A SYSTEMATIC REVIEW OF FEEDBACK MECHANISMS IN DISTANCE EDUCATION: EXPLORING SOURCES, TYPES, AIMS, FORMS, AND OUTCOMES
Abstract
Feedback plays a critical role in learning; however, its structure and implementation in distance education differ significantly from traditional educational practices due to the unique nature of online environments. This necessitates a systematic investigation into feedback mechanisms in distance education. This systematic review aims to explore the literature on feedback in distance education, focusing on the types, purposes, and timing of feedback, as well as their implementation and impact on students. We conducted a systematic search of relevant databases on May 5, 2024, without applying a publication year restriction, thereby including all eligible studies published up to that date in accordance with PRISMA guidelines. After applying inclusion and exclusion criteria, we included 91 studies and subjected them to content analysis. Existing literature guided the categorization of feedback, examining three dimensions: feedback types, purposes, and timing. Automated systems emerged as the most common source of feedback. Formative feedback was the predominant type, evaluation was the primary purpose, and academic performance was the most frequently measured outcome. Findings highlight the emphasis on feedback’s role in 1 improving student learning outcomes, engagement, and instructional practices. This review synthesizes key findings on feedback mechanisms in distance education, focusing on their critical role in enhancing learning outcomes. It underscores the need for innovative feedback strategies that balance personalization and scalability to address diverse learner needs across contexts. Future research should explore adaptive feedback approaches to ensure inclusivity and effectiveness in distance education settings.
Keywords
distance education, feedback, online learning, PRISMA, online response, online feedback
Ethical Statement
The authors acknowledge the use of Chat GPT4o and Gemini Advanced in facilitating various stages of writing and ideation for this paper. All contributions from the AI were reviewed, critically edited, and validated by the human authors to ensure academic rigor and adherence to ethical standards. The authors also assessed and addressed potential biases inherent in the AI-generated content. The final content, conclusions, and assertions in this paper are the sole responsibility of the human authors.
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