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研究基于TextCNN模型的英语教科书可读性评估方法及其在教学设计中的应用

Ying Qin¹, Azeem Irshad²

¹School of Foreign Languages, Wuzhou University, Wuzhou, China.

PeerJ. Computer science

|March 4, 2024

概括

这项研究开发了一种深度学习模型,以评估中国大学生对英语阅读材料的困难. 该模型显著提高了学生的阅读水平和兴趣,提高了英语学习的有效性.

科学领域:

自然语言处理自然语言处理.
教育技术的教育技术
人工智能的人工智能

背景情况:

英语水平对于中国大学生学术和职业成功至关重要.
许多学生在英语阅读材料方面扎,阻碍了有效的学习.
现有的可读性评估可能无法充分解决大学生不同的英语水平.

研究的目的:

开发一种基于深度学习的模型来评估针对中国大学生量身定制的英语文本的可读性.
提供个性化的英语阅读材料,与学生的个性能力相匹配.
提高英语语言学习的整体有效性.

主要方法:

一套大学英语教科书的数据集被策划用于可读性分析.
一个文本卷积神经网络 (TextCNN) 模型被设计和训练用于文本可读性评估.
该模型在参数优化后在自建数据集上实现了90%的准确性.

主要成果:

比较实验教学在使用深度学习可读性评估的学生的阅读水平和兴趣显著改善.
基于TextCNN的可读性评估方法在实验环境中被证明是科学和有效的.
实验组的学生表现出更强的参与和理解能力.

结论:

基于深度学习的文本可读性评估是改善大学生英语学习成果的可行和有效方法.
基于可读性评估的个性化材料选择可以提高学习效率和学生的动力.
进一步的研究将专注于扩大数据集,并验证该模型在不同学生群体中的通用性.

关键词:

深度学习是一种深度学习.英语阅读阅读英语阅读可读性评估阅读性评估文本CNNCNN 在线阅读

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