Analysis of Predictor Factors of Teacher Competency in Vocational Colleges: A Multinomial Logistic Regression Approach

Abstract

Teacher competency is a key pillar in ensuring the effectiveness of the Technical and Vocational Education and Training (TVET) system. This study aims to identify the factors that predict teacher competency in Vocational Colleges based on the dimensions of teaching knowledge, learning methods, and learning assessment. This study employed a quantitative cross-sectional explanatory study design involving 447 final-year Malaysian Vocational Diploma (DVM) students from Vocational Colleges in the central and southern zones of Peninsular Malaysia. Data were analysed using Multinomial Logistic Regression (MLR) analysis via SPSS software. The findings showed that all three predictor variables had a significant influence on teacher competency (p < .05). Learning assessment was identified as the most dominant predictor factor, followed by learning methods, and teaching knowledge. The findings confirm that mastery of competency in learning assessment and the implementation of industry-oriented pedagogy are important factors influencing the level of TVET teacher competency. Accordingly, this study recommends that continuous professional development programmes place emphasis on strengthening authentic assessment skills and implementing learning methods aligned with industry needs in order to comprehensively enhance teacher competency.