This project examines how specific design features of a Python-based intervention shape students’ learning processes and outcomes in systems analysis. By combining different data, this project investigates whether students achieve desired learning outcomes, and which features of the new course design facilitate or hinder student learning in modelling education.

Project description

This project is carried out in three courses in the Global Sustainability Science bachelor program, which incorporate systems analysis modeling assignments (assignments on how systems may change over time). From 2025 on, the courses will transition from the visual program STELLA to the text-based programming language Python as a tool for learning systems analysis and environmental modelling. This transition may challenge students’ conceptual understanding. To compensate for reduced visualization in Python, pen-and-paper drawing exercises are introduced.

Background information

The intervention in this project consists of two elements: 1. replacing STELLA with Python and 2. integrating pen-and-paper drawing tasks to support conceptual understanding. The expected outcomes of the courses and interventions form a clear learning line across the three courses with respect to developing quantitative systems analysis skills: 1. describing real-world systems in simple models, 2. identifying mechanisms that influence system dynamics, and 3. interpreting feedback loops over time. The identified learning mechanisms that may influence whether the desired learning outcomes will be achieved are inspired by learning by drawing, cognitive load theory, self-efficacy theory, and theories of engagement, motivation and self-regulation.

Aims

This project is guided by the following research questions:

• To what extent do students achieve the intended systems-analysis learning outcomes when using Python?
• Which design features of the new learning design facilitate or hinder students’ conceptual understanding of system dynamics and why (considering the role of motivation, self-efficacy, programming anxiety, cognitive load)?

Research method

To answer the first research question, each of the three learning objectives will be tested with exams and written reports. Four complementary methods will be integrated in the course activities to investigate what roles motivation, self-efficacy, cognitive load and feedback play in achieving the learning outcomes. These methods include short surveys at the end of the course, reflection prompts integrated into regular assignments, observation notes written by teaching assistants and student focus groups.

References

• Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
• Chang, L. C., Lin, H. R., & Lin, J. W. (2024). Learning motivation, outcomes, and anxiety in programming courses—A computational thinking–centered method. Education and Information Technologies, 29(1), 545-569.
• Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual review of psychology, 53(1), 109-132.
• Fiorella, L., & Kuhlmann, S. (2020). Creating drawings enhances learning by teaching. Journal of Educational Psychology, 112(4), 811.
• Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into practice, 41(2), 64-70.