Key Factors and Predictive Models of Digital Collaborative Education Based on Machine Learning

Abstract

Digital collaborative education plays a pivotal role in digital education research and significantly contributes to enhancing teaching quality. Furthermore, it provides a new impetus for family–school–community collaboration in talent development. Nevertheless, the key drivers and predictive models of digital collaborative education remain underexplored. To address this gap, this study adopts the perspective of teachers' digital literacy, focusing on primary and secondary school teachers as research subjects. Employing machine learning methods such as gradient boosting regression trees (GBRT) and random forest, we identify the key factors influencing digital collaborative education and develop predictive models. The SHapley Additive exPlanations (SHAP) framework is applied to conduct holistic, heterogeneous, and individual-level explanatory analyses, whereas accumulated local effects (ALE) plots are used for single-feature explanation. The results indicate that random forest outperforms other models in predicting digital collaborative education. Digital academic assessment, digital instructional implementation, digital teaching design, and digital instructional research and innovation are the four most important feature variables in predicting the effectiveness of digital collaborative education, with digital application emerging as the strongest predictor, followed by professional development. These key features exhibit heterogeneity in predicting digital collaborative education across gender, age, educational background, and teaching experience, demonstrating nonlinear relationships. The findings provide empirical support for advancing digital collaborative education and offer valuable insights for enhancing teachers' professional development.