Adaptive Online Assessment in Higher Education: An Improved UEMD-GA Approach with Independent Rating

This paper investigates personalized online learning platforms in higher education and their role in enhancing educational outcomes. Traditional programming assessment systems often struggle with scalability and limited functionality, failing to effectively leverage advanced recommendation algorithms and comprehensive data analytics. The proposed system integrates user interest profiles with capability metrics to deliver personalized exercise recommendations, while implementing a novel rating calculation method that eliminates dependency on external platforms. Built upon a decoupled microservice architecture using Spring Cloud and Vue.js, the system incorporates a simulated time decay. We tested our approach on a comprehensive university dataset. The results show improvements in recommendation precision and system performance over baselines, suggesting the algorithm’s applicability and scalability. This research contributes valuable theoretical insights and practical methodologies for advancing online education technologies, particularly in the context of data-driven teaching evaluation and adaptive learning systems.