This paper reports on the cognitive and affective benefits of data-driven learning (DDL), in which Korean EFL learners at the secondary level notice and unlearn their “overgenerated be” by comparing native English-speaker and learner corpora with guided induction. To select the target language item and compile learner-corpus-based materials, writing samples of 285 learners were collected. The participants were randomly divided into traditional grammar learning and DDL groups. After providing instruction for each group, one immediate and one delayed writing sessions were implemented. Revealing a lower ratio of overgenerated be after the instruction than the control group, the DDL group showed statistically significant retention as well as immediate effects in terms of the raw counts of the target item. Based on this improvement in grammar learning and retention, DDL is considered helpful for these learners as it facilitated their efforts to discover and apply rules. In addition, their positive attitudes toward DDL including both native speaker and learner data provide useful pedagogical implications. Learning from the negative evidence produced in their own classroom helped learners, especially at lower levels, to raise their grammar consciousness and boost their motivation to learn.

Research on data-driven learning (DDL), or teaching and learning languages with the help of electronic corpora, has shown that it is both effective and efficient. Nevertheless, DDL is still far from common pedagogical practice, not least because the empirical research on it is still limited and narrowly focused. This study addresses some gaps in that research by exploring the effectiveness of DDL for teaching low-proficiency learners lexico-grammatical constructions (verb-preposition collocations) in German, a morphologically rich language. The study employed a pretest-posttest design with intact third- and fourth-semester classes for German as a foreign language at a US university. The same collocations were taught to each group during one class period, with one group at each course level taking a paper-based DDL lesson with concordance lines from a native-speaker corpus and the other one taking a traditional rule-based lesson with textbook exercises. These constructions were new to third-semester students, whereas fourth-semester students had been exposed to them in the previous semester. The results show that, whereas the DDL method and the traditional method were both effective and resulted in lexical and grammatical gains, DDL was more effective for teaching new collocations. The study thus argues in favor of using paper-based DDL in the classroom at lower proficiency levels and for languages other than English.

This study examines the role of guided induction as an instructional approach in paper-based data-driven learning (DDL) in the context of an ESL grammar course during an intensive English program at an American public university. Specifically, it examines whether corpus-informed grammar instruction is more effective through inductive, data-driven learning or through traditional deductive instruction. In the study, 49 participants completed two weeks of ESL grammar instruction on the passive voice in English. The learners participated in one of three instructional treatments: a data-driven learning treatment, a deductive instructional treatment using corpus-informed teaching materials, and a deductive instructional treatment using traditional (i.e., non-corpus-informed) materials. Results from pre-test, post-test, and delayed post-test indicated that the DDL group significantly improved their grammar ability with the passive voice, while the other two treatment groups did not show significant gains. The findings from this study suggest that in this learning context there are measurable benefits to teaching ESL grammar inductively using paper-based DDL.