Unraveling the Power of Written Feedback: A Meta-Synthesis on Student Academic Writing
DOI:
https://doi.org/10.31849/n66j6w65Keywords:
Written feedback, Academic writing, Formative feedback, Summative feedback, Corrective feedback, Feedback delivery methodsAbstract
A single written comment can redirect a learner’s academic trajectory, yet evidence on which written feedback practices most effectively improve student academic writing remains dispersed across studies, contexts, and delivery modalities. This study addresses that gap by integrating findings on feedback types, feedback delivery, and contextual influences to clarify how written feedback supports academic writing development, particularly in EFL settings. Employing a qualitative meta synthesis, we reviewed 25 peer reviewed studies published between 2017 and 2024, selected through explicit inclusion criteria and appraised for methodological quality, then analysed using thematic coding to generate higher order interpretive themes. Findings show that formative feedback most consistently supports improvement because it guides revision decisions, scaffolds self-regulation, and builds writer confidence; in contrast, summative feedback and corrective feedback contribute mainly to evaluation and linguistic accuracy and are less likely to generate durable gains when used alone. Across delivery methods, teacher feedback, automated tools, and web platforms show potential, but impact is strongest when technology mediated input is paired with teacher mediation and clear explanation. The synthesis further indicates that student emotions and culturally shaped expectations condition trust, engagement, and whether feedback is translated into substantive textual change. The study contributes an integrated framework that treats feedback effectiveness as a system linking purpose, medium, affect, and context, with implications for feedback design, teacher feedback literacy, and responsible digital feedback policy in higher education. Future research should test which iterative feedback features drive lasting improvement and how hybrid human and intelligent feedback can be optimised for equitable learning.
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