运动会让大脑变得更好:一项关于体育活动对大学生学术成绩的影响的数据挖掘研究
Shuang Du1, Hang Hu2, Kaiwen Cheng1
1College of Language Intelligence, Sichuan International Studies University, Chongqing, China.
Frontiers in psychology
|November 1, 2023
概括
定期的体力活动和一致的运动习惯,比如每周跑1-2次16-25分钟,显著提高了大学生的学业表现. 机器学习模型确定了提高成绩的这些关键因素.
科学领域:
- 体育教育的体育教育
- 机器学习 机器学习
- 学术表现 学术表现 学术表现
背景情况:
- 身体活动 (PA) 与学业成绩之间的关系是一个关键的研究领域.
- 很少有研究使用机器学习来分析与学术成果相关的PA行为.
研究的目的:
- 用机器学习研究体育活动对本科生学业绩的影响.
- 识别与更高学业成绩相关的特定体育活动模式.
主要方法:
- 在12周内收集了2219名本科生关于体力活动和学业表现的数据.
- 利用正在运行的应用程序界面来提取行为指标.
- 开发了使用CHAID决策树进行回归分析和显著性检测的两个模型.
主要成果:
- 更高的学业成绩需要定期体育活动和减少平均步骤频率.
- 与88%的同龄人相比,每周运动1次16-25分钟的学生表现出更高的学业成绩.
- 决策树模型提高了物理观测数据分析的有效性和可靠性.
结论:
- 机器学习,特别是决策树,为分析体育活动数据及其对学业成功的影响提供了强大的方法.
- 调查结果为教育机构提供了可操作的见解,以促进体育活动以提高学生的学业成绩.
- 优化体育活动模式可以是提高大学生学业绩的战略方法.
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