Research on the Path to Improve the Educational Ability of College Counselors Based on Fuzzy Information Big Data Algorithm
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Abstract
Fuzzy information big data algorithms provide an effective approach for processing uncertain and heterogeneous educational data, enabling scientific evaluation and targeted improvement of college counselors’ educational abilities. Such data-driven evaluation frameworks are also valuable in engineering education fields, including electromagnetic, antenna, and communication engineering talent cultivation, where accurate assessment of educational effectiveness and professional development is increasingly important. This study constructs a counselor competency development framework based on fuzzy set theory, fuzzy comprehensive evaluation, and association rule mining. The proposed framework establishes a multi-dimensional ability evaluation system, generates competency profiles, and supports personalized training optimization. Experimental results indicate that the fuzzy feedback mechanism independently improves counselors’ educational abilities by 8.6% and increases the achievement rate of professional development goals by 23.8%. The findings demonstrate the effectiveness of fuzzy information processing and big data analysis for educational management and provide a data-driven methodology for improving educational quality and professional development in complex talent-training environments.
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