## Understanding Systems with Computational Models

Educator supports students in developing systemic understandings of concepts by engaging with computational models.
Made by Digital Promise Computational Thinking
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### Key Method

The educator guides students in working with computational models to develop understandings of systems as wholes and as compositions of subsystems, as well as guides students in understanding the role of agents within systems.

### Method Components

#### Systems Thinking

Systems thinking is a way of thinking about the relationships between parts of a system. A system is any group of things which affect each other, such as plants and animals in a food web, parts of a machine, or images in a poem. Two ways to study systems are:

• The system as a whole: how the system behaves differently from its individual parts. For example, students in a science class might investigate how ant colonies take on personalities (young colonies tend to take more risks and are more aggressive), even though the individual ants behave according to the same well-understood rules.
• The impact of individual participants in a system: how choices made by a participant in a system affect the system as a whole. For example, students in an economics class might study a market-based economy like Craigslist, where prices are set by individual buyers and sellers.

Some of the properties commonly observed and studied in systems include:

• Feedback
• Emergent behavior
• Interrelated systems and subsystems
• Change over time

Systems and models of systems are an increasingly prevalent form of knowledge in the physical and social sciences. Systems thinking can offer rich new ways of answering questions in every discipline. For example, none of the following questions have straightforward answers. Students could construct systems thinking understandings of the relationships involved. For example:

• How do patterns of imagery interact to create meaning in a poem?
• Why are families so different from each other? Who can control how a family works?
• How do goods in a market get their prices?
• How can we use equations to express relationships between many different values?
• What happens when one species is removed from an ecosystem?
• Why is it so hard to predict the effects of climate change?

(See Resources section for lesson examples.)

#### Computational models

Computational models are useful for examining the relationships between components within systems. Typically these relationships are hard to think about intuitively and their behavior is hard to predict with simple equations. Small changes in specific parts of a system can have surprising effects through the whole system. That’s where a computational model is helpful: it offers a reliable, repeatable way to recreate the behavior of a system. Computational tools are well-suited to modeling systems quickly and reliably—for example, a computer simulation environment, a programming language, or a robotics kit—but you may even design an “unplugged” simulation for students to physically act out a repeated process to recreate the behavior of the system.

When students use computational models to make predictions about the behavior of systems, they can develop and test their own intuitions for how real-world systems work. Computational models are excellent for exploring the dynamics of a system. Students can develop understandings of the system as a whole by repeatedly changing parameters. Similarly, students can develop understandings of how individual participants affect a system by changing the rules that simulated participants follow within the model.

#### Suggested implementation

1. Before teaching the lesson, carefully read the submission guidelines and consider what evidence you will need to gather for your submission. If necessary, make arrangements to videotape the lesson or ask a colleague to observe and take notes. You may also want to plan to take notes immediately after the lesson to help you remember the details.
2. Choose a system which is important to your subject area and relevant to your students. (See examples above.)
3. Write an essential question which could lead your students into an exploration of the system. Or, if it is easier, summarize the big idea of the system. (Understandings are often described with essential questions or big ideas. “Integrating computational thinking into curriculum” addresses the nature of understandings.)
4. Find (or build) a computational model of the system. For example, NetLogo was designed specifically for learning about systems thinking through simulations. (See resources below. “Selecting appropriate tools for computational thinking” addresses these considerations.)
5. Plan a lesson in which students use the computational model to construct an understanding of the system. Suggested outline:
• Ask students the essential question. In discussing various answers, it will become clear that there is not a single correct answer. The answer always “depends.”
• Introduce the computational model and give students time to engage with it. Ask them to look for patterns. (“Using computers as tools for thinking” addresses the effective use of computational tools.)
• Pause exploration, and ask students to share patterns they have noticed and questions they have been discussing.
• Ask students to show their understanding. For example, they might write reflections, present theories to their peers, or frame questions to pursue in a future lesson.

## Research & Resources

### Supporting Research

#### Computational models

• Collins, A., & Ferguson, W. (1993). Epistemic Forms and Epistemic Games: Structures and Strategies to Guide Inquiry. Educational Psychologist, 28(1), 25-42. (full text accessible via Google Scholar).
• Horn, M. S., Brady, C., Hjorth, A., Wagh, A., & Wilensky, U. (2014, June). Frog Pond: A Code-First Learning Environment on Evolution and Natural Selection. In Proceedings of the 2014 conference on Interaction design and children (pp. 357-360). ACM.
http://tidal.northwestern.edu/media/files/pubs/idc444-horn.pdf
• Playable website
http://tidal.northwestern.edu/nettango/
• National Research Council. (2013). Next Generation Science Standards: For States, By States.
https://www.nextgenscience.org/
• Papert, S. (1980). Mindstorms: Children, Computers, and Powerful Ideas. Basic Books, Inc.
• Schreibman, S. (2004). Ray Siemens, and John Unsworth, eds. A Companion to Digital Humanities.
http://www.digitalhumanities.org/companion/
• Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351-380.
http://bit.ly/2wVlpTt

## Submission Requirements

### Submission Guidelines & Evaluation Criteria

To earn the micro-credential, you must earn a “passing” evaluation for Parts 1 and 3, and a “Yes” for each component of Part 2. In the assessment of this micro-credential, an educator will plan and teach a lesson in which students use a computational model to develop systems thinking understandings, analyze one student's learning in the lesson, and reflect on the lesson’s successes and limitations. The three parts of the assessment should fit together as evidence of professional reflective practice.

#### Part 1. Overview Questions

(400-word limit)

• What was the learning goal for the lesson you are submitting for this micro-credential? Explain why this learning goal is a systems thinking understanding. How does it consider a system as a whole, or interactions between actors in the system? Additionally, explain why the learning goal is important to your subject area.
• What activities did students participate in during the lesson? Explain your rationale for these activities. Note that the activities should include substantial student interaction with a computational model.

#### Part 2. Work Examples / Artifacts

To earn this micro-credential, please submit the following:

1) Student artifacts

Submit one or more artifacts of three students’ learning in the lesson. These may include artifacts created by the students, student reflection on the lesson, the educator’s or another colleague’s observation notes, a video recording, or other artifacts which provide evidence of student learning.

2) Analysis of student artifacts

(800-word limit total)

As you answer the following questions, choose one student whose work you submitted and refer to specific evidence from the artifact(s) submitted.

• Systems thinking
• To what extent did the student develop a systems thinking understanding? Provide evidence of how the student understood the system as a whole, the impact of individual parts on the system, or other dynamics of the system being investigated (see Method Components, above).
• How does the student’s systems thinking understanding connect to their broader learning? Provide evidence of how the student’s systems thinking understanding builds on prior knowledge or led to new insights, questions, or plans of action.
• Computational models
• How did the student interact with the computational model? Describe specific behaviors such as play, guided or self-directed exploration, testing, confirmation of assumptions, sharing discoveries, etc., and interpret these as part of the student’s learning process.
• How did the student use the computational model to develop a systems thinking understanding? Use specific evidence to show how the student’s interaction with the computational model led to a systems thinking understanding.

#### Part 3. Educator Reflection

(300-word limit total)

• Reflecting on the lesson, what might you change that would support one or more students (not necessarily the student whose work was considered in Part 2) to develop a clearer understanding of the learning goals?
• How has your approach to teaching systems or using computational thinking as an instructional tool changed following this lesson?