## Creating Computational Models

Educator supports students in using computational thinking to model the behavior of a system that has interrelated parts.
Made by Digital Promise Computational Thinking
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### Key Method

The educator guides students through iteratively creating and evaluating a computational model by comparing it with a phenomenon the student is studying.

### Method Components

#### Computational models

Computational models are simulations that recreate the behavior of systems of interrelated parts. 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. Because systems often have relationships between many different parts, they 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. Computational tools are well-suited for creating models because they work in a reliable, repeatable fashion.

Creating a computational model—for example, with a computer simulation environment, a programming language, or a robotics kit—is an effective way to simulate a system quickly and reliably, 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. (“Understanding systems with computational models” is focused on helping students construct understandings from computational models of systems.)

#### Iterative design of models

The predictions of a reliable computational model should match what happens in the real world. When students create computational models, they often begin with hypotheses about how real-world systems work, defining rules either for the system as a whole or for how individual participants in the system should behave. The process of improving a model is an iterative cycle of comparing its behavior with the real world, making a hypothesis to account for mismatches, and then making changes to the model. Through this process, students develop systematic understandings of the real-world systems they are modeling and can generate questions for further investigation.

#### 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, which is relevant to your students, and which your students are able to observe. (See resources below.)
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. Select a computational tool in which students will build their models. Consider students’ existing skills with this tool, if any, and plan how you will support students. Keep in mind that it might be helpful to provide students with some of the building blocks they will need for their models. (See resources below. Additionally, “Selecting appropriate tools for computational thinking” addresses these considerations.)
5. Plan a lesson in which students iteratively create and evaluate a computational model to construct an understanding of the selected system. Suggested outline:
• Ask students the essential question. Ask students to observe the system and propose answers to the question. In discussing various answers, it will become clear that there is not a single correct answer. However, the same actors and the same actions will probably reappear in different students’ descriptions of the system. Make a list of the important actors and actions in the system.
• Introduce the computational tool and guide students in setting up the system’s actors and actions within the tool. Students should work in small groups. (“Using computers as tools for thinking” addresses the effective use of computational tools.)
• Ask students to compare the behavior of their models with the real-world system, and to change their system to match the real-world system.
• After providing time for students to work, bring students back together and ask them to share their strategies that are working effectively in their models. It may be helpful to show parts of successful models with the whole class and provide time for peer-to-peer support.
• Ask students to return to their small groups, going back and forth between their models and the real-world system.
• 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).
• Gerber, L. C., Kim, H., & Riedel-Kruse, I. H. (2016). Interactive Biotechnology: Design Rules for Integrating Biological Matter into Digital Games. In DiGRA/FDG.
• 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
• Sherin, B., diSessa, A. A., & Hammer, D. (1993). Dynaturtle revisited: Learning physics through collaborative design of a computer model. Interactive Learning Environments, 3(2), 91-118.

#### Iterative design of models

• Blikstein, P., Fuhrmann, T., Greene, D., & Salehi, S. (2012, June). Bifocal Modeling: Mixing Real and Virtual Labs for Advanced Science Learning. In Proceedings of the 11th International Conference on Interaction Design and Children (pp. 296-299). ACM. Chicago.
http://stanford.io/2eU5474
• Blikstein, P., & Wilensky, U. (2007). Bifocal modeling: a framework for combining computer modeling, robotics and real-world sensing. In Annual meeting of the American Educational Research Association (AERA 2007), Chicago, USA.
https://ccl.northwestern.edu/papers/2007/09-bifocal_modeling.pdf
• Fuhrmann, T. R., Kali, Y., & Hoadley, C. (2008). Helping education students understand learning through designing. Educational Technology, 48(2), 26-33.
• Garneli, V., & Chorianopoulos, K. (2017). Programming video games and simulations in science education: exploring computational thinking through code analysis. Interactive Learning Environments, 1-16. Chicago.
http://scholar.epidro.me/pdf/Garneli_2018.pdf
• Schwartz, D. L., & Martin, T. (2004). Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction, 22(2), 129-184.
• Wilensky, U., & Rand, W. (2015). An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. MIT Press.
• Windschitl, M., Thompson, J., & Braaten, M. (2008). Beyond the Scientific Method: Model-Based Inquiry as a New Paradigm of Preference for School Science Investigations. Science Education, 92(5), 941-967.

## 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 iteratively develop a computational model of a real-world system. The educator will analyze student 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

(200-word limit total)

• What system did students model in this lesson? Describe how students were able to make observations of the system (e.g., students were physically present, notes from prior observation, historical records, videos, an existing simulation).
• Describe the computational tool students used to develop their models. Why was this an appropriate tool for the task and for students’ existing skills? What support did you provide to enable students to construct models with this tool?

#### Part 2. Work Examples / Artifacts

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

1) Student artifacts

Submit one or more artifacts documenting students’ iterative processes of developing a computational model. These artifacts should reflect the work of two students (or two student groups). They may include a video recording, the teacher’s or another colleague’s observation notes, a student’s journal, screenshots, or other artifacts which show the development of the students’ models.

2) Analysis of student artifacts

(800-word limit total)

As you answer the following questions, refer to specific evidence from the artifacts submitted.

Note: If students worked in groups, you may choose to analyze one student’s learning within each group, or to analyze the learning of the groups as wholes.

• Computational models
• In what ways do the students’ final models capture the dynamics of the system under study? What dynamics of the system do the models fail to accurately simulate?
• How do the models and the students’ processes of developing it demonstrate understanding of the system under study? What did the students discover, observe, or hypothesize? What questions for further exploration were generated? Did the students develop misconceptions?
• Iterative design of models
• Describe two or three changes the students made to their models. How did the students decide to make these changes, and how did they proceed after making the changes?
• What new interactions did you observe between the students, the models, and the system under study (and possibly among the students themselves)? How do these interactions reflect developing skill in the iterative design of computational models?

#### 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 students whose work was considered in Part 2) to more effectively use a computational model to meet the systems thinking learning goal?
• The intuitions, hypotheses, and questions generated in developing a computational model may prepare students to understand more direct instruction in a subject area. How will you integrate this lesson into the broader curriculum?