Transformational feedback practices: A systematic review of perspectives from higher institution educators

Abstract

Background: In our competitive world, students tend to place great emphasis on grades in higher education, as a result they often overlook valuable feedback from their educators.
Aim: This study explores innovative approaches, such as ungrading and using artificial intelligence (AI), to enhance student engagement with quality feedback, fostering deeper learning and skill development.
Setting: The study contextualises quality feedback within evolving educational environments influenced by technological advances, particularly generative AI.
Methods: Through a synthesis of peer reviewed articles published on scholarly platforms, such as Google Scholar and EBSCOhost, a systematic review is used to define the study’s purpose, and apply the transformational learning theory to further truncate key perspectives from educators.
Results: The findings of the study suggest that alternatives to traditional grading can enhance student engagement with feedback. These findings provide a foundation for implementing ungrading in higher education as a transformative teaching and learning approach. This approach aims to produce positive, detailed and actionable feedback to enhance student feedback literacy. An objective of this study is to redefine quality feedback considering technologically advanced and socio-economically dynamic educational environment.
Conclusion: Findings suggest that ungrading and generative AI enhance feedback practices in higher education, emphasising the need for ethical policies and considerations regarding AI’s limitations.
Contribution: This study highlight that effective feedback should empower students and learners, support metacognitive development, and promote educational equity, while it suggests that future research explore students’ perceptions of feedback value and the implementation of ungrading to refine feedback processes.