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在小学和中学学生中预测欺凌受害风险:基于机器学习模型

Tian Qiu¹, Sizhe Wang², Di Hu³

¹Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai 200062, China.

Behavioral sciences (Basel, Switzerland)

|January 26, 2024

概括

机器学习确定了预测学生成为学校欺凌受害者的关键因素. 与老师,同龄人和家庭凝聚力的积极关系是保护性的,而负面影响,焦虑和某些育儿方式会增加风险.

科学领域:

* 教育心理学教育心理学
* 发展心理学发展心理学
* 计算社会科学 * 计算社会科学

背景情况:

*学校欺凌是小学和中学学生的一个重大问题,需要确定风险因素.
* 机器学习为预测个人风险行为提供了先进的方法.

研究的目的:

*系统地检查个人,家庭和学校环境因素,预测一年后学生的欺凌受害者风险.
* 应用机器学习方法,特别是梯度增强决策树 (GBDT) 模型,用于此预测.

主要方法:

* 对2767名中小学生进行了纵向研究.
*数据采集于两个时间点 (T1和T2,间隔一年).
*在T1测量了24个潜在预测因素,包括个人,家庭,同行和学校因素;在T2使用奥尔韦斯欺凌问卷评估了欺凌受害度.

主要成果:

* GBDT 模型在欺凌受害方面表现出强大的预测性能.
*确定了最重要的预测因素:教师与学生的关系,同行关系,家庭凝聚力,负面影响,焦虑和否认育儿风格.
* 保护因素 (积极的关系,凝聚力) 和风险因素 (负面影响,焦虑,育儿风格) 已明确划分.

结论:

* GBDT模型有效地预测了儿童和青少年未来的欺凌受害者风险.
*鉴定的因素可以为减少欺凌提供有针对性的干预措施.
* 机器学习为提高学校欺凌预防策略的有效性提供了宝贵的工具.

关键词:

在GBDTGBDTGBDTGBDTGBDTGBDTGBD 欺凌成为受害者成为受害者生态因素生态因素纵向研究是一种纵向研究.机器学习是机器学习.在学校欺负孩子的欺凌者.

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