Establishing a Personalized Cultivation Model for Vocational Colleges with a Dual Mentor System Based on Big Data Analysis

Abstract

By collecting diverse data such as students learning behaviors, interests, social interactions, and study duration, the system accurately matches academic mentors within the school with industry mentors from enterprises, customizing personalized development plans. This
model significantly enhances students practical abilities and job competitiveness, optimizes the mentor team structure, and deepens the collaboration between schools and enterprises. The mentorship reform driven by big data analysis offers a new path for the high-quality development of vocational education, achieving precise and dynamic talent cultivation, making the educational process more flexible and efficient.