Which Feedback Is More Effective? A Comparative Study on Teachers' Evaluation of Feedback Models in Essay Writing
Abstract
Feedback is a key factor in improving the writing skills of students, but providing it at a large scale remains a significant challenge. Large Language Models (LLMs) emerge as a promising solution; however, the pedagogical quality of the automatically generated feedback requires further investigation. This study examined how effective teachers perceived feedback texts generated by an LLM when the prompts were designed to reflect different aspects of feedback theory. To this end, 450 feedback texts were generated for 30 essays and evaluated by five teachers with varying feedback literacy profiles. The results showed that teachers' preferences for feedback models were strongly shaped by their profiles, indicating that there was no single universally "best" feedback model. From a learning analytics perspective, these findings demonstrate how combining theory-informed prompt design with evaluations from diverse teacher profiles can generate actionable insights for the development of scalable, AI-powered feedback systems. The study contributes to learning by showing that the effectiveness of LLM-based feedback depends on personalization not only for students but also for teachers, highlighting new directions for designing adaptive, equitable, and context-sensitive feedback analytics.