This study explores the application of advanced association rule mining techniques to analyze the factors influencing elementary school teacher certification exam outcomes. By utilizing a large dataset provided by the Ministry of Education (covering examinations between 2016 and early 2022 and education internships from 2015 to January 2022), we investigate the relationship between teacher and student background variables—such as educational attainment, age, gender, and training institution type—and exam passing rates. An improved algorithm, the Important Association Rule Mining (IARM) method, is employed to combine traditional metrics (support, confidence, and lift) with a new importance indicator. Results indicate that factors including holding a bachelor's degree, being aged 20–24, recent graduation status, female gender, attendance at public teacher training universities, and the “practice-first-then-inspect” approach strongly correlate with higher pass rates. These findings provide a data-driven foundation for enhancing teacher training programs and optimizing certification policies.