Hot Topics of Quantitative-Empirical Research Related to Digitalization in Cultural Education: A Mapping Review
Alexander Christ, Kathrin Smolarczyk and Stephan Kröner published a mapping review on hot topics of quantitative-empirical research related to digitalization in cultural education in Zeitschrift für Erziehungswissenschaft (IF 1.4), which utilizes text mining and big data methods: “Hot topics of quantitative-empirical research related to digitalization in cultural education: a mapping review” (https://doi.org/10.1007/s11618-023-01210-7).
Abstract:
In the context of the digital transformation, cultural activities have also changed. “Classical” activities have been digitally transformed, and new, genuinely digital activities have emerged. Many studies from various disciplines refer to this, which have not yet been adequately processed through research syntheses. Mapping reviews are suitable for providing an overview of the hot topics and desiderata of quantitative-empirical research at the intersection of digitization and cultural participation. In view of the large number of articles to be included, text mining including prioritized screening via predictive modelling is helpful. After screening n = 8500 potentially relevant papers from a database of n > 250,000 papers via predictive modelling, n = 3846 papers were included and mapped via topic modelling according to the investigated facets of cultural activities and overarching research themes. For the facets of cultural activities, a hot topic on video games was identified. For the overarching research themes, a variety of different topics emerged such as formal education in schools or motivation and flow. Regarding the methods chosen, it is discussed that these foci and desiderata across heterogeneous themes pave the way for topic-specific research syntheses as well as for further original research on the desiderata. Implications for the further development of the applied methods are discussed.
Keywords: Cultural Education; Digitalization; Text Mining; Research Synthesis; Topic Modeling
https://link.springer.com/article/10.1007/s11618-023-01210-7