Assessing interactional metadiscourse in EFL writing through intelligent data-driven learning: the Microsoft Copilot in the spotlight

Abstract

Abstract The purpose of the current study was twofold: examining the efficacy of data-driven learning (DDL) (hands-on and hands-off approaches) in the realization of interactional metadiscourse markers (IMMs) among English as a foreign language (EFL) learners and analyzing the learners’ perceptions of DDL. The participants consisted of 93 male and female advanced language learners randomly assigned to one of the three groups: hands-on, hands-off, and control. Throughout the duration of treatment lasting for 10 sessions, the hands-on group employed the use of Microsoft Copilot, artificial intelligence (AI) chatbot, on a computer screen to discuss and explore IMMs, but the hands-off group was exposed to IMMs through written texts that were physically printed on paper and articles to be examined through AntConc concordancing program. The control group received conventional instructional techniques including reading assigned course materials. The findings from a one-way analysis of covariance (ANCOVA) procedure indicated that both experimental groups outperformed the control group in the posttest of realizing and identifying IMMs. However, the post hoc comparisons showed statistically significant differences between the hands-on and hands-off groups, with the hands-on group performing more successfully in identifying IMMs. The results of the questionnaire data revealed that all the learners had positive perception of DDL. The results of the current study suggest using both hands-on and hands-off DDL methods helps learners develop their writing performance through metadiscourse realization.

Keywords

• Data-driven learning
• Interactional metadiscourse
• Metadiscourse realization