Abstract
As artificial intelligence has grown, intelligent technology has steadily been used in the classroom. Intelligent in-class evaluation has gained popularity in recent years. In this study, we apply two models: AE-SIS (Analytic Hierarchy Process-Entropy Weight-TOPSIS) and AW-AB (Adjusted Weight in Adaptive Boosting) to evaluate in-class teaching quality. We provide an ensemble scheme for intelligent in-class evaluation that combines the benefits of the two models. We test the current in-class evaluation criteria using classroom datasets for comparison. The outcomes show how great and successful the suggested plan is.
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Guo, J., Song, A., Bai, L., Zhao, Z., Zheng, S. (2023). An Ensemble Scheme Based on the Optimization of TOPSIS and AdaBoost for In-Class Teaching Quality Evaluation. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14254. Springer, Cham. https://doi.org/10.1007/978-3-031-44207-0_7
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