Data-driven Learning in Second Language Writing

This systematic review investigates the integration of Data-Driven Learning (DDL) in second language writing pedagogy through empirical and theoretical perspectives, analyzing pedagogical impacts, methodological constraints, and future directions. Findings demonstrate DDL’s effectiveness in improving writing accuracy, fluency, and learner autonomy through exposure to authentic linguistic corpora. Current limitations include restricted corpus accessibility, variable teacher competencies, learner adaptation barriers, and a predominance of short-term studies. Comparative analysis reveals distinct research trajectories: domestic studies prioritize error correction algorithms and automated feedback systems, while international scholarship emphasizes lexical-grammatical development and feedback mechanism validation. The synthesis identifies critical research gaps requiring large-scale longitudinal investigations, extended pedagogical interventions, and specialized teacher training programs. The paper concludes by proposing an integrative framework for optimizing DDL implementation, advocating for cross-disciplinary collaborations between computational linguistics and language pedagogy, coupled with technological innovations in corpus interface design. These findings contribute to advancing evidence-based practices in data-enhanced language education.