Exploring Factors Contributing to Chinese Early Childhood Educators’ Work Meaningfulness: A Machine Learning Approach (Poster 22)

• In Event: AERA Poster Session Thursday 9:50 a.m.
  In Poster Session: Exploring Factors Contributing to Chinese Early Childhood Educators’ Work Meaningfulness: A Machine Learning Approach

Abstract

Enhancing work meaningfulness is a promising strategy for helping early childhood educators (ECEs) cope with systemic challenges at work. While previous studies identified various contributors to ECEs’ work meaningfulness, they examined limited factors at a time and did not assess their relative importance. Based on the job demands-resources model, we employed a machine learning approach (random forest algorithm) to identify the robust factors associated with work meaningfulness among 1,198 Chinese ECEs from 50 kindergartens. Results indicated that leadership quality and community sense at work were the strongest contributors, followed by self-efficacy, self-control, resilience, influence at work, salary, work experience, quantitative and cognitive demands. This study extends the utility of the JD-R theory in explaining work meaningfulness and inform effective interventions.

Authors

• Juan Yan, The Education University of Hong Kong

• Jianbin Li, The Education University of Hong Kong