Generalizing linguistic patterns through data-driven learning: A study of the dative alternation in Japanese learners of English

This study examines whether learners exposed to specific example sentences through data-driven learning (DDL) can not only identify generalized linguistic patterns but also apply the patterns to other expressions, thereby demonstrating that DDL is a learning method based on a usage-based model. Forty-three Japanese learners of English participated in DDL activities to study the use of six verbs from two verb classes (three from the Throw class and three from the Whisper class) in terms of the dative alternation. Specifically, they studied whether these verbs can be used in the double object (DO) construction or the prepositional dative (PD) construction. The participants underwent pre-, post-, and delayed post-tests, during which they evaluated the grammaticality of sentences containing the studied verbs, as well as unstudied verbs from the same classes and verbs from the control classes (the Send and Mention classes). A cumulative link mixed model (CLMM) was employed to analyse the effects of test timing (pre/post/delayed post), learning (studied/unstudied), and construction (PD/DO) on test scores. The results showed that learners made more correct judgments on the post-test than on the pre-test. This improvement was observed not only for the studied verbs but also for unstudied verbs from the same classes, and even for verbs from the control classes. This indicates that DDL embodies the idea of a usage-based model; that is, learners generalize linguistic patterns through language experience. Furthermore, the learning effects were retained even in the delayed post-test, suggesting that DDL is not merely a tool for referencing word usage but also a learning method that converts input into intake.