Investigating metalinguistic written corrective feedback focused on EFL learners’ discourse markers accuracy in mobile-mediated context

Abstract

Abstract Among a growing body of research that examined the contradictory role of written corrective feedback (WCF) in development of L2 writing accuracy, this study investigated the possible impact of focused metalinguistic WCF on discourse markers (DMs) in writing performance of an intact group of 42 Iranian English as a Foreign Language (EFL) learners over an eight-week period. In an authentic, situated, and personalized learning platform, giving and receiving WCF were made possible only through the mobile-mediated application of WhatsApp. Before participants wrote on selected elicitation topics, they had taken part in a 2004 version of Oxford Preliminary Test in order to be screened for their initial differences in writing performance. After receiving metalinguistic WCF on their scripts, participants were required to work on the coded feedback and try to eliminate the DM errors in their revised writing assignment. After collecting the scripts over an eight-week period, the content of written assignment was thematically analyzed using NVivo 21 Software for the additive, adversative, causal and temporal DMs, following Halliday and Hasan’s (Cohesion in English., 1976) typology. In a convergent mixed-methods design, the content analysis of the qualitative data showed a larger distribution of additive DM than adversative, causal, and temporal DMs in all participants’ written scripts. Exploring the possible impact of metalinguistic WCF on improving the DMs accuracy, analysis of the frequency count data with Statistical Package for the Social Sciences (SPSS) multivariate Chi-square test reported the fluctuation and unsystematic patterns of distribution for four types of DMs with no sign of significant long-term improvement in DMs accuracy after receiving metalinguistic WCF. These findings implied further research on practicing alternative WCF strategies focused on variety of error types in actual and virtual L2 writing environments.

Keywords

• Corrective
• Discourse markers
• Feedback
• Metalinguistic
• Mobile-mediated