Machine Learning Ensemble Model for Predicting Adoption of Metaverse in Higher Education Using PSO Algorithm for Setting Model’s Hyperparameters
الموضوعات : Machine learningAtaollah Abtahi 1 , zahra Afjei 2 , Ebrahim Nazari Farokhi 3
1 - Management and Economics, Science & Research Branch, Islamic Azad University, Tehran, Iran
2 - Management and Economics, Science & Research Branch, Islamic Azad University, Tehran, Iran
3 - Dafoos, Command and Staff University, Tehran, Iran
الکلمات المفتاحية: Metaverse Adoption, Higher Education, Machine Learning, Metaheuristic Setting,
ملخص المقالة :
In recent years, the metaverse has rapidly gained prominence as an emerging virtual platform with multidimensional and interactive features. Its potential applications, particularly in higher education, have drawn increasing attention. However, the acceptance of the metaverse in educational contexts depends on a range of factors that remain insufficiently explored. This study aims to address gaps in previous research, where certain critical factors—such as awareness, user experience, and technological challenges affecting faculty and students in Iran—have been overlooked. The goal of this research is to develop and evaluate an ensemble machine learning model to predict metaverse adoption in Iranian higher education institutions. Drawing on questionnaire data and guided by metaheuristic parameter tuning, the model seeks to identify and forecast factors influencing the uptake of this technology. This descriptive-analytical study collected data from 730 respondents, including university students and faculty members, who answered a questionnaire on metaverse acceptance. Four machine learning models—Random Forest, XGBoost, and Gradient Boosting—were employed. Their parameters were optimized using the PSO (Particle Swarm Optimization) metaheuristic algorithm. Performance metrics included Accuracy, Recall, Precision, and the F1-Score. The results showed that ensemble machine learning models, enhanced by metaheuristic parameter tuning, accurately predicted metaverse adoption. The ensemble model achieved an outstanding accuracy of 95%. Moreover, variables such as technological accessibility, familiarity with the metaverse, and institutional support emerged as key determinants. This research demonstrates that ensemble machine learning models, combined with metaheuristic parameter optimization, can serve as effective tools for forecasting metaverse acceptance in higher education. The findings can help educational administrators devise more effective strategies for implementing and fostering the use of the metaverse, ultimately improving the integration of this technology into academic environments.
[1] Y. Aun, Y. M. J. Khaw, M. L. Gan, and D. L. H. Chern, “A Machine-Learning Ensemble Method for Temporal-aware Sales Forecasting,” in 2022 5th International Conference on Data Science and Information Technology, DSIT 2022 - Proceedings, 2022. doi: 10.1109/DSIT55514.2022.9943925.
[2] D. Buragohain, S. Chaudhary, G. Punpeng, A. Sharma, N. Am-In, and L. Wuttisittikulkij, “Analyzing the Impact and Prospects of Metaverse in Learning Environments Through Systematic and Case Study Research,” IEEE Access, vol. 11, 2023, doi: 10.1109/ACCESS.2023.3340734.
[3] T. Huynh-The et al., “Blockchain for the metaverse: A Review,” Future Generation Computer Systems, vol. 143, 2023, doi: 10.1016/j.future.2023.02.008. [4] M. Wang, H. Yu, Z. Bell, and X. Chu, “Constructing an Edu-Metaverse Ecosystem: A New and Innovative Framework,” IEEE Transactions on Learning Technologies, vol. 15, no. 6, 2022, doi: 10.1109/TLT.2022.3210828. [5] J. Martín-Gutiérrez, C. E. Mora, B. Añorbe-Díaz, and A. González-Marrero, “Virtual technologies trends in education,” Eurasia Journal of Mathematics, Science and Technology Education, vol. 13, no. 2, 2017, doi: 10.12973/eurasia.2017.00626a. [6] B. Ravichandran and K. Shanmugam, “Adoption of EdTech products among college students: a conceptual study,” Management Matters, vol. 21, no. 1, 2024, doi: 10.1108/manm-07-2023-0026. [7] T. M. Ndofirepi and P. Gavai, “The adoption of mobile banking among college students in Zimbabwe,” African Journal of Business and Economic Research, vol. 14, no. 4, 2019, doi: 10.31920/1750-4562/2019/14n4a5. [8] Y. M. Tang, K. Y. Chau, A. P. K. Kwok, T. Zhu, and X. Ma, “A systematic review of immersive technology applications for medical practice and education - Trends, application areas, recipients, teaching contents, evaluation methods, and performance,” 2022. doi: 10.1016/j.edurev.2021.100429. [9] Z. Karbasi and S. R. Niakan Kalhori, “Application and evaluation of virtual technologies for anatomy education to medical students: A review,” Med J Islam Repub Iran, 2020, doi: 10.47176/mjiri.34.163. [10] J. Xiong, E. L. Hsiang, Z. He, T. Zhan, and S. T. Wu, “Augmented reality and virtual reality displays: emerging technologies and future perspectives,” 2021. doi: 10.1038/s41377-021-00658-8. [11] K. Yin, Z. He, J. Xiong, J. Zou, K. Li, and S. T. Wu, “Virtual reality and augmented reality displays: Advances and future perspectives,” 2021. doi: 10.1088/2515-7647/abf02e. [12] A. Antwi-Boampong, “Towards a faculty blended learning adoption model for higher education,” Educ Inf Technol (Dordr), vol. 25, no. 3, 2020, doi: 10.1007/s10639-019-10019-z. [13] A. Granić, “Educational Technology Adoption: A systematic review,” Educ Inf Technol (Dordr), vol. 27, no. 7, 2022, doi: 10.1007/s10639-022-10951-7. [14] H. Lee, Y. Xu, and A. Porterfield, “Consumers’ adoption of AR-based virtual fitting rooms: from the perspective of theory of interactive media effects,” Journal of Fashion Marketing and Management, vol. 25, no. 1, 2021, doi: 10.1108/JFMM-05-2019-0092. [15] S. Kavanagh, A. Luxton-Reilly, B. Wuensche, and B. Plimmer, “A systematic review of Virtual Reality in education,” 2017. [16] N. Kshetri, D. Rojas-Torres, and M. Grambo, “The Metaverse and Higher Education Institutions,” IT Prof, vol. 24, no. 6, 2022, doi: 10.1109/MITP.2022.3222711. [17] N. Kshetri, “Metaverse in Higher Education Institutions: Drivers and Effects,” Academy of Management Proceedings, vol. 2024, no. 1, 2024, doi: 10.5465/amproc.2024.15498abstract. [18] N. N. Masnadi, “The Development of a Metaverse App for a Blended Learning in Higher Education Institutions,” 2023. doi: 10.4018/978-1-6684-6097-9.ch006. [19] S. Kaddoura and F. Al Husseiny, “The rising trend of Metaverse in education: challenges, opportunities, and ethical considerations,” PeerJ Comput Sci, vol. 9, 2023, doi: 10.7717/peerj-cs.1252. [20] A. Tlili et al., “Is Metaverse in education a blessing or a curse: a combined content and bibliometric analysis,” 2022. doi: 10.1186/s40561-022-00205-x. [21] A. Almarzouqi, A. Aburayya, and S. A. Salloum, “Prediction of User’s Intention to Use Metaverse System in Medical Education: A Hybrid SEM-ML Learning Approach,” IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2022.3169285. [22] A. Aburayya et al., “SEM-machine learning-based model for perusing the adoption of metaverse in higher education in UAE,” International Journal of Data and Network Science, vol. 7, no. 2, 2023, doi: 10.5267/j.ijdns.2023.3.005. [23] S. A. Salloum et al., “Novel machine learning based approach for analysing the adoption of metaverse in medical training: A UAE case study,” Inform Med Unlocked, vol. 42, 2023, doi: 10.1016/j.imu.2023.101354. [24] S. H. Lee, H. Lee, and J. H. Kim, “Enhancing the Prediction of User Satisfaction with Metaverse Service Through Machine Learning,” Computers, Materials and Continua, vol. 72, no. 3, 2022, doi: 10.32604/cmc.2022.027943. [25] T. F. Tan et al., “Metaverse and Virtual Health Care in Ophthalmology: Opportunities and Challenges,” Asia-Pacific Journal of Ophthalmology, vol. 11, no. 3, 2022, doi: 10.1097/APO.0000000000000537. [26] L. S. Marques, C. Gresse Von Wangenheim, and J. C. R. Hauck, “Teaching machine learning in school: A systematic mapping of the state of the art,” Informatics in Education, vol. 19, no. 2, 2020, doi: 10.15388/INFEDU.2020.14. [27] Ovat Friday Aje and Anyandi Adie Josephat, “The particle swarm optimization (PSO) algorithm application – A review,” Global Journal of Engineering and Technology Advances, vol. 3, no. 3, 2020, doi: 10.30574/gjeta.2020.3.3.0033. [28] A. G. Gad, “Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review,” Archives of Computational Methods in Engineering, vol. 29, no. 5, 2022, doi: 10.1007/s11831-021-09694-4. [29] J. Tang, G. Liu, and Q. Pan, “A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends,” 2021. doi: 10.1109/JAS.2021.1004129. [30] A. Tharwat, “Classification assessment methods,” Applied Computing and Informatics, vol. 17, no. 1, 2018, doi: 10.1016/j.aci.2018.08.003. [31] P. ElKafrawy, A. Mausad, and H. Esmail, “Experimental Comparison of Methods for Multi-label Classification in different Application Domains,” Int J Comput Appl, vol. 114, no. 19, 2015, doi: 10.5120/20083-1666. [32] M. Sitarz, “Extending F1 Metric, Probabilistic Approach,” Advances in Artificial Intelligence and Machine Learning, vol. 3, no. 2, 2023, doi: 10.54364/AAIML.2023.1161.