Learning Analytics in Physics Education: Equity-Focused Decision-Making Lacks Guidance!

Autor/inn/en	Grimm, Adrian; Steegh, Anneke; Kubsch, Marcus; Neumann, Knut
Titel	Learning Analytics in Physics Education: Equity-Focused Decision-Making Lacks Guidance!
Quelle	In: Journal of Learning Analytics, 10 (2023) 1, S.71-84 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Grimm, Adrian) ORCID (Steegh, Anneke) ORCID (Kubsch, Marcus) ORCID (Neumann, Knut)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Physics; Science Instruction; Learning Analytics; Equal Education; Guidelines; Vignettes; Theory Practice Relationship; Evaluation Criteria; Standards; Diversity; Ethics; Learning Trajectories; Educational History; Gender Differences; Foreign Countries; Race; Social Class; Intersectionality; Bias; Germany + Suchen Sie Ihr Suchwort? Physik; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Richtlinien; Theorie-Praxis-Beziehung; Standard; Ethik; History of education; Bildungsgeschichte; Geschlechterkonflikt; Ausland; Rasse; Abstammung; Social classes; Soziale Klasse; Deutschland
Abstract	Learning Analytics are an academic field with promising usage scenarios for many educational domains. At the same time, learning analytics come with threats such as the amplification of historically grown inequalities. A range of general guidelines for more equity-focused learning analytics have been proposed but fail to provide sufficiently clear guidance for practitioners. With this paper, we attempt to address this theory-practice gap through domain-specific (physics education) refinement of the general guidelines We propose a process as a starting point for this domain-specific refinement that can be applied to other domains as well. Our point of departure is a domain-specific analysis of historically grown inequalities in order to identify the most relevant diversity categories and evaluation criteria. Through two focal points for normative decision-making, namely equity and bias, we analyze two edge cases and highlight where domain-specific refinement of general guidance is necessary. Our synthesis reveals a necessity to work towards domain-specific standards and regulations for bias analyses and to develop counter-measures against (intersectional) discrimination. Ultimately, this should lead to a stronger equity-focused practice in future. (As Provided).
Anmerkungen	Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01