The team of Educational Data Science pursues the central goal of using complex data in the field of education for educational science questions by means of appropriate statistical methods. In doing so, we draw on different types of data (e.g., questionnaire and achievement data, intensive longitudinal surveys, texts, behavioral traces) and statistical methods.
In terms of content, the team's research aims both to explain successful educational processes and to find ways to promote educational processes. The research is divided into three intertwined research strands:
- Development of learning attitudes and their effects on educational and career trajectories:
In this research strand, we address, among other things, heterogeneous educational and career trajectories as a function of gender and family background and whether such heterogeneous trajectories can be explained by differences in learning attitudes.
- Development and evaluation of interventions to promote learning attitudes and educational outcomes:
Central studies in this area focus on the promotion of motivation through targeted interventions in the educational context. In addition, we are currently conducting the learning theory evaluation of the introduction of an accompanying virtual ninth semester at universities in North Rhine-Westphalia (beVinuS.NRW).
- Quality teaching and its correlations with characteristics of teachers and learners:
Here we focus on the questions by which teaching behavior the motivation and learning success of learners can be promoted and which affective-motivational aspects of the professional competencies of teachers are related to such teaching.