Seeing the Connections: Teaching Students to Think in Complex Systems

How environmental science teachers can move students beyond memorizing facts toward explaining how the natural world works.

A lake experiences a massive fish kill. An invasive species spreads through a forest. Arctic sea ice declines as global temperatures rise. These environmental problems rarely have a single cause. They emerge from interactions among organisms, physical processes, human activities, and environmental conditions.

Understanding these interactions is at the heart of environmental science. Yet students can memorize vocabulary, describe individual processes, and perform well on traditional assessments without fully understanding how those processes influence one another.

Research on complex systems learning offers an important insight for science teachers: students need opportunities to explain relationships, trace consequences, and use evidence to understand how systems behave. This is particularly important in environmental science, where cause-and-effect relationships often involve multiple steps, feedback loops, and delays.

Knowing the Parts Is Not the Same as Understanding the System

Consider eutrophication, a familiar topic in environmental science. Students may be able to define nutrient pollution, algal blooms, decomposition, and dissolved oxygen. But can they explain why fertilizer runoff might lead to a fish kill several days later?

Hmelo-Silver, Marathe, and Liu (2007) compared how experts and novices understood complex biological systems, including an aquarium ecosystem. Their research highlighted differences in how learners understood system structures, functions, and behaviors.

Knowing the parts does not automatically mean understanding how the parts interact.

Similarly, Yoon, Goh, and Park (2018), in a review of research on complex systems learning in K–12 science education, examined how students develop an understanding of relationships and system-level behavior.

For environmental science teachers, the implication is clear: students should not simply learn ecological processes independently. They should use those processes to explain environmental phenomena.

Start With a Phenomenon That Demands an Explanation

Imagine projecting a photograph of a lake covered in green algae, followed by an image of dead fish along the shoreline.

Rather than immediately explaining eutrophication, ask students:

• What do you notice?

  
  

• What might have caused the fish to die?

  
  

• What evidence would help us determine what happened?

  
  

• How could activities occurring miles away affect this lake?

  
  

Students might suggest that the algae poisoned the fish, that the water became too warm, or that pollution entered the lake. These initial ideas provide a starting point for investigation.

This approach aligns with the National Research Council's A Framework for K–12 Science Education (2012), which emphasizes developing and using models, analyzing data, and constructing explanations. The framework also identifies systems and system models as an important crosscutting concept.

The goal is not simply to make lessons more interesting. It is to give students a problem that requires them to use scientific knowledge.

Make Invisible Relationships Visible With Models

Many important environmental processes cannot be observed directly. Students cannot easily watch nutrients move from agricultural fields into a lake or see how dissolved oxygen declines throughout an ecosystem.

Models help make these relationships visible.

After introducing the fish-kill phenomenon, students might construct a causal model:

Fertilizer runoff increases → nutrient availability rises → algal growth increases → algae die and decompose → decomposers consume dissolved oxygen → oxygen levels fall → fish may die.

This model provides a starting explanation, not a finished answer. Students can investigate whether temperature, water circulation, lake depth, or other factors influence the outcome.

Research by Yoon and colleagues has explored ways to support complex systems learning through computer-supported models and carefully designed learning environments. Their work, including research on teacher professional development (Yoon et al., 2017), underscores that using a model is not automatically the same as learning from one. Students need opportunities to interpret relationships, test predictions, and revise their thinking.

A useful classroom model should therefore do more than illustrate a process. It should help students explain evidence and predict outcomes.

Inside an AP Environmental Science Classroom: Solving a Fish Kill

Imagine an AP Environmental Science class investigating a fish kill in a lake near an agricultural community. Students receive a short news report, photographs of the lake, and a map showing nearby farms and streams flowing into the water.

The teacher poses a question: What caused the fish to die, and what evidence would convince you that your explanation is correct?

The initial explanation: Pollution killed the fish.

Several students blame fertilizer runoff. Others suspect pesticides or unusually warm water. Most draw a simple arrow from fertilizer to dead fish. Some argue that the algae themselves must be poisonous.

The teacher asks them to explain the mechanism connecting their suspected cause to the fish deaths. The students realize that identifying a possible pollutant is not the same as explaining how it causes harm.

The first evidence: An algal bloom develops.

Students examine a simulated data set showing rainfall, nutrient concentrations, algal abundance, water temperature, and dissolved oxygen over several weeks. Heavy rainfall follows fertilizer application. Nutrient concentrations rise, followed by a substantial algal bloom.

Students conclude that fertilizer caused the bloom and the bloom killed the fish. But the teacher asks, “What specifically made the lake unable to support the fish?”

The unexpected evidence: Oxygen levels collapse.

Students examine additional measurements showing that dissolved oxygen declined sharply as the bloom died back. The lowest oxygen levels occurred shortly before the fish kill.

The class recalls that decomposers break down dead organic matter, consuming dissolved oxygen during aerobic decomposition. Students also recognize that warmer water holds less dissolved oxygen than cooler water.

They revise their model. Fertilizer runoff increased nutrient availability, promoting algal growth. As algae died, decomposers broke down the organic material and consumed oxygen. Oxygen levels fell, contributing to the fish deaths. High temperatures may have intensified the problem.

The deeper question: Why didn't the lake recover immediately?

The teacher reveals that nutrient concentrations began declining after fertilizer inputs decreased, but dissolved oxygen remained low for several days.

Some students assume the data must be wrong: removing the pollution should immediately restore the lake. Others suggest that organic material is still decomposing or that warm temperatures continue to limit oxygen availability.

Students begin to recognize that environmental systems do not necessarily respond immediately when one component changes. The consequences of pollution can persist even after the original source is reduced.

The final explanation: A connected system.

Students write an evidence-based explanation:

“Fertilizer runoff likely increased nutrient availability in the lake, contributing to an algal bloom. As algae died, decomposers broke down the organic material and consumed dissolved oxygen. The decline in oxygen before the fish kill supports the explanation that low oxygen contributed to the deaths. Warm water may have intensified the problem because it holds less dissolved oxygen. The continued low oxygen levels after nutrient inputs declined suggest that the effects of pollution can persist while organic material continues to decompose.”

This explanation is much stronger than the original claim that fertilizer simply killed the fish. Students connect multiple processes, support their reasoning with data, and account for delayed effects.

The teacher can extend the investigation by asking students to evaluate possible solutions. Would vegetated buffer strips reduce nutrient runoff? Would reducing fertilizer applications address the original cause? Could aeration temporarily improve oxygen levels? Which intervention addresses the source, and which addresses a consequence?

Students must now consider how different solutions affect different parts of the system.

The instructional payoff: In one investigation, students practice analyzing data, developing and revising models, constructing explanations, and evaluating solutions. They learn to use environmental science concepts to explain an event rather than merely memorize the definition of eutrophication.

Note: The classroom scenario and data described here are illustrative. A real fish kill may have multiple contributing causes, and conclusions should depend on the available evidence.

Help Students Recognize Feedback, Delays, and Unexpected Outcomes

Environmental systems often behave in ways that challenge simple cause-and-effect reasoning.

Consider Arctic sea ice. As ice melts, darker ocean water becomes exposed. The ocean absorbs more solar energy than reflective ice does, contributing to additional warming and further ice loss. This is a positive feedback loop: an initial change reinforces itself.

Other examples include population growth affected by resource availability, soil erosion that reduces soil quality, and groundwater pumping that alters stream flows.

Jacobson and Wilensky (2006) discuss the scientific and educational importance of complex systems, along with the challenges of helping learners understand them.

Teachers can encourage deeper reasoning by asking:

• What happens after the first effect?

  
  

• Does the change reinforce itself or counteract itself?

  
  

• Is there a delay between cause and consequence?

  
  

• Could the same intervention produce different outcomes under different conditions?

  
  

These questions help students move beyond identifying variables toward understanding how a system behaves over time.

Connect Systems Thinking to AP Environmental Science

Systems thinking can be integrated into existing AP Environmental Science content without requiring a separate unit.

• Population ecology: Explain how resource availability, predation, and competition influence population growth.

  
  

• Land and water use: Trace how development changes runoff, infiltration, groundwater recharge, and stream conditions.

  
  

• Energy resources: Consider how energy choices affect extraction, land disturbance, emissions, waste, and ecosystems.

  
  

• Global change: Connect greenhouse gas emissions to warming, ice loss, sea-level rise, and ecosystem changes.

  
  

• Agriculture and soil: Examine how farming practices influence erosion, nutrient cycling, water quality, and long-term productivity.

York and Orgill (2019), in their review of systems thinking in STEM education, discuss the value of helping students understand relationships and system-level behavior across scientific disciplines.

This approach can also strengthen students' written explanations. A strong response does more than name two related concepts. Students should explain the mechanism connecting them and support their reasoning with evidence when appropriate.

A Simple Routine for Developing Systems Thinkers

Teachers can incorporate systems thinking into almost any lesson with four steps:

1. Observe. Present a photograph, graph, data set, or environmental event. Ask students what they notice and wonder.

2. Explain. Have students propose possible causes and identify the scientific concepts that might be relevant.

3. Model. Ask students to construct a causal diagram showing how the components interact.

4. Test and revise. Introduce new evidence or change one variable. Have students revise their models and explain why their thinking changed.

This routine is a practical synthesis of science education principles, not a single protocol claimed to have been experimentally validated in this exact form.

Assess student understanding by looking for meaningful causal connections, explanations of indirect or delayed consequences, and appropriate use of evidence.

Conclusion: From Memorizing Concepts to Explaining the World

Environmental science offers a powerful setting for teaching systems thinking because its central problems cross boundaries. Water quality connects to land use. Population growth connects to resource availability. Energy choices affect ecosystems, climate, and human health.

Helping students understand these relationships requires more than presenting increasingly complex information. It requires opportunities to investigate phenomena, construct models, test explanations, and consider how changes propagate through a system.

The research of Susan Yoon and other scholars in complex systems education provides a useful foundation for this work. It encourages teachers to think carefully about how students develop an understanding of relationships, interactions, and system behavior.

For environmental science teachers, the practical goal is straightforward: ask students not only what happens, but why it happens, what happens next, and how they know.

When students begin to see the connections between the parts, they move beyond memorizing environmental science toward understanding how environmental systems work.

References

Hmelo-Silver, C. E., Marathe, S., & Liu, L. (2007). Fish swim, rocks sit, and lungs breathe: Expert-novice understanding of complex systems. The Journal of the Learning Sciences, 16(3), 307–331. https://doi.org/10.1080/10508400701413401

Jacobson, M. J., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. The Journal of the Learning Sciences, 15(1), 11–34. https://doi.org/10.1207/s15327809jls1501_4

National Research Council. (2012). A framework for K–12 science education: Practices, crosscutting concepts, and core ideas. The National Academies Press. https://doi.org/10.17226/13165

Yoon, S. A. (2021). Complex systems research in K12 science education: A focus on what works for whom and under which conditions. Systems, 9(2), Article 29. https://doi.org/10.3390/systems9020029

Yoon, S. A., Anderson, E., Koehler-Yom, J., Evans, C., Park, M., Sheldon, J., Schoenfeld, I., Wendel, D., Scheintaub, H., & Klopfer, E. (2017). Teaching about complex systems is no simple matter: Building effective professional development for computer-supported complex systems instruction. Instructional Science, 45(1), 99–121. https://doi.org/10.1007/s11251-016-9388-7

Yoon, S. A., Goh, S.-E., & Park, M. (2018). Teaching and learning about complex systems in K–12 science education: A review of empirical studies 1995–2015. Review of Educational Research, 88(2), 285–325. https://doi.org/10.3102/0034654317746090

Yoon, S. A., Chinn, C. A., Noushad, N., Richman, T., Hussain-Abidi, H., Hunkar, K., Cottone, A., Katz, J., Mitkus, E., & Wendel, D. (2023). Seven design principles for teaching complex socioscientific issues: The design of a complex systems agent-based disease epidemic model and the application of epistemic practices in high school biology. Frontiers in Education, 8, Article 1210153. https://doi.org/10.3389/feduc.2023.1210153

York, S., & Orgill, M. (2019). Applications of systems thinking in STEM education. Journal of Chemical Education, 96(12), 2742–2751. https://doi.org/10.1021/acs.jchemed.9b00261