From Prediction to Agile Interventions in the Social Sciences
FAIR Workshop, April 1-2, 2025

Opening of the Interdisciplinary Center Agile PAIR

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
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FAIR Workshop, September 22 and 25, 2023
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Assistant professorship for Luise von Keyserlingk

FAIR Graduate Program

FAIR Workshop, November 22-23, 2022
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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.
