Framework for evaluating statistical models in physics education research

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 rece...

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Autores principales:	John M. Aiken, Riccardo De Bin, H. J. Lewandowski, Marcos D. Caballero
Formato:	article
Lenguaje:	EN
Publicado:	American Physical Society 2021
Materias:	Special aspects of education LC8-6691 Physics QC1-999
Acceso en línea:	https://doaj.org/article/214d97928e824b449e4598b59d307920
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spelling	oai:doaj.org-article:214d97928e824b449e4598b59d3079202021-12-02T16:44:14ZFramework for evaluating statistical models in physics education research10.1103/PhysRevPhysEducRes.17.0201042469-9896https://doaj.org/article/214d97928e824b449e4598b59d3079202021-07-01T00:00:00Zhttp://doi.org/10.1103/PhysRevPhysEducRes.17.020104http://doi.org/10.1103/PhysRevPhysEducRes.17.020104https://doaj.org/toc/2469-9896Across 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.John M. AikenRiccardo De BinH. J. LewandowskiMarcos D. CaballeroAmerican Physical SocietyarticleSpecial aspects of educationLC8-6691PhysicsQC1-999ENPhysical Review Physics Education Research, Vol 17, Iss 2, p 020104 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Special aspects of education LC8-6691 Physics QC1-999
spellingShingle	Special aspects of education LC8-6691 Physics QC1-999 John M. Aiken Riccardo De Bin H. J. Lewandowski Marcos D. Caballero Framework for evaluating statistical models in physics education research
description	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.
format	article
author	John M. Aiken Riccardo De Bin H. J. Lewandowski Marcos D. Caballero
author_facet	John M. Aiken Riccardo De Bin H. J. Lewandowski Marcos D. Caballero
author_sort	John M. Aiken
title	Framework for evaluating statistical models in physics education research
title_short	Framework for evaluating statistical models in physics education research
title_full	Framework for evaluating statistical models in physics education research
title_fullStr	Framework for evaluating statistical models in physics education research
title_full_unstemmed	Framework for evaluating statistical models in physics education research
title_sort	framework for evaluating statistical models in physics education research
publisher	American Physical Society
publishDate	2021
url	https://doaj.org/article/214d97928e824b449e4598b59d307920
work_keys_str_mv	AT johnmaiken frameworkforevaluatingstatisticalmodelsinphysicseducationresearch AT riccardodebin frameworkforevaluatingstatisticalmodelsinphysicseducationresearch AT hjlewandowski frameworkforevaluatingstatisticalmodelsinphysicseducationresearch AT marcosdcaballero frameworkforevaluatingstatisticalmodelsinphysicseducationresearch
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Framework for evaluating statistical models in physics education research

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