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- W2000475810 abstract "Data-driven learning (DDL) is an inductive approach to language learning in which students study examples of authentic language and use them to ?QGSDWWHUQVRI� ODQJXDJHXVH��7K LVLQGXFWLYHDSS URDFKWROHDUQLQ JKDVWKHDGYDQW DJHV� of being learner-centered, encouraging hypothesis testing and learner autonomy, and helping develop learning skills. The approach has grown out of corpus linguistics, and it has been used to help students learn grammar and vocabulary usage. It is also possible to use it for teaching speech acts. In this paper, we contrast inductive and deductive learning and discuss an example of an exercise using data-driven learning to learn about the expressions and strategies used in apologies." @default.
- W2000475810 created "2016-06-24" @default.
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- W2000475810 date "2013-11-30" @default.
- W2000475810 modified "2023-09-24" @default.
- W2000475810 title "Data-Driven Learning of Speech Acts Based on Corpora of DVD Subtitles" @default.
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- W2000475810 doi "https://doi.org/10.14705/rpnet.2013.000151" @default.
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