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基于物联网和深度学习模型的英语教学质量监测与多维分析。

English Teaching Quality Monitoring and Multidimensional Analysis Based on the Internet of Things and Deep Learning Model.

机构信息

Chongqing Metropolitan College of Science and Technology, Yongchuan, Chongqing 402167, China.

出版信息

Comput Intell Neurosci. 2022 Apr 28;2022:9667864. doi: 10.1155/2022/9667864. eCollection 2022.

DOI:10.1155/2022/9667864
PMID:35528330
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9071951/
Abstract

With the development of the times, English as the universal language in the world has been highly valued by the society and schools, and English skills have become a basic skill in the society. The school is actively developing, and in the process of reform and development, the monitoring of teaching quality is essential. Teaching quality is a complex and vague concept. The establishment of a teaching quality monitoring system helps to ensure the quality of personnel training and improve the level of education and teaching, and the quality of classroom teaching is the core content of education quality. Teaching quality monitoring is the management process of various measures and actions taken to ensure the continuous improvement of students' learning quality and to achieve certain quality standards by systematically supervising and controlling various factors affecting the teaching quality in the teaching process. The research results of the article show that (1) under the traditional teaching mode, the average grades of the three groups were 74, 72, 67, 62, and 62, respectively. The score of the oral test module is relatively low, the highest score is only 63 points, the overall score shows a low level, the academic achievement is hovering on the edge of passing, and the students' English learning situation is poor. Under the new classroom quality monitoring mode, the average scores of the three groups were 96, 92, 90, 86, and 84, respectively. Compared with the ordinary teaching mode, the scores of the five detection modules were greatly improved, and the average score of the listening module was improved. The teaching contents include 22 listening modules, 20 reading modules, 23 cloze modules, 24 translation modules, and 22 speaking modules. Overall, students' English learning level has been greatly improved. (2) Generally speaking, the overall reliability coefficient of the sample data with full English teaching content is mostly kept in the range of 0.70-0.95, and only a few parts show a low situation, which also shows the overall situation of the diversity of teaching content. The overall reliability coefficient of the sample data of good teaching methods shows a relatively high situation, the reliability coefficient of the improvement of learning interest can reach 0.93, the reliability coefficient of the understanding of the learning content can reach the highest 0.96, the problem analysis ability can reach the highest 0.97, and the innovation ability can reach 0.96. The improved reliability coefficient can reach up to 0.98, which shows the authenticity and validity of the experimental data. (3) The detection result of the new classroom quality monitoring teaching mode is the highest among several models, the accuracy rate can reach 96.42%, the recall rate can reach 97.21%, and the F1 value can reach 97.46%, indicating that the new classroom quality monitoring teaching mode is effective. Teaching performance is the highest. According to the ROC curve values of the four models, we can also conclude that the ROC value of the new classroom quality monitoring teaching has been maintained at 0.98 without major twists and turns. Whether it is in the test set or the training set, the detection results of the new classroom quality monitoring are still the highest, the accuracy rate can reach 94.42%, the recall rate can reach 94.78%, and the F1 value can reach 94.49%. After the training set runs, except the performance of the traditional teaching mode increases, the performance of the other 3 models decreases.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d03/9071951/f2588d55cd65/CIN2022-9667864.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d03/9071951/fd1bfd4d4341/CIN2022-9667864.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d03/9071951/9c7b848461d9/CIN2022-9667864.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d03/9071951/086c2b79b65e/CIN2022-9667864.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d03/9071951/fe92ada8483b/CIN2022-9667864.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d03/9071951/a1103c317bc4/CIN2022-9667864.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d03/9071951/f2588d55cd65/CIN2022-9667864.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d03/9071951/fd1bfd4d4341/CIN2022-9667864.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d03/9071951/9c7b848461d9/CIN2022-9667864.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d03/9071951/086c2b79b65e/CIN2022-9667864.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d03/9071951/fe92ada8483b/CIN2022-9667864.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d03/9071951/a1103c317bc4/CIN2022-9667864.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3d03/9071951/f2588d55cd65/CIN2022-9667864.006.jpg
摘要

随着时代的发展,英语作为世界通用语言受到社会和学校的高度重视,英语技能已成为社会的基本技能。学校正在积极发展,在改革和发展过程中,教学质量监测必不可少。教学质量是一个复杂而模糊的概念。建立教学质量监测体系有助于确保人才培养质量,提高教育教学水平,课堂教学质量是教育质量的核心内容。教学质量监控是通过系统监督和控制教学过程中影响教学质量的各种因素,采取各种措施和行动,不断提高学生学习质量,达到一定质量标准的管理过程。本文的研究结果表明:(1)在传统教学模式下,三组的平均成绩分别为 74、72、67、62 和 62,口语测试模块的分数相对较低,最高分数仅为 63 分,整体分数水平较低,学业成绩徘徊在及格边缘,学生的英语学习情况较差。在新的课堂质量监控模式下,三组的平均成绩分别为 96、92、90、86 和 84,与普通教学模式相比,五个检测模块的分数均有大幅提高,听力模块的平均分也有所提高。教学内容包括 22 个听力模块、20 个阅读模块、23 个完形填空模块、24 个翻译模块和 22 个口语模块。总体而言,学生的英语学习水平有了很大提高。(2)一般来说,全英文教学内容样本数据的整体可靠性系数大多保持在 0.70-0.95 之间,只有少数部分显示出较低的情况,这也反映了教学内容多样性的整体情况。良好教学方法样本数据的整体可靠性系数表现出相对较高的情况,学习兴趣提高的可靠性系数可达到 0.93,学习内容理解的可靠性系数可达到最高 0.96,问题分析能力可达到最高 0.97,创新能力可达到 0.96。改进的可靠性系数最高可达 0.98,这表明实验数据的真实性和有效性。(3)新课堂质量监控教学模式的检测结果在几种模式中最高,准确率可达 96.42%,召回率可达 97.21%,F1 值可达 97.46%,表明新课堂质量监控教学模式是有效的。教学表现最高。根据四个模型的 ROC 曲线值,我们还可以得出结论,新课堂质量监控教学的 ROC 值一直保持在 0.98 不变,没有大的波动。无论是在测试集还是训练集,新课堂质量监控的检测结果仍然是最高的,准确率可达 94.42%,召回率可达 94.78%,F1 值可达 94.49%。在训练集运行后,除了传统教学模式的性能提高外,其他 3 种模式的性能都有所下降。

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本文引用的文献

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Internet of things-enabled real-time health monitoring system using deep learning.基于深度学习的物联网实时健康监测系统。
Neural Comput Appl. 2023;35(20):14565-14576. doi: 10.1007/s00521-021-06440-6. Epub 2021 Sep 15.