Analysing the longitudinal course selection panel data (2014-2020) of K-12 teachers from Zhejiang province: a comprehensive study on in-service training needs

Abstract

ABSTRACT Effective teacher training programs rely on accurate need analysis. However, traditional data sources often limit the comprehensive understanding of teachers’ and stakeholders’ needs. This study examines panel data comprising K-12 teachers’ course selections from 2014 to 2020 on the management platform of Zhejiang Province, China. Employing quantitative analysis techniques such as word frequency, co-occurrence, variability, and regression analysis on the corpus of teacher training, this paper explores the significance of these analyses in understanding training needs. The results reveal significant patterns in word frequency, co-occurrence, difference, and regression analyses, aligning with previous studies. Based on these findings, we propose that training topics should address education reform, meeting the needs of government and school leaders. Additionally, training methods should integrate theory and practice to cater to the needs of training designers, and training programs should aim to enhance teachers’ teaching abilities. This paper contributes to the existing literature on training needs analysis, showcasing how the integration of teacher education and corpus linguistics expands cross-disciplinary research and addresses the issue of drawing unreliable conclusions from limited evidence. The research findings offer valuable insights to stakeholders.