结构和过程:智能小学中文词汇教学理论模型
1Research Institute of International Chinese Language Education, Beijing Language and Culture University, Beijing, China.
Heliyon
|October 14, 2024
概括
本研究介绍了一种人工智能生成内容 (AIGC) 模型,用于创新的初级中文词汇教学 (ECVT). 它概述了智能ECVT系统的理论框架,增强了第二语言学习模型.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 获得第二语言的学习.
背景情况:
- 目前对第二语言教学人工智能的研究主要集中在辅助角色上,忽视了智能教学实施.
- 人工智能 (AI) 的快速发展需要在教育模式中采用创新的方法,特别是在词汇获取方面.
- 关于初级汉语词汇教学 (ECVT) 的智能,个性化的教学模式,文献中存在一个空白.
研究的目的:
- 从人工智能生成内容 (AIGC) 的角度构建初级中文词汇教学 (ECVT) 的理论模型.
- 介绍一个智能ECVT系统的开发过程.
- 为优化中文语言教学和其他语言教学模式提供科学基础的框架.
主要方法:
- 文献审查以确定17个关于ECVT教学结构的观点和19个关于教学流程的观点.
- 确定ECVT的基本宏观阶段.
- 在每个阶段中划出微层结构和过程,以推断一个理论模型.
主要成果:
- 已开发出智能ECVT的理论模型,集成AI解构和生成原则.
- 该模型概述了ECVT的宏观层次阶段和微观层次结构和流程.
- 该研究为开发智能ECVT系统提供了基础框架.
结论:
- 拟议的基于AIGC的ECVT模型为智能第二语言教学提供了一种新的方法.
- 这一框架可以作为教育工作者寻求优化教学策略和增强语言学习的宝贵参考.
- 这项研究推动了第二语言教学模式的创新,为语言教育提供了前性视角.
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