Rubric-based dynamic assessment and multimodal feedback: A transformative model for doctoral supervision - Homayounzadeh, Zahra; Bavali, Mohammad; Behjat, Fatemeh (Journal of Academic Language and Learning, 2025)
This study investigates the effectiveness of rubric-based dynamic assessment combined with multimodal feedback in enhancing the quality of doctoral dissertations and fostering long-term academic development. Using a mixed-methods approach, the research examines the impact of structured, multimodal feedback – delivered through written comments, audio, and video – on doctoral candidates’ dissertation quality, feedback literacy, and critical writing skills. Quantitative results reveal statistically significant improvements across all dissertation components (Academic Writing Quality, Literature Review, Methodology, and Data Analysis) from initial to final submissions, as indicated by a repeated measures ANOVA (p < 0.001). Qualitative findings highlight the importance of feedback clarity, relevance, and emotional support, with students and supervisors emphasizing the value of video feedback in providing actionable guidance and maintaining motivation. The integration of rubrics facilitated targeted revisions and enhanced feedback literacy, enabling students to independently interpret and apply feedback. The study contributes to the theoretical understanding of dynamic assessment, rooted in Vygotsky’s Zone of Proximal Development, by demonstrating how iterative, multimodal feedback scaffolds students’ learning and fosters self-regulated academic growth. Practical implications include recommendations for integrating multimodal feedback in doctoral supervision to promote both cognitive and emotional support. The findings underscore the potential of this feedback model to inform higher education policy and improve doctoral education globally.