Journal of Engineering, Project, and Production Management, 2026, 16(5), 2025-357
A Fusion Model Combining Anomaly Detection and Multidimensional Measurement: Evaluation of Vocational Education Faculty Competencies
Director, Human Resources Department, Wuhu Vocational Technical University, Wuhu, 241000, China, E-mail: Leizzhul@outlook.com
Project Management
Received February 16, 2026; revised May 18, 2026; accepted May 27, 2026
Available online June 17, 2026
Abstract: This study addresses issues in current vocational education faculty competency evaluations, including high data noise, inconsistent rater strictness, and inadequate scale reliability and validity. It proposes a two-stage integrated model. The first stage employs the Local Outlier Factor (LOF) algorithm to detect and clean anomalies in raw evaluation data. The second stage feeds the cleaned data into a multi-faceted Rasch model to achieve separation and objective estimation of multidimensional parameters, including teacher competency, task difficulty, and rater strictness. Experimental validation using real-world data demonstrates that this model significantly enhances evaluation data quality, delivers fairer, more stable, and interpretable teacher competency assessments, and supports the generation of personalized “competency-task difficulty” diagnostic charts. This study offers a data-driven, algorithm-enhanced intelligent solution for the evaluation of vocational education teacher’s competency, combining theoretical innovation with practical application value.
Keywords: Teacher competency evaluation, vocational education, anomaly detection, local outlier factor, multidimensional Rasch model, data cleansing, intelligent evaluation system.
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Citation: Zhu, L. (2026). A Fusion Model Combining Anomaly Detection and Multidimensional Measurement: Evaluation of Vocational Education Faculty Competencies. Journal of Engineering, Project, and Production Management, 16(5), 2025-357.
