在穆斯林社会使用机器学习算法预测学生的自我效能
Mohammed Ba-Aoum1,2, Mohammed Alrezq1, Jyotishka Datta3
1Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, United States.
Frontiers in big data
|December 30, 2024
概括
机器学习模型确定了自我调节,问题解决和归属作为穆斯林社会学生自我效能的关键预测因素. 这些因素显著影响学术成功和幸福感,指导有针对性的干预措施.
科学领域:
- 教育心理学教育心理学
- 机器学习在教育中的应用
- 社会文化对学习的影响.
背景情况:
- 自我效能对于学业成功和生活成果至关重要.
- 使用机器学习对自我有效性预测器的研究是有限的,特别是在穆斯林社会.
- 这项研究通过分析影响学生自我效能的因素来解决这一差距.
研究的目的:
- 确定穆斯林社会中学学生中自我效能的关键预测因素.
- 利用机器学习模型来分析这些预测因素.
- 为教育干预提供数据驱动的基础.
主要方法:
- 穆斯林社会中学学生的实证数据集.
- 使用了四种机器学习算法 (决策树,随机森林,XGBoost,神经网络).
- 预测因素包括人口,社会情绪,认知和监管因素,以及文化相关的变量.
主要成果:
- 随机森林模型表现出卓越的准确性 (R平方和RMSE).
- 自律,解决问题和归属感是最重要的预测因素.
- 感恩,宽恕,同情和创造意义显示出适度的影响;性别,情绪调节和集体主义-个体主义倾向的影响最小.
结论:
- 机器学习有效地识别了各种文化背景中的自我效能预测因素.
- 自律和社会情绪因素对于学生的成绩和福祉至关重要.
- 研究结果支持有针对性的教育干预措施,以提高穆斯林社会学生的成绩.
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