使用阿尔法功率建模学生的认知成就技能,转换了林德利的概率分布
Shibiru Jabessa Dugasa1, Butte Gotu Arero2
1Department of Mathematics, Kotebe University of Education, Addis Ababa, Ethiopia.
PloS one
|July 24, 2025
概括
阿尔法功率转换的林德利概率分布 (APTLD) 回归模型最适合学生的数学技能得分. 这种统计方法增强了对数学认知成就的分析.
科学领域:
- 统计学和概率学 统计和概率学
- 教育心理学教育心理学
- 数据分析 数据分析
背景情况:
- 数学方面的认知成就对于现代社会运作至关重要.
- 现有的概率分布可能无法最佳地捕捉认知成就数据的细微差别.
- 年轻生活数据集为分析学生表现提供了丰富的来源.
研究的目的:
- 为了将阿尔法功率转换的林德利概率分布 (APTLD) 应用于学生的数学技能分数.
- 用回归模型确定最适合用于分析认知成就的概率分布.
- 将APTLD与APTEPLD,TPLD和TwPLD等其他发行版进行评估.
主要方法:
- 使用阿尔法功率的回归建模转换了林德利概率分布.
- 从Young Lives数据集中分析了数学技能得分中的认知成就.
- 使用Akaike信息标准 (AIC) 和贝叶斯信息标准 (BIC) 值进行比较的模型匹配.
主要成果:
- 学生的数学技能平均得分为37.01% (SD=14.9),以右倾分布 (平均值>中位数).
- 阿尔法功率转换的林德利概率分布 (APTLD) 回归模型显示出最合适的情况,由最低的AIC和BIC值证明.
- 家长的教育水平和生活环境 (农村/城市) 被认为是人口因素.
结论:
- APTLD回归模型在分析数学技能得分中的认知成就方面优越.
- 这种统计模型为教育数据的概率方法提供了重大进步.
- 未来的研究可以探索使用贝叶斯回归模型扩展APTLD分布.
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