Optimization of Higher Vocational English Teaching Design Based on Optimizing Apriori Algorithm

In the context of rapid advancement in information technology and the explosion of big data, the education sector is facing unprecedented opportunities to improve teaching effectiveness through data-driven approaches. The emergence of big data in education has paved the way for exploring new methods and extracting meaningful insights from vast amounts of teaching data. Analyzing assessment data in vocational English teaching using data mining and analysis techniques has become an urgent need to drive evidence-based improvements in teaching practices. This paper combines the advantages of the traditional Apriori algorithm and introduces the improved O-Apriori algorithm, which addresses the performance issue of full database scanning by adopting a data partitioning approach. The core of this algorithm lies in reducing the number of database scans and candidate sets and introducing a relevance value to address the problem of inaccurate association rules obtained by the original algorithm. It identifies the correlation between the evaluation of vocational English teaching quality and Key Performance Indicator (KPI) factors in teaching content and methods, thereby accurately pinpointing the key areas to enhance teaching quality. This enables teachers to make targeted adjustments to their content and provides robust data support for optimizing vocational English teaching design. The contribution of this paper lies in the development and application of the O-Apriori algorithm, which extracts actionable knowledge from assessment data in vocational English teaching, empowering educators to make data-based adjustments to their teaching methods. The findings of this research have the potential to have a significant impact on the future of vocational English education by providing a systematic and evidence-based approach to optimize teaching design and enhance overall teaching quality.