Highly informative feedback using learning analytics: how feedback literacy moderates student perceptions of feedback

Abstract

Quality feedback is essential for supporting student learning in higher education, yet personalized feedback at scale remains costly. Advances in learning analytics and artificial intelligence now enable the automated delivery of personalized feedback to many students simultaneously. At the same time, recent feedback research increasingly emphasizes learner-centered approaches, particularly the role of feedback literacy—students' varying capacities to engage with and benefit from feedback. Despite growing interest, few studies have quantified how feedback literacy affects students' perceptions of feedback, especially in technology-supported contexts. To address this, we examined (1) students' perceptions of personalized, detailed feedback generated via learning analytics and (2) how feedback literacy moderated these perceptions. In a randomized field experiment, teacher education students (N = 196) participated in a week-long computer-supported collaborative learning task on cognitive activation in the classroom. Both groups received automated, personalized feedback: the control group received basic feedback on task completion, while the experimental group received detailed feedback on group processes and the quality of their collaborative statement. The highly informative feedback significantly improved perceptions of feedback helpfulness, enhanced learning insights, and supported self-reflection and self-regulation. Feedback literacy partially moderated these effects, influencing perceptions of feedback helpfulness and motivational regulation.