An ability to engage in system thinking is necessary to understand complex problems. While many pre-college students use system modeling tools, there is limited evidence of student reasoning about causal relationships that interact in diverging and converging chains, and how these affect system behavior. A chemistry unit on gas phenomena was implemented in two successive years with 73 high school students. Although the phenomena could be explained with simple linear causal reasoning, many student models included surprising and problematic causal chains and non-linear patterns.
Research
CREATE for STEM Institute teams conduct research that focuses on impactful projects in undergraduate education through Discipline-Based Educational Research (DBER). We design innovative K-16 science curricula and investigate the effects of new teaching methods on student learning, engagement, and community impact, with our work increasingly incorporating artificial intelligence to enhance support for teachers and learners. We are leaders in STEM assessment design and professional development for educators, collaborating with international partners in over a dozen countries. CREATE fosters new talent, provides seed money for initial work and supports the grant writing process. Our goal is for CREATE to be a hub for the exchange of information and ideas!
Publications
Students Do Not Always Mean What We Think They Mean: A Questioning Strategy to Elicit the Reasoning Behind Unexpected Causal Patterns in Student System Models
Student Conceptions, Conceptual Change, and Learning Progressions
Attention to improving science education has grown nationally and globally, as science and policy communities fnd themselves challenged by complex real-world problems that have neither straight-forward nor ultimate solutions (Anderson & Li, 2020). Science, technology, engineering, and math-ematics (STEM) permeate nearly every facet of modern life; indeed, STEM holds the key to meeting many of the problems facing humans today and in the future (OECD, 2019). Moreover, understand-ing science can fundamentally improve people’s lives (National Research Council, 2012b; OECD, 2019).
The effect of using different computational system modeling approaches on applying systems thinking
This paper discusses the potential of two computational modeling approaches in moving students from simple linear causal reasoning to applying more complex aspects of systems thinking (ST) in explanations of scientific phenomena. While linear causal reasoning can help students understand some natural phenomena, it may not be sufficient for understanding more complex issues such as global warming and pandemics, which involve feedback, cyclic patterns, and equilibrium. In contrast, ST has shown promise as an approach for making sense of complex problems.
Using Visualization and Laboratory to Promote Learning in Science
As technology advances, opportunities and challenges of its uses arise. The educational field is no different, and along with the need for educators to adapt to technological changes that occur spontaneously (widespread use of smartphones, for example), the opportunity of utilizing various digital platforms for educational purposes that were not possible or at least not cost-effective until now exist. The authors in this section illustrate these opportunities and challenges.
Thinking in Terms of Change over Time: Opportunities and Challenges of Using System Dynamics Models
Understanding the world around us is a growing necessity for the whole public, as citizens are required to make informed decisions in their everyday lives about complex issues. Systems thinking (ST) is a promising approach for developing solutions to various problems that society faces and has been acknowledged as a crosscutting concept that should be integrated across educational science disciplines. However, studies show that engaging students in ST is challenging, especially concerning aspects like change over time and feedback.
