A Coding Framework for Analyzing the Types and Levels of Peer Feedback

Peer assessment has gained increasing attention as an effective instructional approach that enhances students’ learning competence. However, its effectiveness largely depends on the quality of peer feedback. To enhance the quality of feedback, it is essential to develop methodological tools that can hierarchically evaluate the different levels of feedback produced by students. This study developed a coding framework for analyzing peer feedback quality and applied it to authentic data from two rounds of peer assessment conducted in high school science classes to both validate the framework and explore students’ feedback patterns. A total of 1,960 peer feedback comments were collected from 49 male 11th-grade students. A seven-level peer feedback coding framework was constructed based on prior literature, and its hierarchical structure was validated through Rasch analysis. Using the same dataset, the distribution, consistency, and changes in student feedback were also examined. The results confirmed the statistical validity of the hierarchical structure. Students most frequently provided low-level feedback that merely verified the appropriateness of responses, while high-level feedback that identified errors or suggested improvements was relatively rare. Student feedback also showed consistency both within items and across the overall assessment, but an overall decline in feedback quality was observed in the second round. These findings demonstrate that the developed framework can serve as a reliable tool for diagnosing and evaluating students’ feedback competence. Moreover, the study highlights the need for structured instructional support to promote high-quality feedback and provides practical implications for designing and evaluating peer assessment–based instructional interventions.