Predictive analysis of college students' academic procrastination behavior based on a decision tree model

This study investigates the predictive factors of academic procrastination among college students in China during the COVID-19 pandemic using a decision tree model. Data from 776 students were analyzed, considering factors such as demographics, academic achievement, subjective well-being, smartphone addiction, negative emotions, self-esteem, life autonomy, pro-environmental behavior, and sense of school belonging. The decision tree model identified eight predictive factors, with subjective well-being, smartphone addiction, and negative emotions being the most significant. The model achieved 85.78% accuracy, offering insights for mitigating academic procrastination.

Pu Song, Xiangwei Liu, Xuan Cai, Mengmeng Zhong, Qingqing Wang, Xiangmei Zhu