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通过机器学习改善教育:改善PISA分数的战略模型

Bilal Baris Alkan¹, Serafettin Kuzucuk¹, Şevki Yetkin Odabasi²

¹Department of Educational sciences, Akdeniz University, Antalya, Turkey.

|July 2, 2025

概括

这项研究确定了超越传统指标的关键因素,这些因素可以预测学生在科学,数学和阅读方面的成功. 获得技术和教学时间对国际评估的表现产生重大影响,例如国际学生评估计划 (PISA).

科学领域:

教育研究教育研究
教育中的数据科学教育中的数据科学
国际比较研究国际比较研究

背景情况:

关于学生成绩的传统研究往往侧重于经济财富和阅读习惯.
现有的研究可能无法充分捕捉跨不同教育系统的学术成功的多面性质.
国际学生评估计划 (PISA) 为了解全球教育绩效提供了关键数据集.

研究的目的:

确定影响学生成绩并预测学业成功的新变量.
通过探索更广泛的社会经济,教学和心理因素,超越传统的预测因素.
为学生在科学,数学和阅读方面的表现开发增强的预测模型.

主要方法:

使用随机森林算法进行变量重要性分析.
分析了PISA 2018数据集,包括科学,数学和阅读领域.
确定了影响高绩效国家学生成功的关键预测变量.

主要成果:

确定的主要因素包括:获得信息技术,每周的教学时间,经济社会和文化地位,父母的职业,元认知意识,PISA意识,竞争精神和阅读态度.
这些变量在PISA 2018评估中显示了学生成功的显著预测能力.
开发了结合这些变量的新预测模型.

结论:

学生的成功受到技术访问,教学时间,社会经济背景和认知/态度因素的结合的影响.
拟议的模型为旨在提高国家PISA分数的政策制定者提供了显著的优势.
这些发现可以指导实施有针对性的教育政策,以改善学生的成绩.

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