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From Prediction to Agile Interventions in the Social Sciences
FAIR Learning Analytics Workshop at the IFS
Workshop on ‘Learning Analytics’ took place at the IFS with participants from all over Germany.
IFS contributes to the FAIR Graduate Programme for Statistical Methods
The FAIR Graduate Programme offers doctoral students in the social sciences workshops on statistical methods.
UA Ruhr-funded Civic Education Research Lab starts in October
Elisabeth Graf, Pascal Alscher und Daniel Deimel are pursuing the goal of conducting research on successful civic education with CERL.
DoDaS Guest: Arne Bathke
Prof. Dr. Arne Bathke (Paris Lodron Universität Salzburg)
Inference methods for multivariate data
FAIR Workshop, September 25-27, 2024
The interdisciplinary research area FAIR organizes a three-day workshop on Learning Analytics.
DoDaS & FAIR Guest: Chris Schwiegelshohn
Prof. Dr. Chris Schwiegelshohn (Aarhus University)
Deterministic Clustering in High Dimensional Spaces: Sketches and Approximation
Successful workshop on "Digital Trace Data in Social Science Research"
From June 26 to 28, 2024, the workshop "Digital Trace Data in Social Science Research," organized by the research area FAIR, took place.
FAIR Graduate Program
The interdisciplinary research area FAIR will offer a graduate program in statistical methods starting in the summer semester 2024.
Special talk on Non-parametric Methods, April 24, 2024
Special talk by Prof. Dr. Edgar Brunner "Missbrauch, Gefahren und Fehlinterpretationen nichtparametrischer Verfahren"
FAIR Workshop, June 26-28, 2024
The interdisciplinary research area FAIR organizes a three-day workshop on Digital Trace Data in Social Science Research.
DoDaS & FAIR Guest: Jeff M. Phillips
Prof. Jeff M. Phillips, Ph.D. (University of Utah)
Talk title: "Ferret: Reviewing Tabular Datasets for Manipulation"
Mentoring award for Prof. Dr. Sarah Weigelt
Prof. Dr. Sarah Weigelt receives the Mentoring Award of the Developmental Psychology Section of the German Psychological Society
Self-regulated learning to improve teaching in computer science
New FAIR project in collaboration with Thomas Zeuma from RUB investigates formal computer science courses to improve their quality
Cooperation project Klett "Meister Cody Kids"
The educational game "Meister Cody Kids" is developed as part of a project funded by the Federal Ministry for Economic Affairs and Climate Action
FAIR workshop on "Using Resampling and Simulation to Tackle Heterogeneity in Social Science Research"
FAIR workshop on "Using Resampling and Simulation to Tackle Heterogeneity in Social Science Research" took place on 22 and 25 September 2023.
FAIR Workshop, October 11-12, 2023
The interdisciplinary research area FAIR organizes a two-day workshop on Prediction Modeling.
FAIR Workshop, September 22 and 25, 2023
The interdisciplinary research area FAIR organizes a two-day workshop on Using Resampling and Simulation.
Assistant professorship for Luise von Keyserlingk
Luise von Keyserlingk is Assistant Professor for Learning and Instruction at the University of Tübingen, Hector Research Institute of Education…
FAIR Graduate Program
The interdisciplinary research area FAIR will offer a graduate program in statistical methods starting in the summer semester 2023.
FAIR Workshop, November 22-23, 2022
The interdisciplinary research area FAIR organizes a two-day workshop on Sequence and Streaming Data Analysis.
Agile Working
On August 18th and 19th, the FAIR team attended a training on agile project management by the ibo Academy.
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.
