中小学生校园欺凌问题日益受到重视,识别相关因素是降低欺凌受害风险的重要途径。机器学习方法可以帮助研究人员预测和识别个体风险行为。通过机器学习方法(即,梯度提升决策树模型,GBDT),本纵向研究旨在系统地检查个体,家庭,和学校环境因素,可以预测一年后中小学生遭受欺凌的风险。共有2767名参与者(2065名中学生,702名小学生,55.20%的女学生,T1的平均年龄为12.22)在第一波完成了24个预测因子的测量,包括个人因素(例如,自我控制,性别,grade),家庭因素(家庭凝聚力,家长控制,父母教养方式),同伴因素(同伴关系),和学校因素(师生关系,学习能力)。一年后(即,T2),他们完成了Olweus欺凌调查问卷.GBDT模型通过培训一系列基础学习者并输出预测因子的重要性排名,预测了一年后中小学生是否会遭受学校欺凌。GBDT模型表现良好。GBDT模型产生了前6个预测因子:师生关系,同伴关系,家庭凝聚力,负面影响,焦虑,否认育儿方式。保护因素(即,师生关系,同伴关系,和家庭凝聚力)和风险因素(即,负面影响,焦虑,并否认父母教养方式)与一年后中小学生中欺凌受害的风险相关,通过使用机器学习方法来识别。GBDT模型可以作为预测儿童和青少年未来欺凌受害风险的工具,并有助于提高学校欺凌干预措施的有效性。
School bullying among primary and secondary school students has received increasing attention, and identifying relevant factors is a crucial way to reduce the risk of bullying victimization. Machine learning methods can help researchers predict and identify individual risk behaviors. Through a machine learning approach (i.e., the gradient boosting decision tree model,
GBDT), the present longitudinal study aims to systematically examine individual, family, and school environment factors that can predict the risk of bullying victimization among primary and secondary school students a year later. A total of 2767 participants (2065 secondary school students, 702 primary school students, 55.20% female students, mean age at T1 was 12.22) completed measures of 24 predictors at the first wave, including individual factors (e.g., self-control, gender, grade), family factors (family cohesion, parental control, parenting style), peer factor (peer relationship), and school factors (teacher-student relationship, learning capacity). A year later (i.e., T2), they completed the Olweus Bullying Questionnaire. The
GBDT model predicted whether primary and secondary school students would be exposed to school bullying after one year by training a series of base learners and outputting the importance ranking of predictors. The
GBDT model performed well. The
GBDT model yielded the top 6 predictors: teacher-student relationship, peer relationship, family cohesion, negative affect, anxiety, and denying parenting style. The protective factors (i.e., teacher-student relationship, peer relationship, and family cohesion) and risk factors (i.e., negative affect, anxiety, and denying parenting style) associated with the risk of bullying victimization a year later among primary and secondary school students are identified by using a machine learning approach. The
GBDT model can be used as a tool to predict the future risk of bullying victimization for children and adolescents and to help improve the effectiveness of school bullying interventions.