Welcome! I’m John Balwid from the Learning Lab at the Santa Fe Institute. With my background as a biology teacher, I’ve developed a keen interest in using computer models and simulations as educational tools. In this article, we’ll explore how models can enhance learning in the classroom, focusing on their theoretical foundations and how they fit into educational curricula.
We all create mental models to understand the world around us, often without realizing it. When you grasp a concept, it’s because you’ve formed a mental model of it. These models are like invisible boxes in our minds. By using computer models, we make these mental models visible and tangible. This process allows us to share, discuss, and refine our understanding.
Creating explicit models challenges us to question our assumptions and think critically. By comparing our models with others and testing them against real-world data, we can explore different scenarios, uncover trade-offs, and identify new questions to investigate.
Models are powerful tools beyond just prediction. While they are famously used in weather forecasting, they also explain phenomena like epidemic dynamics, wealth distributions, and energy flows in ecosystems. Models can reveal connections between seemingly unrelated processes, such as how an epidemic resembles the spread of ideas or innovations.
Moreover, models guide scientific inquiry by suggesting what data to collect. For example, Maxwell’s electromagnetic theory predicted radio waves, which were later discovered. Models help us formulate new questions, a crucial aspect of advancing knowledge.
The use of computer models in education has roots in Seymour Papert’s theory of constructionism. Papert, an MIT professor, built on Piaget’s constructivism, emphasizing active engagement in learning. Constructionism focuses on creating knowledge through interactions and the creation of socially relevant artifacts.
Papert is well-known for developing Logo, a programming language that allows learners to control a turtle on a screen. This hands-on approach helps students connect personal experiences with mathematical concepts. Today’s agent-based modeling environments, like StarLogo Nova, are descendants of Logo, enabling learners to model complex systems.
Integrating models into the classroom can follow a use-modify-create progression. This approach helps students develop skills in modeling and simulation, moving from simple to complex learning experiences.
In this phase, students work with a pre-existing model. They can run simulations, adjust parameters, and observe outcomes. This stage requires less time but provides a foundation for understanding model dynamics. Teachers should ensure students grasp the underlying mechanisms, not just the input-output relationships.
Here, students delve deeper by examining and altering the model’s rules. This phase is more challenging and time-consuming, but it enhances understanding of how models work. Students can uncover assumptions and see how changes impact model behavior.
In the final phase, students design and program their own models. This is the most demanding but rewarding stage, fostering deep learning. Students learn to pose questions, create models as experimental testbeds, and share their findings, fully engaging in the constructionist experience.
By following the use-modify-create progression, students develop critical thinking and problem-solving skills, making their learning experience richer and more meaningful.
Engage with a pre-existing computer model related to your field of study. Run simulations, adjust parameters, and observe the outcomes. Reflect on how these changes affect the model’s behavior and discuss your findings with peers to deepen your understanding of the model’s dynamics.
Take a model you have explored and modify its rules or parameters. This activity will challenge you to think critically about the assumptions underlying the model. Document the changes you make and analyze how they impact the model’s predictions or behaviors.
Design and program a simple model using a platform like StarLogo Nova. Choose a topic of interest and develop a model that can simulate a real-world process or phenomenon. Share your model with classmates and gather feedback to refine your approach.
Formulate a research question that can be explored using models. Use the use-modify-create progression to develop a comprehensive project. Present your findings in a class seminar, highlighting how modeling helped you investigate your question and what insights you gained.
Work in groups to review and critique a complex model used in scientific research. Analyze its assumptions, strengths, and limitations. Prepare a group presentation that discusses how the model contributes to understanding a specific phenomenon and suggests potential improvements.
Sure! Here’s a sanitized version of the transcript:
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Hi and welcome! My name is John Balwid, and I work at the Learning Lab at the Santa Fe Institute. I’ve been a classroom biology teacher and have a long-standing interest in understanding how to teach and learn with computer models and simulations.
In this video, we’re going to discuss the educational aspects of using models in the classroom, including the theoretical foundations and the learning progression used in the curriculum.
First of all, why should we model? Everyone is creating mental models of how the world works all the time, often without being conscious of it. When you say you understand something, it usually means that you’ve created a mental model of that concept. You can think of your mental models as being inside a box where no one else can see them. Learning to make computer models forces us to make our mental models explicit or visible. The models we create become objects or artifacts that we can share and discuss.
Making our mental models explicit forces us to confront our assumptions and think critically about what we’re attempting to model. By communicating our mental models clearly, we can compare them with others’ models and test them against real-world data. We can also use models to test different scenarios, revealing trade-offs, uncertainties, and sensitivities, and discovering new questions and lines of investigation.
Why are models so valuable? When you think of models, you might think of weather forecasting, where models are commonly used to predict the weather. However, models can do more than just predict; they can explain phenomena such as epidemic dynamics, wealth distributions, and energy flows through a food chain. Models can also suggest analogies that allow us to see connections between seemingly unrelated processes. For example, an epidemic is similar to the spread of ideas, revolutions, religions, the adoption of innovations, or even forest fires.
Models also raise new questions and guide data collection in science. Often, you need a theory before you know what data to collect. For instance, Maxwell’s electromagnetic theory deduced the existence of radio waves, which were later sought and found. Models help us think about what data to look for.
Doing well on school exams shows that you can answer someone else’s questions, but a more important goal of education is to ask new questions. Only new questions can lead to new discoveries and knowledge. Models help us generate new questions because they naturally lead us to ask “what if?”
The use of computer models as tools for thinking has a long history. Seymour Papert, a professor at MIT from the 1960s to the 1980s, invented a theory of knowledge called constructionism. Papert defined constructionism as building relationships between old and new knowledge through interactions with others while creating artifacts of social relevance. He built upon Piaget’s theory of constructivism, which emphasized the learner’s active engagement in knowledge construction. However, while Piaget saw formal abstraction as the goal, Papert valued both abstract and concrete forms of knowledge and the social context in which learning occurs.
When considering education, constructionism focuses on how learning is constructed through interactions between teachers and students as they engage in the design, creation, and discussion of learning artifacts. Papert is probably best known for inventing Logo, a programming language that features a turtle that can follow simple movement commands and draw on a computer screen. Logo allows children to identify with a computational object, manipulate the turtle similarly to real-world objects, and connect personal experiences to mathematical concepts and operations.
Computer models are a type of object to think with. The agent-based modeling and simulation environments we use today are descendants of Papert’s original Logo. Instead of using a single turtle, we now create virtual worlds populated with many turtles or agents, and through their interactions, we can model complex systems.
Models are learning artifacts that can be designed, created, and discussed in the act of constructing learning. Using models enables learners to manipulate mathematical and scientific objects and conduct inquiry in new ways while potentially reassessing and reinforcing content knowledge. Modifying and creating models are even more constructionist in nature because they engage students in a design process to produce objects to think with, which they can share and discuss with others.
The use-modify-create progression can be followed when integrating models into classroom practice. As students move from using to modifying to creating, they progress from easier but less powerful learning to harder but more powerful learning. They also transition from using someone else’s creation to making a creation of their own.
In the use phase, students work with a completed base model. They may run the model using different parameters, make observations, collect data, and try to infer the rules of the model. This type of activity takes less time to implement than modifying or creating. Teachers need preparation time to select or modify an existing model or create their own and design the activity for students to interact with that model. However, it’s important to allocate time to examine the model’s underlying mechanisms and discuss what is included and what has been left out. Otherwise, students may understand the connections between inputs and outputs but lack a real understanding of the mechanisms involved.
In the modify phase, students can examine the rules of the model, change them, or add new ones. This phase is a bit harder and takes more classroom time to help students understand the model’s rules. Computer models built in environments like StarLogo Nova allow students to see the rules behind them, uncover the assumptions of the model’s creator, and understand how changes may affect the model’s behavior.
In the create phase, students design the model and determine its rules. Often, multiple modifications amount to creating a model from scratch. This is the most time-consuming and challenging activity in the progression because it requires teaching students how to design and program, but it also has the most potential for deep learning.
Circling back to Piaget, Papert, and constructivism, students can fully participate in making their mental models explicit, sharing and comparing their models with others, and owning their ideas. When students pose their own questions and use a model of their own creation as an experimental testbed, then analyze and share the results of their research, the full power of the constructionist experience takes place.
The use-modify-create progression offers a scaffolded approach to developing student skills in modeling and simulation.
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This version maintains the key points while ensuring clarity and coherence.
Models – Representations or simulations used to explain complex biological processes or educational theories. – In biology, computer models are often used to simulate the spread of diseases in populations.
Learning – The process of acquiring knowledge or skills through study, experience, or teaching. – Active learning strategies in education can significantly enhance students’ understanding of complex biological concepts.
Education – The systematic instruction, schooling, or training given to students, especially in a formal setting. – Modern education in biology emphasizes hands-on laboratory experiences to reinforce theoretical knowledge.
Biology – The scientific study of life and living organisms, including their structure, function, growth, and evolution. – Advances in molecular biology have revolutionized our understanding of genetic diseases.
Inquiry – An approach to learning that involves exploring questions, problems, or scenarios to gain deeper understanding. – Inquiry-based learning in biology encourages students to formulate questions and conduct experiments to find answers.
Constructionism – A learning theory that suggests learners construct knowledge through experiences and reflections. – In biology education, constructionism can be applied by having students build models of cellular structures to enhance understanding.
Simulation – A method for implementing a model over time to study the behavior of a system. – Virtual lab simulations in biology allow students to experiment with chemical reactions safely and efficiently.
Critical – Involving analysis and evaluation to form a judgment, especially in the context of learning and scientific inquiry. – Developing critical thinking skills is essential for students to evaluate scientific literature in biology.
Engagement – The involvement or commitment of students in learning activities, often leading to better educational outcomes. – Interactive lectures and group discussions can increase student engagement in biology courses.
Knowledge – Information, understanding, or skills acquired through education or experience. – A solid foundation of knowledge in biology is crucial for students pursuing careers in healthcare and research.
