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QUADRIGA Datenkompetenzframework

Authors: Petras, Vivien; Neuroth, Heike; Seltmann, Melanie; Schnaitter, Hannes; Walter, Paul;

QUADRIGA Datenkompetenzframework

Abstract

Datenkompetenzframework des Datenkompetenzzentrums QUADRIGA.Das QUADRIGA Datenkompetenzframework wurde als Teil des Arbeitspaketes 2 (Lernorte) des BMBF-geförderten Projektes QUADRIGA entworfen. Es ist zum einen Grundlage der Fallstudien und der in diesen entworfenen Quadriga Educational Resources sowie des QUADRIGA Navigators, der Anwender*innen ermöglicht, auf sie zugeschnittene Lehr-/Lernangebote zu filtern. Ziel war die Entwicklung eines Frameworks, das am Datenfluss ausgerichtet ist, die für Quadriga nötigen Kompetenzen möglichst umfassend abdeckt und mit existierenden nationalen und internationalen Frameworks vergleichbar bleibt. Darüber hinaus sollte es möglich bleiben, weitere Komponenten zum Framework hinzuzufügen. Version 1 entstand für die Antragstellung des Projekts unter Einbindung einer Analyse der Data Literacy Competencies Matrix von Ridsdale et al. (2015), des Future Skills Framework für Data Literacy von Schüller et al. (2019) und des FAIR4S Frameworks von Whyte et al. (2019). Es beinhaltet die 7 Dimensionen Datenfluss, Datenkompetenzen, Datenfähigkeiten/Wissen, Anwendungsdomänen, Basiswissenschaften, Datentypen sowie Kompetenzniveau.Version 2 entstand aus der projektinternen Weiterentwicklung eines Entwurfs der im Rahmen einer Masterarbeit für den Studiengang Digitales Datenmanagement der Humboldt Universität zu Berlin und der Fachhochschule Potsdam entstanden ist (Walter, 2014). Dieser ist wiederum die Fortführung der ersten Version unter nochmaliger Prüfung der o. g. drei Frameworks und unter Einbeziehung der Data Management Skills Competency Matrix von Sapp Nelson (2017), des Data Literacy Maturity Models von Sternkopf (2019) und des DaLiCo Dimensions Frameworks des DaLiCo Teams (2022). Version 3 ist eine weitere Anpassung hinsichtlich der Prozesse und ersten Ergebnisse im Projekt QUADRIGA und eine Präzisierung und Kürzung der Dimensionen Wissen, Fähigkeiten und Haltung sowie der Beispiele. Dieses Framework besteht aus 15 Kompetenzen, die sich 6 Phasen des Datenflusses zuordnen lassen. Für weitere Informationen siehe:Buchholz, B., Lucke, U., Schimmler, S., Bakels, J.-H., Bernoth, J., Fischer, F., Gieseke, L., Grotkopp, M., Hiltmann, T., Krupka, D., Loist, S., Meisner, J., Mischke, D., Möser, R., Neuroth, H., Petras, V., Schmeling, J., Söring, S., & Trilcke, P. (2024). Umsetzungskonzept QUADRIGA: Berlin-Brandenburgisches Datenkompetenzzentrum für Digital Humanities, Verwaltungswissenschaft, Informatik und Informationswissenschaft. Zenodo. https://doi.org/10.5281/zenodo.10805016.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average