Framework for evaluating statistical models in physics education research,Physical Review Physics Education Research

当前位置： X-MOL 学术 › Phys. Rev. Phys. Educ. Res. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Framework for evaluating statistical models in physics education research
Physical Review Physics Education Research ( IF 2.6 ) Pub Date : 2021-07-28 , DOI: 10.1103/physrevphyseducres.17.020104
John M. Aiken ₁ , Riccardo De Bin ₂ , H. J. Lewandowski _{3,

4} , Marcos D. Caballero _{1,

5}

Affiliation

Across the field of education research there has been an increased focus on the development, critique, and evaluation of statistical methods and data usage due to recently created, very large datasets and machine learning techniques. In physics education research (PER), this increased focus has recently been shown through the 2019 Physical Review PER Focused Collection examining quantitative methods in PER. Quantitative PER has provided strong arguments for reforming courses by including interactive engagement, demonstrated that students often move away from scientistlike views due to science education, and has injected robust assessment into the physics classroom via concept inventories. The work presented here examines the impact that machine learning may have on physics education research, presents a framework for the entire process including data management, model evaluation, and results communication, and demonstrates the utility of this framework through the analysis of two types of survey data.

中文翻译：

评估物理教育研究中的统计模型的框架

由于最近创建的非常大的数据集和机器学习技术，在整个教育研究领域，人们越来越关注统计方法和数据使用的开发、批评和评估。在物理教育研究 (PER) 中，最近通过 2019 年物理评论 PER Focused Collection 检查了 PER 中的定量方法，显示了这种日益增加的关注。定量 PER 通过包括互动参与为改革课程提供了强有力的论据，表明学生经常因科学教育而远离科学家的观点，并通过概念清单将有力的评估注入物理课堂。这里介绍的工作考察了机器学习可能对物理教育研究产生的影响，

更新日期：2021-07-28

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文本刊介绍/投稿指南11