School of Foreign Language, Beihua University, Jilin 132011, China.
School of Literature and Journalism, Yantai University, Yantai, Shangdong 264005, China.
Comput Intell Neurosci. 2022 Aug 31;2022:7391258. doi: 10.1155/2022/7391258. eCollection 2022.
In order to improve the defect that the quality of English flipped classroom teaching cannot be quantitatively evaluated, an English flipped classroom teaching model based on big data learning analysis is proposed. In the English flipped classroom teaching mode, which applies the flipped classroom teaching mode, the classroom teaching links are changed, the preview feedback, joint answer and question between teachers and students, classroom teaching, and teachers' questions are taken as the key links of classroom teaching, and the teacher education and school management system are improved, so as to complete the reform of English flipped classroom teaching mode. The convolution neural network is used to extract the evaluation text features, mine the association rules of massive evaluation text data through the Apriori algorithm, determine the evaluation index of English flipped classroom teaching quality, and complete the evaluation of English flipped classroom teaching quality by using the decision tree method in big data analysis. The experimental results show that the proposed method can quantitatively evaluate the quality of English flipped classroom teaching by using the evaluation text, and the evaluation accuracy and recall rate are higher than 98%, which can realize the objective evaluation of English flipped classroom teaching quality.
为了提高英语翻转课堂教学质量无法定量评估的缺陷，提出了一种基于大数据学习分析的英语翻转课堂教学模式。在应用翻转课堂教学模式的英语翻转课堂教学模式中，改变了课堂教学环节，将预习反馈、师生联合解答问题、课堂教学和教师提问作为课堂教学的关键环节，并完善了教师教育和学校管理体系，从而完成了英语翻转课堂教学模式的改革。采用卷积神经网络提取评价文本特征，通过 Apriori 算法挖掘海量评价文本数据的关联规则，确定英语翻转课堂教学质量的评价指标，利用大数据分析中的决策树方法完成英语翻转课堂教学质量的评价。实验结果表明，该方法可以利用评价文本对英语翻转课堂教学质量进行定量评估，评估准确率和召回率均高于 98%，可以实现英语翻转课堂教学质量的客观评估。
