Speaker
Description
Data-driven learning (DDL) is widely recognized as a powerful pedagogical approach that helps learners check word usage and self-correct errors, particularly for vocabulary and grammar (Huang & Ma, 2025; Ma & Mei, 2021; Zareva, 2016). Yet despite this potential, DDL remains underused in classrooms worldwide. This presentation asks why, examining key barriers: a lack of readily available, level-appropriate teaching materials (Crosthwaite et al., 2021; Eslek-Onur & Tosun, 2023; Luo, 2025), steep learning curves and overly complicated platforms (Cakebread-Andrews & Donnellan, 2024; Crosthwaite et al., 2021; Huang & Ma, 2025), time-consuming implementation (Eslek-Onur & Tosun, 2023; Ma & Mei, 2021), and the extensive scaffolding DDL requires (Schmidt, 2022). Learners may also feel overwhelmed by a style of learning so different from traditional approaches (Huang & Ma, 2025). Despite these challenges, DDL's potential is too valuable to overlook. I will present practical strategies for integrating corpora into the classroom, including custom corpus design and targeted teacher training.