Content area
Abstract
This dissertation applies systems engineering tools such as graph theory and optimization to analyze and design the chemical engineering curriculum and courses to enhance student learning and promote multi-scale systems thinking. These tools, commonly used to optimize processes and supply chains, are adapted to formally represent and improve educational structures.
First, a graph-theoretic framework is developed to model curricula, where nodes represent topics and edges represent conceptual dependencies. Here, courses can be thought of as tightly connected clusters of topics. This abstraction enables visualization, analysis, and reorganization of curricula to improve course connectivity and cohesion. It also supports more structured discussions among faculty. The framework is applied to the chemical engineering curriculum at the University of Wisconsin–Madison to demonstrate its capabilities.
Then, the graph framework is further developed to create a more detailed model of individual courses, incorporating the topic-level model with lectures, assessments, and learning outcomes as interconnected components. This forms a hierarchical structure that supports backward design and helps instructors plan aligned courses, while giving students clear insights into how course elements support their learning. A case study on a process design course illustrates the approach.
Finally, case studies are introduced to promote multi-scale systems thinking throughout the chemical engineering curriculum. These include data, models, and Jupyter Notebook assignments to help students explore real-world challenges such as sustainability and supply chains, so they can better solve complex, interdisciplinary problems that require considerations from the molecular level to phenomena, to units, to processes, to markets, to society, and to the global environment.
