Machine Learning Ensemble Model for Predicting Adoption of Metaverse in Higher Education Using PSO Algorithm for Setting Model’s Hyperparameters
محورهای موضوعی : 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
کلید واژه: Metaverse Adoption, Higher Education, Machine Learning, Metaheuristic Setting,
چکیده مقاله :
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.
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.
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