对性别平等的深度学习方法:预测教育指标与1D-CNN与SDG 5保持一致
Ghada Alturif1, Alaa A El-Bary2,3,4, Radwa Ahmed Osman2,4
1Department of Social Work, College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
PloS one
|September 16, 2025
概括
这项研究使用深度学习模型来预测五个国家的教育性别平等. 该模型分析历史数据以预测教育成果,帮助性别平等倡议.
科学领域:
- 社会科学 社会科学 社会科学
- 教育 技术 技术 教育 技术
- 性别研究是性别研究.
背景情况:
- 可持续发展目标5强调性别平等和赋权.
- 分析按性别分类的数据对于理解教育差异至关重要.
- 预测建模可以为推进教育中的性别平等策略提供信息.
研究的目的:
- 开发和实施一个时间序列预测模型,用于性别相关的教育成果.
- 分析不同国家的教育数据中的时间模式和差异.
- 为促进性别平等的利益相关者提供可操作的见解.
主要方法:
- 使用了一个1D卷积神经网络 (1D-CNN) 深度学习模型.
- 分析了按性别分类的人口,社会经济和教育数据.
- 在经过验证的历史数据上训练模型,以实现现实的轨迹预测.
主要成果:
- 1D-CNN模型成功预测了与性别相关的教育成果.
- 识别了教育进度中的时间模式和差异.
- 创建了针对性别平等的具体国家进展轨迹.
结论:
- 该模型为以性别为重点的教育措施提供了有价值的预测.
- 研究结果支持基于证据的规划和针对性别平等的有针对性的干预措施.
- 这项研究有助于推进教育领域及其他领域的性别平等.
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