Metaverse Learning Environments and Student Well-Being: A Longitudinal Study on The Impact of Immersive Educational Counseling

Main Article Content

Rinovian Rais
Alfiani Dwita
Rosidin Rosidin
Vann Sok

Abstract

This longitudinal study investigates the influence of immersive educational counseling within metaverse learning environments on student well-being. As educational institutions increasingly integrate virtual and augmented reality technologies, understanding their effects on students’ mental health and academic performance is essential. The research focuses on a diverse cohort of students who participated in immersive counseling sessions designed to enhance emotional support, social engagement, and academic guidance. Data collection spanned two academic years, utilizing quantitative surveys, qualitative interviews, and performance metrics to assess changes in well-being and academic outcomes.Results reveal that students engaged in metaverse counseling exhibited significant improvements in emotional resilience, motivation, and overall academic performance compared to their peers receiving traditional counseling. Additionally, the immersive nature of the metaverse fostered a sense of community and belonging, which further contributed to enhanced well-being. This study underscores the transformative potential of metaverse environments in educational settings, advocating for the incorporation of innovative technologies to support student mental health and academic success.

Article Details

How to Cite
Rais, R., Dwita, A., Rosidin, R., & Sok, V. (2025). Metaverse Learning Environments and Student Well-Being: A Longitudinal Study on The Impact of Immersive Educational Counseling. International Journal of Research in Counseling, 3(2), 118–133. https://doi.org/10.70363/ijrc.v3i2.265
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References

Ansari, A. (2023). Deep learning model for predicting tunnel damages and track serviceability under seismic environment. Modeling Earth Systems and Environment, 9(1), 1349–1368. https://doi.org/10.1007/s40808-022-01556-7

Bellavista, P. (2022). Decentralised Learning in Federated Deployment Environments: A System-Level Survey. ACM Computing Surveys, 54(1). https://doi.org/10.1145/3429252

Bicen, H. (2022). Assessing perceptions and evaluating achievements of ESL students with the usage of infographics in a flipped classroom learning environment. Interactive Learning Environments, 30(3), 498–526. https://doi.org/10.1080/10494820.2019.1666285

Bucea-Manea-?oni?, R. (2022). Artificial Intelligence Potential in Higher Education Institutions Enhanced Learning Environment in Romania and Serbia. Sustainability (Switzerland), 14(10). https://doi.org/10.3390/su14105842

Cai, Q. (2022). Applying machine learning and google street view to explore effects of drivers’ visual environment on traffic safety. Transportation Research Part C: Emerging Technologies, 135(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.trc.2021.103541

Capone, R. (2022). Blended Learning and Student-centered Active Learning Environment: A Case Study with STEM Undergraduate Students. Canadian Journal of Science, Mathematics and Technology Education, 22(1), 210–236. https://doi.org/10.1007/s42330-022-00195-5

Castro, G. G. R. d. (2023). Adaptive Path Planning for Fusing Rapidly Exploring Random Trees and Deep Reinforcement Learning in an Agriculture Dynamic Environment UAVs. Agriculture (Switzerland), 13(2). https://doi.org/10.3390/agriculture13020354

Clark, R. M. (2022). Adaptive learning: Helpful to the flipped classroom in the online environment of COVID? Computer Applications in Engineering Education, 30(2), 517–531. https://doi.org/10.1002/cae.22470

Dai, Z. (2022). A Multi-Agent Collaborative Environment Learning Method for UAV Deployment and Resource Allocation. IEEE Transactions on Signal and Information Processing over Networks, 8(Query date: 2025-03-15 16:54:26), 120–130. https://doi.org/10.1109/TSIPN.2022.3150911

Faraji, M. (2022). An integrated 3D CNN-GRU deep learning method for short-term prediction of PM2.5 concentration in urban environment. Science of the Total Environment, 834(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.scitotenv.2022.155324

Farid, A. (2023). A Fast and Accurate Real-Time Vehicle Detection Method Using Deep Learning for Unconstrained Environments. Applied Sciences (Switzerland), 13(5). https://doi.org/10.3390/app13053059

Ghanekar, P. G. (2022). Adsorbate chemical environment-based machine learning framework for heterogeneous catalysis. Nature Communications, 13(1). https://doi.org/10.1038/s41467-022-33256-2

Goudarzi, M. (2023). A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments. IEEE Transactions on Mobile Computing, 22(5), 2491–2505. https://doi.org/10.1109/TMC.2021.3123165

Gupta, T. (2022). A deep learning approach based hardware solution to categorise garbage in environment. Complex and Intelligent Systems, 8(2), 1129–1152. https://doi.org/10.1007/s40747-021-00529-0

Han, W. (2023). A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities. ISPRS Journal of Photogrammetry and Remote Sensing, 202(Query date: 2025-03-15 16:54:26), 87–113. https://doi.org/10.1016/j.isprsjprs.2023.05.032

Haq, A. u. (2022). DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-19465-1

Kalapaaking, A. P. (2023). Blockchain-Based Federated Learning With Secure Aggregation in Trusted Execution Environment for Internet-of-Things. IEEE Transactions on Industrial Informatics, 19(2), 1703–1714. https://doi.org/10.1109/TII.2022.3170348

Khaire, P. (2022). A semi-supervised deep learning based video anomaly detection framework using RGB-D for surveillance of real-world critical environments. Forensic Science International: Digital Investigation, 40(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.fsidi.2022.301346

Kumar, Y. (2022). A novel deep transfer learning models for recognition of birds sounds in different environment. Soft Computing, 26(3), 1003–1023. https://doi.org/10.1007/s00500-021-06640-1

Li, D. (2022). Blockchain-based federated learning methodologies in smart environments. Cluster Computing, 25(4), 2585–2599. https://doi.org/10.1007/s10586-021-03424-y

Liu, T. (2022). A review of deep learning-based recommender system in e-learning environments. Artificial Intelligence Review, 55(8), 5953–5980. https://doi.org/10.1007/s10462-022-10135-2

Lunghi, A. (2022). Computational design of magnetic molecules and their environment using quantum chemistry, machine learning and multiscale simulations. Nature Reviews Chemistry, 6(11), 761–781. https://doi.org/10.1038/s41570-022-00424-3

Malibari, A. A. (2022). A novel metaheuristics with deep learning enabled intrusion detection system for secured smart environment. Sustainable Energy Technologies and Assessments, 52(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.seta.2022.102312

Malik, O. A. (2022). Automated Real-Time Identification of Medicinal Plants Species in Natural Environment Using Deep Learning Models—A Case Study from Borneo Region. Plants, 11(15). https://doi.org/10.3390/plants11151952

Mansour, R. F. (2022). Artificial intelligence based optimization with deep learning model for blockchain enabled intrusion detection in CPS environment. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-17043-z

Martin, F. (2022). A Meta-Analysis on the Community of Inquiry Presences and Learning Outcomes in Online and Blended Learning Environments. Online Learning Journal, 26(1), 325–359. https://doi.org/10.24059/olj.v26i1.2604

Mayuranathan, M. (2022). An efficient optimal security system for intrusion detection in cloud computing environment using hybrid deep learning technique. Advances in Engineering Software, 173(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.advengsoft.2022.103236

Mhlongo, S. (2023). Challenges, opportunities, and prospects of adopting and using smart digital technologies in learning environments: An iterative review. Heliyon, 9(6). https://doi.org/10.1016/j.heliyon.2023.e16348

Miguel-Alonso, I. (2023). Countering the Novelty Effect: A Tutorial for Immersive Virtual Reality Learning Environments. Applied Sciences (Switzerland), 13(1). https://doi.org/10.3390/app13010593

Minn, S. (2022). AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 3(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.caeai.2022.100050

Nair, A. K. (2023). A robust analysis of adversarial attacks on federated learning environments. Computer Standards and Interfaces, 86(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.csi.2023.103723

Nandanwar, H. (2024). Deep learning enabled intrusion detection system for Industrial IOT environment. Expert Systems with Applications, 249(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.eswa.2024.123808

Ojo, M. O. (2022). Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects. Sensors (Basel, Switzerland), 22(20). https://doi.org/10.3390/s22207965

Padakandla, S. (2023). A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments. ACM Computing Surveys, 54(6). https://doi.org/10.1145/3459991

Pepe, M. (2022). Data for 3D reconstruction and point cloud classification using machine learning in cultural heritage environment. Data in Brief, 42(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.dib.2022.108250

Priyadarshini, R. (2022). A deep learning based intelligent framework to mitigate DDoS attack in fog environment. Journal of King Saud University - Computer and Information Sciences, 34(3), 825–831. https://doi.org/10.1016/j.jksuci.2019.04.010

Qu, G. (2022). ChainFL: A Simulation Platform for Joint Federated Learning and Blockchain in Edge/Cloud Computing Environments. IEEE Transactions on Industrial Informatics, 18(5), 3572–3581. https://doi.org/10.1109/TII.2021.3117481

Raj, N. S. (2022). A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020. Journal of Computers in Education, 9(1), 113–148. https://doi.org/10.1007/s40692-021-00199-4

Sambangi, S. (2022). A Feature Similarity Machine Learning Model For Ddos Attack Detection In Modern Network Environments For Industry 4.0. Computers and Electrical Engineering, 100(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.compeleceng.2022.107955

Shadiev, R. (2023). A review study on eye-tracking technology usage in immersive virtual reality learning environments. Computers and Education, 196(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.compedu.2022.104681

Shah, H. (2023). Deep Learning-Based Malicious Smart Contract and Intrusion Detection System for IoT Environment. Mathematics, 11(2). https://doi.org/10.3390/math11020418

Shahbazi, Z. (2022). Agent-Based Recommendation in E-Learning Environment Using Knowledge Discovery and Machine Learning Approaches. Mathematics, 10(7). https://doi.org/10.3390/math10071192

Uppal, M. (2022). Cloud-Based Fault Prediction for Real-Time Monitoring of Sensor Data in Hospital Environment Using Machine Learning. Sustainability (Switzerland), 14(18). https://doi.org/10.3390/su141811667

Waqas, M. (2022). Botnet attack detection in Internet of Things devices over cloud environment via machine learning. Concurrency and Computation: Practice and Experience, 34(4). https://doi.org/10.1002/cpe.6662

Warshawski, S. (2022). Academic self-efficacy, resilience and social support among first-year Israeli nursing students learning in online environments during COVID-19 pandemic. Nurse Education Today, 110(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.nedt.2022.105267

Xing, Z. (2023). Coal resources under carbon peak: Segmentation of massive laser point clouds for coal mining in underground dusty environments using integrated graph deep learning model. Energy, 285(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.energy.2023.128771

Yang, L. (2022). Autonomous environment-adaptive microrobot swarm navigation enabled by deep learning-based real-time distribution planning. Nature Machine Intelligence, 4(5), 480–493. https://doi.org/10.1038/s42256-022-00482-8

Yu, Y. (2023). Compressive strength evaluation of cement-based materials in sulphate environment using optimized deep learning technology. Developments in the Built Environment, 16(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.dibe.2023.100298

Yuan, C. (2022). Application of explainable machine learning for real-time safety analysis toward a connected vehicle environment. Accident Analysis and Prevention, 171(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.aap.2022.106681

Zhang, H. (2023). Combing remote sensing information entropy and machine learning for ecological environment assessment of Hefei-Nanjing-Hangzhou region, China. Journal of Environmental Management, 325(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.jenvman.2022.116533

Zhang, S. (2022). Autonomous navigation of UAV in multi-obstacle environments based on a Deep Reinforcement Learning approach. Applied Soft Computing, 115(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.asoc.2021.108194

Zhang, W. (2022). A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment. Renewable and Sustainable Energy Reviews, 167(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.rser.2022.112704

Zhao, T. (2022). Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction. Computers, Environment and Urban Systems, 94(Query date: 2025-03-15 16:54:26). https://doi.org/10.1016/j.compenvurbsys.2022.101776