Computational modeling at school
The importance of computational modeling and simulations in education
In science, understanding the world requires thinking with models—simplified representations of reality that help us explain, predict, and explore phenomena. Computational modeling and simulations bring this fundamental scientific practice into education, allowing students not only to observe but to actively engage in the processes of hypothesis formulation, experimentation, data analysis, and argumentation. By integrating these tools into the classroom, educators can foster scientific thinking and inquiry-based learning, equipping students with skills essential for both academia and real-world problem-solving.
One of the greatest challenges in education is helping students grasp abstract concepts. Computational models play a crucial role in this process by offering a structured way to simplify and explore complex ideas. Through modeling, students learn that scientific understanding is not about memorizing isolated facts but about constructing and refining representations of reality.
In one of his short stories, "Funes the Memorious", the famous Argentine writer Jorge Luis Borges (1899 -1986) presents a striking analogy for why abstraction is essential for thought. Funes, endowed with a perfect memory, is incapable of generalizing or abstracting, as he recalls every detail of every moment with absolute precision. His inability to forget prevents him from forming concepts or making connections, leaving him trapped in a world of pure recollection rather than true thinking.
Similarly, without the ability to simplify and model, scientific thinking becomes impossible. Models allow students to filter out unnecessary details, identify patterns, and develop conceptual frameworks, rather than getting lost in an overwhelming amount of data. Abstraction is at the heart of modeling, as it enables us to focus on what truly matters while ignoring irrelevant complexities. Borges himself captured this idea powerfully:
With no effort, he had learned English, French, Portuguese and Latin. I suspect, however, that he was not very capable of thought. To think is to forget differences, generalize, make abstractions.
(Borges, 1998, p. 154)
By engaging with computational models, students move beyond passive learning and start acting like scientists. They develop scientific skills by formulating hypotheses, adjusting variables, and predicting outcomes; conducting experiments to test different scenarios; analyzing data to observe patterns and relationships; and revising their models based on new evidence, reinforcing the iterative nature of scientific inquiry, among other skills.
Moreover, an essential lesson emerges: all models are approximations. No model can perfectly capture reality; instead, it highlights certain aspects while omitting others. Understanding this limitation helps students develop a critical mindset, making them more aware of the assumptions behind scientific theories and real-world data interpretations.
The Power of Simulations
While models allow us to think abstractly, simulations allow us to experiment safely. Many real-world scientific experiments are:
- Too dangerous (e.g., studying nuclear reactions).
- Too expensive (e.g., sending spacecraft to different planets).
- Impossible (e.g., modeling the evolution of an ecosystem over thousands of years).
Simulations provide an alternative by creating controlled, interactive environments where students can explore "what-if" scenarios that would otherwise be unattainable. A well-designed simulation enables learners to manipulate variables, test hypotheses, and see the effects of their choices in real-time, reinforcing causal reasoning and scientific literacy.
Beyond Science: Computational Modeling Across Disciplines
While commonly associated with the natural sciences, computational modeling extends beyond physics, chemistry, and biology. In social sciences, simulations help students explore economic models, population dynamics, and decision-making processes. In humanities, historical simulations can illustrate how small changes in events might have led to entirely different outcomes. Interdisciplinary learning becomes more tangible when students can manipulate and interact with models rather than simply reading about theories.
Integrating computational modeling and simulations into education transforms students from passive receivers of information into active investigators of knowledge. By thinking with models, learners develop essential scientific skills—formulating hypotheses, analyzing data, and refining their understanding—while also recognizing the limitations inherent in any model. Simulations, in turn, allow them to experiment beyond the constraints of reality, testing ideas in ways that would otherwise be too risky, expensive, or impossible.
For educators, these tools provide a bridge between theory and practice, helping students develop critical thinking, problem-solving abilities, and a deeper appreciation for the scientific process. In an era where data and computational reasoning shape our understanding of the world, equipping students with these skills is more essential than ever.
StarLogo NOVA is an accessible and powerful tool that enables teachers and students, even those with no prior programming experience, to use, modify, and create (Lee, 2018) computational simulation models. Designed specifically for educational settings, StarLogo NOVA scaffolds the processes of modeling and simulation, offering a 3D, fun, game-oriented environment that fosters engagement and creativity. Rooted in the principle of providing a “low threshold and high ceiling” (Papert, 1980), it ensures that beginners can easily get started while still allowing for advanced exploration and deeper learning.
The History and Evolution of Computational Modeling
The origins of computational modeling are closely tied to the development of educational programming languages, particularly LOGO, created by Seymour Papert and his colleagues at MIT in the late 1960s (Papert, 1980). LOGO was designed as a tool for learning through exploration, embodying Papert’s vision of "constructionism"—the idea that students learn best when they actively construct knowledge rather than passively receive it. Through the use of turtle graphics, LOGO allowed students to visualize geometric patterns by issuing simple commands, fostering computational thinking long before the term became widely recognized. Papert argued that “computers are instruments whose music is ideas”, emphasizing their potential to revolutionize learning (Papert, 1980, Mindstorms: Children, Computers, and Powerful Ideas).
As computational modeling evolved, the development of domain-specific programming languages for education gained traction. StarLogo, an extension of LOGO developed by Mitchel Resnick in the 1990s, shifted from individual turtle-based commands to agent-based modeling, enabling students to simulate complex systems such as flocking behaviors or predator-prey interactions (Resnick, 1994, Turtles, Termites, and Traffic Jams). This marked a shift in educational programming, where the focus moved from controlling single agents to modeling emergent behaviors in decentralized systems. By manipulating simple local rules, students could observe how global patterns emerged—an essential concept in both science and computational thinking.
The rise of block-based programming environments in the early 2000s, such as Scratch (Resnick et al., 2009) and later StarLogo Nova, further democratized access to computational modeling by making programming more intuitive and visual. These platforms eliminated syntax barriers, allowing learners to focus on the logic of computation rather than on the specifics of text-based coding. This shift reflects a broader educational trend: rather than training students solely in coding, modern computational modeling environments aim to develop broader problem-solving skills applicable across disciplines (Grover & Pea, 2013, Computational Thinking in K-12: A Review of the State of the Field).
Today, computational modeling is not only a tool for learning programming but also a gateway to scientific inquiry, enabling students to construct and test hypotheses in virtual environments that mirror real-world phenomena. The increasing integration of machine learning and data science into educational platforms continues to push the boundaries of how students engage with models, reinforcing the idea that computational modeling is not just about learning to program—it is about learning to think. As Wing (2006) famously stated, "computational thinking is a fundamental skill for everyone, not just for computer scientists", a perspective that has only grown more relevant in modern education (Computational Thinking, Communications of the ACM).
StarLogo NOVA as a Computational Modeling Tool
Modeling approaches: agent-based modeling
Strategies to integrate modeling into the school curriculum
Sample Learning goals for activities in SLNOVA
Educational resources and support materials
Case studies and practical examples
Designing New Modeling Challenges
How to design effective activities and challenges
Assessing and providing feedback on students’ models
Encouraging collaboration and teamwork