Systematic reviews are an essential tool to obtain an overview of interdisciplinary research fields. However, they are often hampered by broad search strings leading to many false negative results. Such literature searches can lead to several 100,000 papers and manual screening and categorization are no longer feasible. In such cases, text mining can support screening and categorizing of large literature corpora. In our study, we used a corpus of n > 250,000 papers from a literature search on international research on digitalisation in arts, aesthetic and cultural education (D-ACE). After cleaning the exported texts (title, journal, keywords, abstracts), we applied an iterative procedure of predictive modeling and prioritized screening (as described in detail by Christ et al., 2021), followed by topic modelling. Predictive modelling was utilized to avoid having to screen all texts. Finally, topic modeling of the included papers was performed to determine content and size of hot topics of research on D-ACE. Across iterations, the inclusion rate decreased from 85.8% to 1.8% in the 17th iteration. In total, n = 3,846 papers (including n = 1786 from the training set) were included. Most of them were from authors from the USA, followed by authors from the UK, Taiwan, Australia, Spain, Canada and Germany. The publication rate has increased at an accelerated rate since 2012, which did not differ for the affiliation country or continent of the authors. The included papers were first analyzed via topic modelling to determine the latent research topics within the corpus, followed by a more specific analysis of investigated cultural activities. In total, k = 31 latent topics were covered in the included papers. Hot topics included “formal education and learning”, “serious games and gamification of learning”, “popular games and engagement”, “user experience and interface design” and “effects on personality and behavior”. Focusing on the investigated cultural activities resulted in k = 17 topics containing all major facets of cultural activities i.e. music, literature, visual arts, performing arts and games. With k = 8, a majority of topics related to video games. The distribution of articles in the topic models did not differ according to the affiliation countries or continents. Overall, predictive modelling and priority screening turned out to be well suited for efficiently identifying hot topics of international research during preparation of systematic reviews. Implications for further development of these methods as well as for conducting systematic reviews and original work are discussed.

Christ, A., Penthin, M., & Kröner, S. (in revision). Two decades of research syntheses on digital cultural education: A tertiary review. 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%2F0894439319888455 Kröner, S., Christ, A., & Penthin, M. (2021). Stichwort: Digitalisierung in der kulturell-ästhetischen Bildung–eine konfigurierende Forschungssynthese [Digitalization in aesthetics, arts and cultural education – a scoping review]. Zeitschrift für Erziehungswissenschaft, 24, 9-39. https://doi.org/10.1007/s11618-021-00989-7