Identifying High Impact School Improvements Using Conditional Mean Independent Correlations and Growth Functions

Autor/inn/en	van Halem, Nicolette; Cornelisz, Ilja; Daly, Alan; van Klaveren, Chris
Titel	Identifying High Impact School Improvements Using Conditional Mean Independent Correlations and Growth Functions
Quelle	In: International Journal of Research & Method in Education, 46 (2023) 2, S.211-228 (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (van Halem, Nicolette) ORCID (Cornelisz, Ilja) ORCID (van Klaveren, Chris)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1743-727X
DOI	10.1080/1743727X.2022.2099826
Schlagwörter	Educational Improvement; Evaluation Methods; Decision Making; Growth Models; Correlation; Public Education; Data Use; Elementary Secondary Education; Theory Practice Relationship; Research and Development; Educational Research; Educational Environment; Sense of Community; School Safety; Student Centered Learning; Relevance (Education); Teacher Student Relationship; Peer Relationship; Technology Uses in Education; California + Suchen Sie Ihr Suchwort? Teaching improvement; Unterrichtsentwicklung; Decision-making; Entscheidungsfindung; Korrelation; Öffentliche Erziehung; Theorie-Praxis-Beziehung; Forschung und Entwicklung; Bildungsforschung; Pädagogische Forschung; Lernumgebung; Pädagogische Umwelt; Schulumwelt; Group work; Student-entered learning; Student-centred learning; Student centred learning; Schülerorientierter Unterricht; Schülerzentrierter Unterricht; Gruppenarbeit; Relevance; Relevanz; Teacher student relationships; Lehrer-Schüler-Beziehung; Peer-Beziehungen; Technology enhanced learning; Technology aided learning; Technologieunterstütztes Lernen; Kalifornien
Abstract	In educational contexts where many domains subject to improvement are interdependent and causal evidence is frequently lacking it is difficult, if not impossible, for policymakers and educational practitioners to decide which domain should be invested in. This paper proposes a new method that uses Conditional Mean Independent Correlations (CMIC) and normative growth functions to inform such decision-making processes. In this paper, CMIC and growth functions are applied to data from a research-practice partnership to identify high impact improvements among domains that are considered important to the district's mission and vision around student learning. The results point to improvement domains that administrators did not consider to be high impact improvements initially, suggesting that this method brings leaders food for thought around strategies for improvement efforts. The CMIC and growth functions moreover accommodate opportunities for policymakers and practitioners to base their decisions on theory and data, providing them with a stronger degree of decision-making authority for use of resources for improvement. Simultaneously, CMIC and growth functions enable researchers to test and further develop theoretical models on improvement efforts. Limitations and suggestions for further research are discussed. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01