语料库数据驱动的外语学习:思想、方法和技术被引量：160

Corpus-base data-driven foreign language learning： Ideas, methods and technology

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摘要 Tim Johns于20世纪90年代初提出“数据驱动学习”（data-driven learning，简称DDL），代表了一种新的基于语料库数据学习外语的方法，挑战了以教师和教科书为中心的传统外语教学。随着语料库技术在外语教学中的应用与普及，DDL方法引起众多研究者的关注。本文分析了DDL学习外语的四大特征：1）以学生为中心，培养其自主学习能力；2）提供学生真实语言材料，营造真实语言学习环境；3）强调学生自我探索和发现的学习过程；4）主张自下而上的归纳式学习。此外，文章重点讨论了实现DDL的方法和技术，即KWIC检索、分析搭配、类联接和扩展语境、使用主题词和制作课件等方法。通过这些方法，学生不仅实现了自主学习，而且掌握了地道的语言。 “Data-driven learning” initially proposed by Tim Johns in 1991 is regarded as a new corpus-based approach to foreign language learning and teaching （FLLT） and a challenge to the traditional FLLT which is usually teacher-or-textbook-centered, As the use of corpora becomes more and more popular among language teachers and learners, “data-driven learning” attracts due attention. This paper first comments on the four features of data-driven learning： 1 ） being learner-centered and aiming to develop learners＇ autonomous learning; 2 ） providing learners with real data and authentic learning environment; 3） focusing on the process of self-discovery; 4） being a bottom-up and inductive learning method. Moreover, this paper discusses the methods of DDL, namely KWIC concordancing, analyzing collocation, colligation and extending context, using keywords and making courseware. It is argued that by using these methods, learners may learn autonomously and achieve the attainment of native-like proficiency in the full range of understanding, speaking, reading and writing skills in the target language.

作者甄凤超

机构地区上海交通大学外国语学院上海闵行

出处《外语界》 CSSCI 北大核心 2005年第4期19-27,40,共10页 Foreign Language World

关键词数据驱动学习语料库外语教学电化教学 data-driven learning corpus foreign language teaching

分类号 H319 [语言文字—英语] G434 [文化科学—教育技术学]

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参考文献11

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