Framework for Evaluating Statistical Models in Physics Education Research

Autor/inn/en	Aiken, John M.; De Bin, Riccardo; Lewandowski, H. J.; Caballero, Marcos D.
Titel	Framework for Evaluating Statistical Models in Physics Education Research
Quelle	In: Physical Review Physics Education Research, 17 (2021) 2, Artikel 020104 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Aiken, John M.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2469-9896
Schlagwörter	Physics; Science Education; Educational Research; Statistical Analysis; Artificial Intelligence; Evaluation Criteria; Science Tests; Pretests Posttests; Undergraduate Students; Force Concept Inventory + Suchen Sie Ihr Suchwort? Physik; Naturwissenschaftliche Bildung; Bildungsforschung; Pädagogische Forschung; Statistische Analyse; Künstliche Intelligenz
Abstract	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 scientist-like 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. (As Provided).
Anmerkungen	American Physical Society. One Physics Ellipse 4th Floor, College Park, MD 20740-3844. Tel: 301-209-3200; Fax: 301-209-0865; e-mail: assocpub@aps.org; Web site: http://prst-per.aps.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/1/01