Systems Thinking – a key concept in Geography Education

Authors: Armin Rempfler*, University of Teacher Education Lucerne
Topics: Geography Education
Keywords: System competence modelling, Socio-ecological approach, Tipping points, Qualitative system-specific characteristics
Session Type: Virtual Paper
Day: 4/8/2021
Start / End Time: 1:30 PM / 2:45 PM
Room: Virtual 28
Presentation File: No File Uploaded


The great challenges of the 21st century (e.g. climate change, global migration, ecological exploitation) are geographically relevant highly complex topics. Geography has the potential to make learners aware of this complexity and to help them understand how to deal with it adequately. To achieve this, instructional problems not only require a didactic reduction of facts, but also an increase in the self-complexity of the students. This means that students need to be instructed to make more cognitive decisions per action, which implies running through causes and their causes, or effects and effects of the effects, and so on, in their mind. According to this, high self-complexity is congruent with high competence in systems thinking.
Firstly, I show how we developed a competence model based on a socio-ecological approach. To validate the theoretical model, we developed a measuring instrument and tested it with Item Response Theory methods, so that we can now diagnose the system competence of students in a valid and reliable way.
Secondly, I present a planned follow-up study to address a remaining weakness of the model because it is primarily oriented on quantitative system properties. From a system-theoretical point of view, however, qualitative system characteristics are much more important for a profound system understanding. Thanks to recent studies on ‘tipping points’, we are now able to derive theoretically and empirically founded system-specific characteristics that can be operationalized, enabling us to measure students' ability to recognize system properties such as ‘nonlinear dynamics’ or ‘feedback’ with high selectivity.

Abstract Information

This abstract is already part of a session. View the session here.

To access contact information login