, point cloud processing, digital modeling, VR interaction, and 3D printing, while a control group (n = 22) received conventional instruction. Student outcomes were evaluated using course grades, pass rates, retention measures, and attendance-related failures. Results indicate that the PDP framework significantly improved academic performance and student engagement. PDP students achieved higher pass rates (87.0% vs. 63.6%), more favorable grade distributions (χ2(5) = 13.98, p = 0.016), and eliminated attendance-related failures. The intervention was particularly effective for academically at-risk students, substantially increasing the likelihood of successful course completion. These findings suggest that immersive, experiential learning environments that position students as creators of digital twins rather than consumers of prebuilt models can enhance learning, engagement, and persistence in engineering education. The study provides evidence that integrating reality capture, digital twins, VR, and additive manufacturing within a unified instructional framework offers a promising approach for preparing students for emerging digital engineering workflows and Industry 4.0 environments.
References
[1]
(2026) Global Digital Twin Market: Size, Share and Industry Growth. https://www.bccresearch.com/market-research/engineering/digital-twin-market.html?srsltid=AfmBOoql7bkjPWxGvJZgL-IkZkcpumS0L2fQh-u-PxK0dY0p0TK2QtkV
[2]
(2026) Industry 4.0 Technologies: Global Market Through 2030. https://www.bccresearch.com/market-research/manufacturing/industry-4-technologies-global-market.html?srsltid=AfmBOopHNu9Z33R1mJt_elUhPkCB3z1sL1h-I5QyuEUUYIbqlczV5uLi
[3]
(2026) Creating Pathways for Tomorrow’s Workforce Today. Deloitte Insights. https://www.deloitte.com/us/en/insights/industry/manufacturing-industrial-products/manufacturing-industry-diversity.html
[4]
(2026) Improving the Frontline Employee Experience. The Manufacturing Institute. https://themanufacturinginstitute.org/research/digital-skills-report-2024/
[5]
Tao, F., Zhang, H., Liu, A. and Nee, A.Y.C. (2019) Digital Twin in Industry: State-Of-The-Art. IEEETransactionsonIndustrialInformatics, 15, 2405-2415. https://doi.org/10.1109/tii.2018.2873186
[6]
Grieves, M. and Vickers, J. (2016) Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In: Kahlen, J., Flumerfelt, S. and Alves, A., Eds., TransdisciplinaryPerspectivesonComplexSystems, Springer, 85-113. https://doi.org/10.1007/978-3-319-38756-7_4
[7]
Huang, Z., Shen, Y., Li, J., Fey, M. and Brecher, C. (2021) A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics. Sensors, 21, Article 6340. https://doi.org/10.3390/s21196340
[8]
Yin, Y., Zheng, P., Li, C. and Wang, L. (2023) A State-of-the-Art Survey on Augmented Reality-Assisted Digital Twin for Futuristic Human-Centric Industry Transformation. Robotics and Computer-Integrated Manufacturing, 81, Article ID: 102515. https://doi.org/10.1016/j.rcim.2022.102515
[9]
Zheng, P., wang, H., Sang, Z., Zhong, R.Y., Liu, Y., Liu, C., et al. (2018) Smart Manufacturing Systems for Industry 4.0: Conceptual Framework, Scenarios, and Future Perspectives. FrontiersofMechanicalEngineering, 13, 137-150. https://doi.org/10.1007/s11465-018-0499-5
[10]
Nikolakis, N., Alexopoulos, K., Xanthakis, E. and Chryssolouris, G. (2018) The Digital Twin Implementation for Linking the Virtual Representation of Human-Based Production Tasks to Their Physical Counterpart in the Factory-Floor. InternationalJournalofComputerIntegratedManufacturing, 32, 1-12. https://doi.org/10.1080/0951192x.2018.1529430
[11]
Remondino, F. (2011) Heritage Recording and 3D Modeling with Photogrammetry and 3D Scanning. RemoteSensing, 3, 1104-1138. https://doi.org/10.3390/rs3061104
[12]
Fassi, F., Achille, C., Mandelli, A., Rechichi, F. and Parri, S. (2015) A New Idea of Bim System for Visualization, Web Sharing and Using Huge Complex 3D Models for Facility Management. The International Archives of the Photogrammetry, Remote SensingandSpatialInformationSciences, 5, 359-366. https://doi.org/10.5194/isprsarchives-xl-5-w4-359-2015
[13]
Radianti, J., Majchrzak, T.A., Fromm, J. and Wohlgenannt, I. (2020) A Systematic Review of Immersive Virtual Reality Applications for Higher Education: Design Elements, Lessons Learned, and Research Agenda. Computers&Education, 147, Article ID: 103778. https://doi.org/10.1016/j.compedu.2019.103778
[14]
Ojajuni, O.P., Warren, B., Dawan, F., Ismail, Y. and Lawson, A.H. (2024) Board 296: Immersive Engineering Learning and Workforce Development: Pushing the Boundaries of Knowledge Acquisition in a CAVE. https://peer.asee.org/board-296-immersive-engineering-learning-and-workforce-development-pushing-the-boundaries-of-knowledge-acquisition-in-a-cave
[15]
Ojajuni, O., Ismail, Y., Mellieon-Williams, F. and Lawson, A. (2023) Factors Contributing to Student Experience in the Cave Automatic Virtual Environment (CAVE) for Computational Thinking (CT) Development. In: Langran, E., Christensen, P. and Sanson, J., Eds., Proceedings of Society for Information Technology & Teacher Education International Conference, Association for the Advancement of Computing in Education, 65-74.
[16]
Ojajuni, O., Ismail, Y., Mellieon-Williams, F. and Lawson, A. (2023) Investigating Student and Faculty Attitudes and Perceptions towards Using Virtual Reality (VR) to Improve Computational Thinking (CT) Levels in Cybersecurity-Additive Manufacturing (CAM) Training. In: Langran, E., Christensen, P. and Sanson, J., Eds., Proceedings of Society for Information Technology & Teacher Education International Conference, Association for the Advancement of Computing in Education, 75-84.
[17]
Ojajuni, O.P., Ismail, Y., Dawan, F., Lawson, A.H. and Warren, B. (2025) BOARD # 238: Exploring the Impact of Knowledge Acquisition in a CAVE (Cave Automatic Virtual Environment) on Engineering Students Computational Thinking Skill Levels. https://peer.asee.org/board-238-exploring-the-impact-of-knowledge-acquisition-in-a-cave-cave-automatic-virtual-environment-on-engineering-students-computational-thinking-skill-levels
[18]
Huang, S.H., Liu, P., Mokasdar, A. and Hou, L. (2012) Additive Manufacturing and Its Societal Impact: A Literature Review. The International Journal of Advanced ManufacturingTechnology, 67, 1191-1203. https://doi.org/10.1007/s00170-012-4558-5
[19]
Chaudhary, R., Fabbri, P., Leoni, E., Mazzanti, F., Akbari, R. and Antonini, C. (2022) Additive Manufacturing by Digital Light Processing: A Review. ProgressinAdditiveManufacturing, 8, 331-351. https://doi.org/10.1007/s40964-022-00336-0
[20]
Armstrong, M., Mehrabi, H. and Naveed, N. (2022) An Overview of Modern Metal Additive Manufacturing Technology. JournalofManufacturingProcesses, 84, 1001-1029. https://doi.org/10.1016/j.jmapro.2022.10.060
[21]
Zhou, L., Miller, J., Vezza, J., Mayster, M., Raffay, M., Justice, Q., et al. (2024) Additive Manufacturing: A Comprehensive Review. Sensors, 24, Article 2668. https://doi.org/10.3390/s24092668
[22]
Kolb, D.A., Boyatzis, R.E. and Mainemelis, C. (2014) Experiential Learning Theory: Previous Research and New Directions. In: Sternberg, R.J. and Zhang, L., Eds., PerspectivesonThinking, Learning, andCognitiveStyles, Routledge, 227-248. https://doi.org/10.4324/9781410605986-9
