Computational modeling at school

1. Foreword

The importance of computational modeling in education

The importance ofand 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.

2.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

Origins and evolution of the LOGO language

Development of programming languages oriented towards education

3.

Fundamentals of computational modeling

Definition and basic concepts in modeling

Advantages and challenges of using models in teaching

4.

Computational modeling in the classroom

Pedagogical approaches to teaching modeling

Benefits of modeling for developing critical and analytical thinking

Modeling approaches: agent-based modeling

Strategies to integrate modeling into the school curriculum

5.

StarLogo NOVA as a Computational Modeling Tool

Sample Learning goals for activities in SLNOVA

Educational resources and support materials

Case studies and practical examples

6.

Designing New Modeling Challenges

How to design effective activities and challenges

Assessing and providing feedback on students’ models

Encouraging collaboration and teamwork