分析学习行为:以数据为导向的方法来改善学生的时间管理和积极倾听技能
Vinayak Hegde1, Vishrutha M1, Pallavi M Shanthappa1
1Department of Computer Science, School of Computing, Mysuru, Amrita Vishwa Vidyapeetham, India.
MethodsX
|April 9, 2025
概括
这项研究强调了时间管理和积极倾听等非认知技能对于学业成功至关重要. 机器学习模型有效地预测并帮助减轻影响学生表现的负面因素.
科学领域:
- 教育心理学教育心理学
- 机器学习应用 机器学习应用
背景情况:
- 学习行为包括获取知识的策略和态度.
- 时间管理和积极倾听是影响学习的重要非认知技能.
- 学生的学术失败是一个日益关注的问题,需要进行预测性分析.
研究的目的:
- 分析学习行为及其对学业成功的影响.
- 确定非认知技能在教育中的重要性.
- 探索机器学习在理解和减轻学术挑战方面的实用性.
主要方法:
- 使用谷歌表格对350名参与者进行了调查.
- 进行了数据预处理,特征选择和模型培训.
- 评估了多个机器学习算法,随机森林被选为最可靠的模型.
主要成果:
- 随机森林模型在交叉验证过程中表现出高耐用性 (0.811012) 和准确性.
- 发现时间管理和积极倾听等非认知技能对学业成绩至关重要.
- 该研究确定了影响学生表现的关键学习行为和障碍.
结论:
- 非认知技能对学业成功和学习效率有着显著的贡献.
- 机器学习方法可以有效地分析学习行为并预测学术成果.
- 调查结果强调了有针对性的干预措施的潜力,以支持学习者并减少学业失败.
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