Feedback and revision in cloud-based writing: Variations across feedback source and task type
Journal: Writing & Pedagogy
Collaborative writing is one of the twenty-first century writing competencies critical for college and career success. Technology-enhanced writing platforms, such as Google Docs, can serve as effective media for written collaboration. Although cloudbased tools such as Google Docs are increasingly used in secondary schools, little is known about how students collaboratively write in these environments, including how feedback sources and types of tasks affect collaborative writing patterns. This study examined the content of feedback and revision in 424 Google Docs written by 145 sixth grade students to understand the variations in feedback and revision patterns across key contextual factors: the source of feedback (i.e., teacher vs. peer) and assigned task type (i.e., argumentative, narrative, report). We conducted a qualitative content analysis of feedback and revision, followed by Chi-square and ANCOVA analyses. With regards to variations across feedback sources, we found that teacher feedback addressed more macro-level features (e.g., content, organization) whereas student feedback focused more on micro-level features (e.g., mechanics, conventions), and neither teacher nor peer feedback led to subsequent revisions. With regards to variations across task types, we found that among the three writing genres, the narrative genre had the greatest number of coauthors and feedback activities, and most of these activities consisted of affective feedback or direct edits. In contrast, in the report genre, the feedback activities tended to focus on content and organization, and the language functions of both feedback (e.g., advice, explanation) and revision (e.g., acknowledging, clarifying) were most evident in the report genre. We discuss the implications of these findings for the design and implementation of technology-based collaborative writing tasks in academic settings, as well as the limitations and directions for future studies.
Author: Soobin Yim, Binbin Zheng, Mark Warschauer
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