Thinking in Terms of Change over Time: Opportunities and Challenges of Using System Dynamics Models

Area of Science:

• Science education
• Systems thinking
• Computational modeling

Background:

• Systems thinking (ST) is crucial for addressing complex societal issues and is recommended for integration across science disciplines.
• Engaging students in ST, particularly with concepts like change over time and feedback, presents significant educational challenges.
• Computational system models and system dynamics offer potential solutions to enhance student understanding of complex phenomena.

Purpose of the Study:

• To investigate how 10th-grade students engage with systems thinking (ST) through computational system modeling.
• To analyze students' ability to explain complex phenomena, focusing on change over time and causal relationships.
• To identify challenges students face when evaluating and revising models, including the use of real-world data.

Main Methods:

• An empirical study was conducted with 10th-grade students in a project-based learning unit on chemical kinetics.
• Students utilized computational system modeling within a system dynamics framework.
• Student models and explanations were analyzed to assess their understanding of ST concepts.

Main Results:

• Students demonstrated an enhanced ability to explain phenomena in terms of change over time, moving beyond linear causality.
• Student models and explanations were limited, notably excluding feedback mechanisms.
• Students encountered epistemological barriers when attempting to revise models using real-world data.

Conclusions:

• Computational system modeling, particularly with a system dynamics approach, offers opportunities for teaching complex phenomena.
• Supporting students in understanding feedback mechanisms remains a key challenge in ST education.
• Addressing epistemological barriers is crucial for effective model-based learning and revision using empirical data.