Affordable Language Gain? Data-Driven Learning for lower-level ESL Learners: A Mixed-Method Study in Southern China

Xin Xu, Cambridge University

The majority of English as Second Language (ESL) learners in contemporary China are disadvantaged students. Research into the provision and practice of language teaching in this key area – in other words, minimising disadvantage to students – is therefore urgently needed. Data-driven learning (DDL) has received growing attention and recognition, but not yet become the mainstream teaching practice (for example Sinclair, 2004; Reppen, 2010; Boulton, 2010). This study employs and evaluates the effectiveness of paper-based DDL materials on lower-level learners’ vocabulary acquisition in an underprivileged high school in southern China. Adopting critical realism, this study explores two key questions: 1. to what extent could DDL be significantly more effective than the traditional approach in vocabulary acquisition; 2. how does the underlying mechanism of change triggered by the intervention counteract existing regularities within a specific social and cultural conditions. The context of this study is situated in an underprivileged high school in Hunan Province. A classroom of 40 students is selected to be research participants. Data will be collected from pre-test, post-test and delayed post-test. Qualitative data should be complemented with participants review and stimulus-recalled interviews to gain insights into aforementioned research questions. If the general results were to be positive, it would enable paper-based DDL to reach a wider audience in China and beyond.

Slides: BAAL DDL 2018-Xin Xu