探索老年人数字信息素养的影响因素和改进策略:基于综合学习算法的分析
Haiyan Kong1,2, Xinyu Wang3
1School of Business, Xinyang Normal University, Xinyang, China.
Digital health
|November 19, 2024
概括
集成机器学习模型准确预测影响老年人数字素养的关键因素. 调查结果强调了社会支持和数字环境的重要性,为弥合数字沟的战略提供了信息.
科学领域:
- 社会科学 社会科学 社会科学
- 计算机科学 计算机科学
- 老年学是一门学科.
背景情况:
- 之前的研究已经确定了影响老年人数字素养的人口和技术因素.
- 在全面预测和理解这些影响力以进行有效干预方面存在差距.
研究的目的:
- 应用整体机器学习算法来预测影响老年人数字信息素养的关键因素.
- 确定可行的策略,以加强老年人的数字包容性.
主要方法:
- 利用了来自2022年老年人数字沟调查的初级数据.
- 开发了使用集合算法的预测模型:随机森林,LGBM,XGBoost,AdaBoost和CatBoost.
- 确定了15个非常重要的数字信息素养预测变量.
主要成果:
- 集体学习模型在预测数字素养方面显著优于传统方法.
- CatBoost获得了最高的准确度 (86.2%),其次是随机森林 (85.5%).
- 关键预测因素包括人口,个人技术使用,社会关系支持和社会数字环境因素.
结论:
- 集体学习对于分析复杂的社会科学问题,如老年人数字素养,是有效的.
- 鉴定的因素需要多方面的战略,包括教育,社会支持和政策干预.
- 研究结果旨在改善数字化适应,缩小数字沟,提高老年人的福祉.
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