Data Science
Research Syntheses and Data Science/Big Data
We use research methods related to data science, such as text mining, predictive modeling, and topic modeling to identify and analyse literature from broad research areas that can otherwise only be considered incompletely in the context of mapping reviews. Thereby, we reliably identify even those publications that only implicitly refer to the research area under investigation. An example for our use of data science methods is the project on digitization in cultural education (DiKuBi). In forthcoming projects, we are going to use these methods for the analysis of data collected by means of qualitative survey methods as well as for learning analytics in the context of teaching/learning and educational software – with a special focus on user privacy.
| Selected Publications |
Christ, A., Smolarczyk, K., & Kröner, S. (2023). Schwerpunktthemen der quantitativ-empirischen Forschung mit Bezug zur Digitalisierung in der kulturellen Bildung: Eine kartierende Forschungssynthese [Hot topics of quantitative-empirical research related to digitalization in cultural education: a mapping review]. Zeitschrift für Erziehungswissenschaft, Online First. https://doi.org/10.1007/s11618-023-01210-7 (Open Access)
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(5), 821-843. https://doi.org/10.1177%2F0894439319888455 (open access)
| Third-Party Funded Projects |
MetaIntBil: Meta-Project Migration, Integration, and Participation in Education
KuBi-Meta: Aesthetic and Cultural Education in a Changing Society – A Meta-Project
DiKuBi-Meta: Digitalisation in cultural education – a meta-project
