Correlation Analysis of Teachers' Educational Technology Quality and Teachers' Professional Competence by Integrating Collaborative Filtering Algorithm

Abstract

In today's educational landscape, the effective integration of technology into teaching practices is paramount for fostering engaging and impactful learning experiences. Central to this integration is the competence of educators and the quality of educational technology utilized. This study presents a novel approach to correlating teachers' educational technology quality with their professional competence, utilizing a fusion of collaborative filtering algorithm and Weighted Collaborative Mamdani Optimization (WCMO). By leveraging collaborative filtering, the model extracts patterns from historical data to recommend educational technology tools tailored to individual teachers' needs and preferences. The WCMO framework further refines these recommendations by incorporating weighted factors related to teachers' professional competence, ensuring a more nuanced understanding of their instructional requirements. WCMO considers the collaborative input from multiple stakeholders or sources, assigning weighted values to each input based on their relevance or significance. This collaborative approach allows for a more comprehensive analysis of the problem space, ensuring that diverse perspectives and factors are adequately accounted for in the decision-making process. Additionally, by leveraging Mamdani fuzzy logic, WCMO can handle imprecise or uncertain input data effectively, enabling more robust and adaptive optimization outcomes. Through extensive analysis and evaluation using real-world educational datasets, proposed approach demonstrates a significant correlation between teachers' technology quality and professional competence, achieving a correlation coefficient of 0.92. Moreover, qualitative assessments reveal the effectiveness of the WCMO-enhanced recommendations in enhancing teachers' instructional practices and technology integration.