在语言课堂中语言库和数据驱动学习 (DDL):系统性审查
Amel Lusta1, Özcan Demirel1, Behbood Mohammadzadeh2
1Cyprus International University (Dept. of ELT), Haspolat, Lefkoşa, Cyprus.
Heliyon
|December 18, 2023
概括
使用 corpora 的数据驱动学习 (DDL) 增强了语言教育. 定制任务和团体学习等有效策略有助于低水平的学生,尽管仍然存在挑战.
科学领域:
- 语料库的语言学.
- 语言教育 语言教育教育
- 教学方法 教学方法
背景情况:
- 语料库语言学利用电子文本集合进行语言分析.
- 数据驱动学习 (DDL) 在语言教学中用于教学目的的 corpora.
- DDL为改善教学和学习提供了传统方法的替代方案.
研究的目的:
- 系统地审查语言教育中关于语料库和DDL的文献.
- 分析DDL的实施,教学实践和语言课堂上的挑战.
- 确定有效的策略,以增强人体参与度.
主要方法:
- 1997年至2022年间发表的89项研究的系统文献综述.
- 使用关键词搜索数据库:"DDL"",语文库"和相关术语.
- 收录标准:英语,同行评审的文本与可访问的PDF,专注于课堂干预.
主要成果:
- 作为语言教育中的教学工具,DDL显示出巨大的潜力.
- 挑战和局限性阻碍了DDL对语言学习的充分积极影响.
- 有效的策略包括为低水平学习者定制任务,指导,支持和协作学习.
结论:
- DDL是语言教育的一个有希望的方法,但实施需要仔细考虑挑战.
- 具体的策略可以促进有意义的语料库参与,特别是对于水平较低的学生.
- 进一步的研究可能会探索优化DDL干预措施,以最大限度地提高语言学习成果.
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