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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.

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概括
此摘要是机器生成的。

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.

关键词:
穆斯林社会穆斯林社会.学术表现 学术表现 学术表现教育公平的教育公平.机器学习是机器学习.自我有效性的自我效能.自己监管的自我监管.社会情感学习是社会情感学习.学生的福祉 学生的福祉

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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.