Student Evaluation Based on Association Rule Reinforcement Learning for Teaching Quality Assurance

Abstract

Optimizing university Teaching quality teaching student evaluation through association rule mining leverages data-driven insights to enhance instructional effectiveness. By analyzing student interactions, performance, and engagement patterns, association rule mining identifies key relationships among course components, such as content type, activity sequence, and learning outcomes. This approach enables educators to tailor course structures to improve student engagement and comprehension, ensuring that blended learning elements are effectively aligned to maximize educational impact and address diverse learning needs. This paper presents an exploration of the Query Swarm Blended Teaching Association Rule (QSBTAR) algorithm and its application in optimizing student evaluation with blended learning environments in China. The proposed model with the data mining approach for the examination of Student performance. The QSBTAR extracts valuable insights from educational data to establish associations between teaching components and student outcomes. Through the analysis of association rules generated by QSBTAR, the paper elucidates the intricate relationships between various instructional elements and key performance metrics such as quiz scores, participation rates, and exam performance. Subsequently, these insights are integrated into course design, facilitating improvements in student engagement, comprehension, and satisfaction. While the algorithm showcases promising results, considerations are given to its limitations, including data quality constraints and interpretability challenges.