基于图形卷积网络的大学英语课程的智能推系统
1School of Foreign Languages, Guangdong Pharmaceutical University, China.
Heliyon
|April 22, 2024
概括
本研究引入了一个图形卷积神经网络模型,以解决大学英语课程建议中的信息过载问题. 该模型通过分析课程文本和学生数据来提高教学绩效,以获得更好的课程建议.
科学领域:
- 教育技术的教育技术.
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 由于国际沟通,英语课程的普及导致了信息过载.
- 这种过度负担会对推的英语课程的有效性和整体教学表现产生负面影响.
研究的目的:
- 为大学英语课程开发一个智能推系统.
- 为了减轻信息过载对英语课程选择和学习成果的负面影响.
主要方法:
- 一个图形卷积神经网络 (GCNN) 模型被设计,结合了大学英语课程文本,学生专业和英语水平.
- 该GCNN模型在大学英语学习环境中使用近距离比较策略.
- 整合了注意力机制,以完善特征表示,提高英语技能推准确度.
主要成果:
- 拟议的GCNN模型与现有的大学英语课程推方法相比,表现优越.
- 实验数据验证了注意力增强GCNN在提供准确的课程建议方面的有效性.
- 该系统成功地整合了多层注意力建模,以实现个性化的英语技能发展.
结论:
- 开发的图形卷积神经网络模型有效地解决了大学英语课程建议中的信息过载.
- 学生数据和学习策略的整合大大提高了推质量.
- 这种方法为通过智能系统增强大学英语教育提供了一个有希望的解决方案.
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