<p>With the ongoing development and application of technology in the writing classroom, peer feedback through computer-mediated communication (CMC) has been increasingly practiced and researched in the past couple of decades. Researchers have been interested in examining how CMC peer feedback differs from the traditional pen and paper or face-to-face (F2F) peer feedback. Results of previous research have indicated that CMC and F2F each has its own merits, and simply replacing the latter with the former is not advisable (Guardado & Shi, 2007; Ho, 2015; Liu & Sadler, 2003). Instead, researchers have suggested using the two means of communication together; and when that is the case, written asynchronous computer-mediated communication (WACMC) and traditional oral F2F (OF2F) commenting are recommended. While some researchers have suggested that WACMC should come before OF2F commenting, others recommended putting WACMC after OF2F commenting. Though the field has seen numerous studies that compare CMC with F2F commenting, both in written and oral forms, little has been done to examine the effects of WACMC and OF2F peer feedback when they are used together. To address these gaps, this study investigates how WACMC in Google Docs and traditional OF2F peer feedback affect three aspects: student comments, revisions, and writing quality. It also examines whether WACMC followed by OF2F (WACMC–OF2F sequence, henceforth) or OF2F followed by WACMC (OF2F–WACMC sequence, henceforth) works better regarding the three aspects mentioned above. In order to achieve the above aims, both quantitative and qualitative approaches were used. A quantitative approach, descriptive statistics in particular, was employed to understand the outcomes of student feedback, revisions, and writing quality from the two feedback forms and sequences. A qualitative approach was used to examine attitudinal aspects and to support quantitative findings. By means of interviews, student opinions about the feedback forms and sequences, their review and revision strategies were explored. Thematic analyses were employed to process qualitative data and results were reported in themes. Data analysis yielded several major findings. First, the student participants typically offered feedback on grammar and vocabulary in the form of suggestions, and they revised at surface and word levels. Second, the students’ last drafts had higher scores than the first, suggesting the effectiveness of student revisions. Third, in terms of feedback forms, WACMC was used as the main feedback tool for both feedback and revisions. Fourth, regarding feedback sequences, the students made more quality comments, i.e., comments that were revision-oriented, on both local and global areas in the WACMC–OF2F sequence. Fifth, also in the WACMC–OF2F sequence, the students made more revisions at global level. Sixth, the students’ writing mean scores were higher in the WACMC–OF2F than in the OF2F–WACMC sequence. Finally, results of the end-of-study survey questionnaire and student opinions showed that a majority of the students found the WACMC–OF2F sequence to be more helpful because the WACMC step better prepared them for the OF2F step. This study explores the affordances of WACMC and OF2F peer feedback. The overall conclusion of the study is both WACMC and OF2F commenting should be used together, and when that is the case, WACMC should be followed by OF2F feedback. The study contributes to the existing literature on computer-assisted language learning in two regards: (1) it examines two feedback forms that are underexplored: the WACMC and traditional OF2F commenting, and (2) it confirms that the WACMC commenting followed by traditional OF2F commenting is more helpful to student writing.</p>
Investigating Instagram as an EFL Learning Tool
