Corpus Use in Language Learning: A Meta-Analysis
Corpus Use in Language Learning: A Meta-Analysis
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DOI：
10.1111/lang.12224
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发表时间：
2017-06-01
影响因子：
4.4
通讯作者：
Cobb, Tom
中科院分区：
文献类型：
--
作者：
Boulton, Alex;Cobb, Tom
本研究应用系统的元分析程序来总结实验和准实验调查的结果，以了解使用语料库语言学工具和技术进行第二语言学习或使用的有效性，这里称为数据驱动学习（DDL）。对代表 88 个报告足够数据的独特样本的 64 项独立研究的分析表明，DDL 方法对对照组/实验组比较 (d = 0.95) 和前/后测试设计 (d = 1.50) 产生巨大的总体影响。对调节变量的进一步研究表明，较小的效应量通常与较小的样本量相关。一些关键领域的研究刚刚开始，通过延迟后测试实现学习的持久性/迁移仍然是一个需要进一步研究的领域。尽管 DDL 研究在调查期间明显得到改善，但建议进一步改变实践和报告。
This study applied systematic meta-analytic procedures to summarize findings from experimental and quasi-experimental investigations into the effectiveness of using the tools and techniques of corpus linguistics for second language learning or use, here referred to as data-driven learning (DDL). Analysis of 64 separate studies representing 88 unique samples reporting sufficient data indicated that DDL approaches result in large overall effects for both control/experimental group comparisons (d = 0.95) and for pre/posttest designs (d = 1.50). Further investigation of moderator variables revealed that small effect sizes were generally tied to small sample sizes. Research has barely begun in some key areas, and durability/transfer of learning through delayed posttesting remains an area in need of further investigation. Although DDL research demonstrably improved over the period investigated, further changes in practice and reporting are recommended.