<![CDATA[This study aims to synthesize the latest empirical evidence regarding assessment and feedback practices in Massive Open Online Courses (MOOCs) through the Systematic Literature Review (SLR) approach that follows the PRISMA guidelines. The identification process was carried out using the Scopus database with keywords related to MOOCs, assessments, and feedback, resulting in 514 initial articles. After going through the stages of screening, feasibility assessment, and selection based on the 2023–2025 publication year range, document type, language, access status, and file availability, as many as 29 articles met all inclusion criteria. Analysis of these articles shows that assessment in MOOCs is evolving towards an automated, adaptive, and data-driven approach, with the use of automated quizzes, adaptive assessments, peer assessments, and learning analytics. On the other hand, feedback mechanisms show significant transformation through the integration of generative artificial intelligence technology and learning analytics that provide fast, relevant, and personalized feedback at massive scale. The results of the synthesis also revealed that good evaluation design consistently increases the motivation, engagement, and learning outcomes of MOOC participants, while a number of challenges such as peer assessment bias, evaluation design complexity, and technological barriers still need to be considered. Overall, this study confirms that assessment and feedback are key components that determine the quality of learning in MOOCs and contribute greatly to the effectiveness of learning processes and outcomes when designed in a systematic, adaptive, and participant-oriented manner.]]>
Copyrights © 2026
