MuLeCo项目:学习罗曼语的L1德语学习者语料库

Q1 Arts and Humanities
Stephan Lücke , Patricia de Crignis , Johanna Wolf , Florian Zacherl
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引用次数: 0

摘要

学习者语料库在外语习得研究中的重要性及其在数据驱动学习和其他学习环境中的作用已得到广泛认可。它们已经成为外语教学和学习的宝贵资源。到目前为止,对于学校教授的罗曼语(法语、西班牙语、意大利语),还没有广泛收集来自L1德语使用者的学习者语言数据。错误标注学习语料库MuLeCo(慕尼黑学习语料库)解决了这个问题。学习者写作作品的收集旨在揭示法语、西班牙语和意大利语学习者所面临的持续挑战,同时也为开发用于外语教学的教学和数据驱动材料提供坚实的经验基础,从而有助于弥合外语习得(FLA)和外语教学(FLT)之间的差距。此外,MuLeCo还为批判性地重新审视中介语现象背景下的“错误”、“变异”和“规范”等关键概念创造了空间。本文旨在说明如何构建学习者语料库来识别外语学习和处理中持续存在的问题区域。在概述了语言和教学目标之后,文章详细介绍了收集、结构、组织、分析和使用语料库数据的方法。整个工作流程的设计是完全数字化的。语料库的核心是错误分类。用于存储和处理高度结构化语料库数据的关系数据库允许进行多重分析。文章总结了初步的分析方法和选定的发现
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The MuLeCo project: A learner corpus of L1 German learners of romance languages
The importance of learner corpora for foreign language acquisition research as well as their role in data-driven learning and other learning contexts is now widely recognised. They have become a valuable resource for both foreign language teaching and learning. To date, there is no extensive collection of learner language data from L1 German speakers for the Romance languages taught in schools (French, Spanish, Italian). This desideratum is addressed by the error-annotated learner corpus MuLeCo (Munich Learner Corpus). The collection of written learner productions aims to shed light on persistent challenges faced by learners of French, Spanish, and Italian, while also providing a solid empirical basis for developing didactic and data-driven materials for foreign language teaching—thus helping to bridge the gap between Foreign Language Acquisition (FLA) and Foreign Language Teaching (FLT). In addition, MuLeCo creates a space for critically revisiting key concepts such as “error,” “variation,” and “norm” in the context of interlanguage phenomena. This article aims to demonstrate how a learner corpus can be constructed to identify persistent problem areas in foreign language learning and processing. Following an outline of the linguistic and didactic objectives, the article presents in detail the methodology employed to collect, structure, organise, analyse, and make the corpus data accessible. The entire workflow is designed to be fully digital. At the core of the corpus lies the categorisation of errors. The relational database used for storing and handling the highly structured corpus data allows for multifold analysis. The article concludes with initial analytical approaches and selected findings
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来源期刊
Ampersand
Ampersand Arts and Humanities-Language and Linguistics
CiteScore
1.60
自引率
0.00%
发文量
9
审稿时长
24 weeks
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