Gamified Environmental Education Platform for School and Colleges
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Abstract
This paper presents a novel meta-ensemble machine learning framework designed to dynamically adapt gamified environmental education platforms for school and college students. While existing gamified systems effectively initiate student interest in sustainability, they often suffer from long-term engagement drops due to static reward mechanics. Our approach addresses this limitation by integrating traditional classifiers (Random Forest, SVM) with advanced gradient boosting models (XGBoost, LightGBM, CatBoost) through voting, stacking, and bagging techniques to predict student drop-off and dynamically adjust task difficulty. Experiments conducted on a dataset of student interactions with an environmental tracking application achieved a remarkable predictive accuracy of 97.5%, outperforming individual models. Crucially, this research differs from existing literature by moving beyond singular predictive algorithms and static gamification, offering a hierarchical architecture that recalibrates ecological tasks (e.g., carbon tracking, waste reduction challenges) in real-time based on cognitive load and behavioral patterns
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References
[1] McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). "Communication-Efficient Learning of Deep Networks from Decentralized Data." Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS).
[2] Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). "Gamification: Using Game-Design Elements in Non-Gaming Contexts." CHI '11 Extended Abstracts on Human Factors in Computing Systems.
[3] Lim, W. Y. B., Luong, N. C., Hoang, D. T., Jiao, Y., Liang, Y. C., Yang, Q., Niyato, D., & Miao, C. (2020). "Federated Learning in Mobile Edge Networks: A Comprehensive Survey." IEEE Communications Surveys & Tutorials, 22(3), 2031-2063.
[4] Bai, M. (2024). "Leveraging Gamification Strategies for Enhanced Environmental Education." International Journal of Emerging Technologies in Learning, 19(2), 131-145.
[5] Chen, T., & Guestrin, C. (2016). "XGBoost: A Scalable Tree Boosting System." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
[6] Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T. (2017). "LightGBM: A Highly Efficient Gradient Boosting Decision Tree." Advances in Neural Information Processing Systems, 30.
[7] Breiman, L. (2001). "Random Forests." Machine Learning, 45(1), 5-32.
[8] Wolpert, D. H. (1992). "Stacked Generalization." Neural Networks, 5(2), 241-259.
[9] Abadi, M., et al. (2016). "TensorFlow: A System for Large-Scale Machine Learning." 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), 265-283.