From Coffee to Complexity: Exploring Model Thinking in the Classroom

What can a cup of coffee teach us about teaching & learning?

That question was at the heart of a recent workshop Storymodelers co-led at the University of Agder library in Kristiansand, Norway as part of the Modeling for Histories project. Participants came from a range of backgrounds—including education, ethnography, information technology, media studies, history, and modeling—to explore a simple idea: can model thinking help students engage more deeply with complex topics?

We began with a relatable question:

What has to happen for a cup of coffee to appear in your kitchen tomorrow morning?

Instructions for the group on how to begin the coffee exercise.

The starting place for this exercise are straightforward: coffee, water, electricity, a cup, and a coffee machine. From there, participants worked in small groups using sticky notes and string to build physical, tactile models, and the coffee quickly became something much larger.

Thinking about coffee expanded to thinking about stores, transportation networks, farms, and labor. Water led to infrastructure, governance, and environmental management. Coffee machines led to factories, mining, manufacturing, and technology. Before long, discussions had expanded into climate change, migration, colonialism, labor exploitation, education systems, and globalization.

One of the first observations from the room was also one of the most important:

Where do we stop?

Every component seemed connected to something else. Every answer generated another question. Participants quickly discovered what modelers regularly confront: complexity has no obvious endpoint. That realization became a lesson in itself as participants grappled with how they might employ similar techniques in their own teaching and learning environments. But this massive brain-dump exercise of the complex systems around a cup of coffee are really just the "appetizer" to modeling.

Models are not attempts to capture everything. They are attempts to make sense of something. To do that, we must make choices about boundaries, scope, and level of detail. What belongs in the model? What can be simplified? What can be left out?

In other words, modeling is not just about representation—it is about making decisions. It is at this point that we need to abstract from the massive reference model some conceptualization that centralizes the elements remaining in the model around a more specific research question (a topic for a future workshop!).

As the activity continued, participants added new layers to their models. They identified actors (colored tags), drew connections between them (physically with string), and discussed assumptions embedded in their representations. We thought about actors not only as people, but also as collections of people/entities, and institutions. We spent time discussing and labeling our emerging model with actors and connecting them (adding labels to denote the connections, like "labor" { works at } "factories"; and "educational institutions" { teach } "labor" (to stand in for workforce broadly)).

This messy, strung-together brain-dump of ideas on the table is what we call a "reference model." This first step in the direction of modeling is intended to get all the knowledge from your head into an artifact before we abstract into the "conceptual model." To move to the next step, we need to consider available data.

What evidence would help us understand the system better?

Rather than debating the definition of "data," we asked participants to brainstorm the kinds of "evidence" that would help them understand their section of this coffee (reference) model. The answers ranged from production statistics and transportation flows to interviews, historical records, cultural practices, government policies, and consumer behavior.

The conversation revealed something important about modeling: everything can be data, not necessarily just quantitative/numbers-based data. We all—across all of our disciplines—grapple with what data is available, how to translate that data into meaning, and strategically determine where to expend resources to collect new data. But the modeling activity also made some participants acutely aware of how assumptions about processes—things we maybe know intuitively but don't have the expertise to know as data or as theory (like exactly how a coffee farm works)—are made visible through reflection of the mental reference model laid out before you during the exercise.

Perhaps the most surprising discussion emerged when we introduced a single new variable: time.

How would our coffee model change if we explicitly considered time?

The conversation quickly moved beyond supply chains and infrastructure. Participants discussed historical pathways, future scenarios, changing meanings, and different temporal scales. Some processes unfold over days. Others take decades. Some are tied to personal experiences, while others are shaped by historical events and long-term social change.

What started as a model of coffee became a discussion about how we understand change itself. For a workshop focused on teaching and learning, this may have been the most valuable insight of all. One participant reflected that modeling helps answer the question students often ask:

Why do I need to know this?

By making relationships visible, modeling helps connect ideas that are often taught separately. It encourages learners to move between individual examples and larger systems, between specific details and broader patterns. Rather than memorizing isolated facts, students can begin to see how pieces fit together.

For one of the participant, the addition of time to our model offered a narrowing effect on the modeling effort, focusing what should be included or not. Another participant, however, felt that his historian training led to a massive widening and ambiguity of modeling when we introduced time.

This led to a discussion about the importance of the modeling question and purpose when it comes to abstracting a "reference model" into a "conceptual model." Question and purpose become necessary scoping tools to filter out some of the fuller, widely complex model.

We did not leave the workshop with a definitive model of coffee. Instead, having used this tactile approach to grow a simple relatable cup of coffee into a widely complex model, we walked through emerging ideas about how teachers and learners might utilize this or similar modeling exercises to practice expansive thinking, and also practice the discipline of abstracting that larger model into something targeted and useful for their studies or research.

The workshop did not aim to turn participants into modelers. Instead, it offered a glimpse of a modeling mindset—one that asks questions, surfaces assumptions, examines evidence, and explores connections.

A simple cup of coffee turned out to be a surprisingly powerful place to start.

We would like to thank R. V. "Gus" Gusentine for sharing his thoughtfully developed coffee complex systems exercise and encouraging us to adapt it to our needs.