DiKuBi-Meta (TP2): Digitalisation in cultural education – a meta-project

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Principal Investigators: Stephan Kröner

Staff: Alexander Christ, Marcus Penthin, Kathrin Smolarczyk, Lisa Birnbaum
Period: 2017-2022
Funding: Federal Ministry of Education and Research

In this project, we focus on summarizing and mapping international quantitative research on digital phenomena in arts, aesthetic and cultural education (D-ACE). As our contribution to the meta-project on digitization in arts education funded by the German Federal Ministry of Education and Research, we aim at providing an overview of published international research on D-ACE and identifying hot spots as well as desiderata in current research on D-ACE. However, conducting research syntheses on D-ACE is complicated by the heterogeneity of the field. The latter is due to the fragmentation of international research on D-ACE resulting from (a) the diversity of cultural activities involved, including visual and performing arts, literature, music, and video games, (b) the large number of relevant digital phenomena, ranging from new hardware over software and algorithms to new forms of interaction, (c) the wide range of educational processes, outcomes, and domains, and (d) the variety of applied research methods. This fragmentation is further aggravated by the large number of disciplines involved in the study of phenomena relevant to D-ACE, each using different terminologies and research paradigms. This complication is overcome in DiKuBi-Meta with both qualitative-narrative research syntheses and scoping reviews based on methods from the fields of text mining and Big Data that enable the screening and categorization of large corpora.

Publications:

Kröner, S., Christ, A. & Penthin, M. (2021) Stichwort: Digitalisierung in der kulturell-ästhetischen Bildung – eine konfigurierende ForschungssyntheseZeitschrift für Erziehungswissenschaft, 24, 9-39.

Christ, A., Penthin, M., & Kröner, S. (2021). Big Data and Digital Aesthetic, Arts and Cultural Education: Hot Spots of Current Quantitative Research. Social Science Computer Review, 39, 821-843. https://doi.org/10.1177/0894439319888455