From Prediction to Agile Interventions in the Social Sciences
FAIR Learning Analytics Workshop at the IFS
IFS contributes to the FAIR Graduate Programme for Statistical Methods
UA Ruhr-funded Civic Education Research Lab starts in October
DoDaS Guest: Arne Bathke
FAIR Workshop, September 25-27, 2024
DoDaS & FAIR Guest: Chris Schwiegelshohn
Successful workshop on "Digital Trace Data in Social Science Research"
FAIR Graduate Program
Special talk on Non-parametric Methods, April 24, 2024
FAIR Workshop, June 26-28, 2024
DoDaS & FAIR Guest: Jeff M. Phillips
Mentoring award for Prof. Dr. Sarah Weigelt
Self-regulated learning to improve teaching in computer science
Cooperation project Klett "Meister Cody Kids"
FAIR workshop on "Using Resampling and Simulation to Tackle Heterogeneity in Social Science Research"
FAIR Workshop, October 11-12, 2023
FAIR Workshop, September 22 and 25, 2023
Assistant professorship for Luise von Keyserlingk
FAIR Graduate Program
FAIR Workshop, November 22-23, 2022
Agile Working
Welcome!
The interdisciplinary research area From Prediction to Agile Interventions in the Social Sciences (FAIR) brings together researchers from the data sciences, statistics, education sciences, psychology, rehabilitation studies, and sociology. FAIR-researchers from these different disciplines will focus on the development and application of innovative research methods from the data sciences and use them to address societal challenges in highly relevant areas such as education, health, and societal inclusiveness and participation. Increasingly larger and more complex data have become available in the social sciences that can contribute to more precise prediction models (e.g., for such outcomes as academic success, health, and well-being) and aid our understanding of cause-effect relationships. An important objective is also the development of a framework for “Agile Intervention Research” that allows for individualized, data-driven, and need-based adaptations of interventions in authentic contexts. The FAIR-researchers will use “big” (large data sets) and “small” (small case numbers) data to optimize prognostic models in the social sciences and maximize the impact of available interventions in these fields by allowing for individualized adaptations.
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Funded by the Ministry of Culture and Science of the State of Northrhine Westphalia.