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首页> 外文期刊>Journal of computational methods in sciences and engineering >Student offline classroom concentration identification research based on deep learning
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Student offline classroom concentration identification research based on deep learning

机译:Student offline classroom concentration identification research based on deep learning

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摘要

During the reform of the deep teaching model, students' deep learning quality was affected and restricted by various factors. During the offline class learning process of students, the concentration of deep learning directly affects the quality of learning. This article analyzes the study focus of students in deep learning models, conducts research on the quality of class offline learning of different students, quantifies the factors that affect students' deep learning, and builds an analysis model for quantitative comparison. Important influence factor affecting students' offline classroom concentration, through targeted measures, improve teaching methods and quality, optimize classroom teaching models, use various methods and measures to effectively improve learning focus, and further promote the reform of teaching models. The level of concentration of students' learning has been steadily improved, and the model of deep learning is proposed to help the teaching model reform.

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