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An In-class Teaching Comprehensive Evaluation Model Based on Statistical Modelling and Ensemble Learning

机译：基于统计建模和集成学习的课堂教学综合评价模型

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摘要

Education is the foundation of our cities' and society's development. The realization of Smart City is inseparable from the construction of Smart Education. The development of science and technology has promoted the popularity and booming of information technology in the education field. With the rise of Smart Education, intelligent learning and evaluation have provided new ideas for in-class teaching evaluation. The model proposed in this article is based on existing multi-dimensional and multi-modal data set from in-class audio and video recognition, as well as movement perception and interaction analysis. We firstly designed a statistical model and an ensemble learning model for in-class teaching evaluation, which are based on Analytic Hierarchy Process - Entropy Weight Method and AdaBoost algorithm respectively. Then, we designed experiments to assess the performance of the proposed statistical model and ensemble learning model. Finally, we compared and selected better models through experiments in different evaluation indicators and combined them into our In-class Teaching Comprehensive Evaluation Model with outstanding performance.
机译：教育是我们城市和社会发展的基础。智慧城市的实现离不开智慧教育的建设。科学技术的发展促进了信息技术在教育领域的普及和蓬勃发展。随着智能教育的兴起，智能学习与评估为课堂教学评估提供了新思路。本文提出的模型基于现有的多维和多模式数据集，这些数据集来自于类内音频和视频识别以及运动感知和交互分析。首先，我们分别基于层次分析法-熵权法和AdaBoost算法设计了一种用于课堂教学评价的统计模型和整体学习模型。然后，我们设计了实验，以评估所提出的统计模型和集成学习模型的性能。最后，我们通过在不同的评估指标上进行实验来比较和选择更好的模型，并将它们组合到我们的具有优异性能的课堂教学综合评估模型中。

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