Design of comprehensive evaluation model for pre-service teachers’ Five-dimensional education based on machine learning

This paper explores China’s Five-dimensional education policy, aiming at holistic student development. It analyzes its historical and theoretical underpinnings and proposes a method for constructing a comprehensive evaluation index system for pre-service teachers. We suggest using behavioral data from campus cards and online learning systems as independent variables, with data in the Five-dimensional education system as dependent variables. Six common machine learning models are employed to train and evaluate the data, identifying the optimal model. Our research aims to uncover the nonlinear relationship between behavior and Five-dimensional education levels, facilitating comprehensive student development interventions. This research provides a new train of thought for in-depth study on education evaluation.