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Paper Presentation Regular Presentation

Data-driven Learning (DDL): The Analysis of Corpus Tools’ Designs to Facilitate EFL Learners' Error Correction

Sat, Jun 5, 15:30-16:00 Asia/Tokyo

Research has shown that corpus tools can facilitate non-native speakers of English, including English as a second (ESL) and foreign language learners (EFL), in their grammatical choices when using data-driven learning (DDL). Gigantic corpora and user-friendly interface are two features that were found to facilitate English learners to use corpus tools for DDL (Boisson et al., 2013). Netspeak and Linggle are two corpus tools with similar interfaces and functions that are designed with the two key features mentioned above. However, each corpus tool is designed to target different users and could further affect the data-driven learning experiences. Thus, this study analyzed how Netspeak and Linggle facilitated four college EFL learners to correct ten common types of grammatical error through data-driven learning. Searching logs and interview data were collected and analyzed to understand how learners consulted with the two corpus tools for data-driven learning. Findings showed that the affordances of the two corpus tools, particularly the operators and the interface design, led to various data-driven learning strategies for error correction, such as replacing word choices and using multiple operators. Pedagogical implications of ways to facilitate EFL learners to use corpus tools for data-driven learning will be further addressed.

  • Christine Chang

    I'm currently a Postdoctoral fellow at the Higher Education Sprout Office/ Educational Psychology and Counseling Department (NTNU-Haskins Joint Laboratory of Brain Development and Learning). My research interests are computer-assisted language learning; multimodal composition and literacy; curriculum and instruction; interdisciplinary studies and educational neuroscience (fNIRS).