To solve the one-sidedness and subjectivity in traditional teacher evaluations at vocational colleges, we're building a system based on multiple data sources. It highlights six key areas — teacher ethics, instruction, mentorship, growth, service, and innovation — and pulls data from student records, learning platforms, and smart classroom tools. We use the Analytic Hierarchy Process (AHP), along with big data tech, to assign proper weights and develop personal growth profiles. What makes it different is its five-layer structure that not only checks performance but also helps teachers reflect and improve continuously. Instead of just ending with a score, the system supports both teachers and managers: teachers can self-assess; managers gain insights for smarter decisions. The goal is to move from fixed assessments to supportive feedback, helping build a stronger, “dual-qualified” teaching team and raising the standard of vocational instruction.

Yiyao Zhang. Design of a Big Data-driven Multi-dimensional Evaluation System for "Dual-qualified" Teachers. International Journal of New Developments in Education (2025), Vol. 7, Issue 9: 70-76. https://doi.org/10.25236/IJNDE.2025.070910.

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