Design and application of a quality evaluation index system for sustainable cultivation of big data professionals in universities based on Bloom cognitive domain classification method

This article aims to design and apply a quality evaluation index system based on Bloom's cognitive domain classification method to evaluate the training quality of big data professionals in universities. With the rapid development of big data technology, the importance of cultivating big data professionals in universities is becoming increasingly prominent. However, the existing training quality evaluation system often lacks systematicity and scientificity, making it difficult to fully reflect the comprehensive abilities of students. This article combines key knowledge of big data in the field of finance and economics with Bloom’s cognitive domain classification method to divide the cognitive process of talent cultivation into six levels: memory, understanding, application, analysis, evaluation, and creation. Through expert interviews and the Delphi method, specific evaluation indicators for each cognitive level were determined, and 18 indicators were extracted. Through data collection and empirical analysis, the performance of students at various cognitive levels was evaluated. The results showed that third year students performed the best on average at all cognitive levels, reflecting their rich learning experience and practical skills. The analysis also shows that there are significant differences in the performance of students at higher-order cognitive levels, with a coefficient of variation of 0.23 for the A1 indicator and 0.84 for the F3 indicator. This indicates that higher-order cognitive activities require higher demands from students, with significant individual differences. Through correlation analysis between various indicators, it was found that the proficiency level of tool usage in the project is strongly correlated with other indicators. Bloom’s cognitive domain classification system can reflect students’ comprehensive abilities and provide a scientific evaluation method for universities. Through continuous improvement and in-depth research, we hope to provide more scientific and comprehensive support for the cultivation of big data talents in universities.