# Teaching Lesson 5

**Assignment:**

Review the activities from Lesson 5 as well as the material below. Reflect on how you would teach this in your class. Post your reflection to your portfolio in "Pedagogy->Module 3" under the heading Lesson 5.

**Lesson Objectives**

**The student will:**

- Develop an original design for a computational science project

- Develop a scientific question that can be answered with data output from running a model

- Use abstraction to develop an idea for a model

- Use the computational science cycle map to develop their question, model, and experimental design

- Practice pair programming and iterative design, implement, test cycle

**Teaching Summary**

**Getting
started **–
5 minutes

1. Review of the previous day’s lesson and concepts and connection to today’s lesson

**Activity #1: Designing and running
experiments **– 20 minutes

2. Designing your experiment

3. Running your experiment

4. Collecting and analyzing data

=

**Activity #2: Preparing your presentation **–
20 minutes

5. Presenting your project

**Wrap-up** – 5 minutes

Since this is an open-ended exploration and creative activity, there isn’t a formal wrap-up.

**Assessment questions (suggested):**

- Use the rubric to assess students' projects and presentations.

## NRC Disciplinary Core Ideas

**Interdependent Relationships in Ecosystems**

DCI-LS2.A: Organisms, and populations of organisms, are dependent on their environmental interactions both with other living things and with nonliving factors. In any ecosystem, organisms and populations with similar requirements for food, water, oxygen, or other resources may compete with each other for limited resources, access to which consequently constrains their growth and reproduction. Growth of organisms and population increases are limited by access to resources.

**Ecosystem Dynamics, Functioning, and
Resilience**

## NRC Scientific and Engineering Practice Standards

1A: Ask questions that arise from careful observation of phenomena, models, or unexpected results. 1B: Ask question to identify and/or clarify evidence and/or the premise(s) of an argument. 1C: Ask questions to determine relationships between independent and dependent variables and relationships in models.
2A: Evaluate limitations of a model for a proposed object or tool. 2C: Use and/or develop a model of simple systems with uncertain and less predictable factors. 2E: Develop and/or use a model to predict and/or describe phenomena. 2G: Develop and/or use a model to generate data to test ideas about phenomena in natural or designed systems, including those representing inputs and outputs, and those at unobservable scales.
3A: Plan an investigation individually and collaboratively, and in the design: identify independent and dependent variables and controls, what tools are needed to do the gathering, how measurements will be recorded, and how many data are needed to support a claim. 3B: Conduct an investigation and/or evaluate and/or revise the experimental design to produce data to serve as the basis for evidence that meet the goals of the investigation. 3D: Collect data to produce data to serve as the basis for evidence to answer scientific questions or test design solutions under a range of conditions.
4A: Construct, analyze, and/or interpret graphical displays of data and/or large data sets to identify linear and nonlinear relationships. 4B: Use graphical displays (e.g., maps, charts, graphs, and/or tables) of large data sets to identify temporal and spatial relationships. 4D: Analyze and interpret data to provide evidence for phenomena.
5B: Use mathematical representations to describe and/or support scientific conclusions and design solutions. 5D: Apply mathematical concepts and/or processes (e.g., ratio, rate, percent, basic operations, simple algebra) to scientific and engineering questions and problems.
6A: Construct an explanation that includes qualitative or quantitative relationships between variables that predict(s) and/or describe(s) phenomena. 6B: Construct an explanation using models or representations. 6D: Apply scientific ideas, principles, and/or evidence to construct, revise and/or use an explanation for real-world phenomena, examples, or events.
7C: Construct, use, and/or present an oral and written argument supported by empirical evidence and scientific reasoning to support or refute an explanation or a model for a phenomenon or a solution to a problem.
8E: Communicate scientific and/or technical information (e.g. about a proposed object, tool, process, system) in writing and/or through oral presentations. |

## NRC Crosscutting Concepts

1B: Patterns in rates of change and other numerical relationships can provide information about natural and human designed systems. 1D: Graphs, charts, and images can be used to identify patterns in data.
3A: Time, space, and energy phenomena can be observed at various scales using models to study systems that are too large or too small.
4A: Systems may interact with other systems; they may have sub-systems and be a part of larger complex systems. 4B: Models can be used to represent systems and their interactions—such as inputs, processes and outputs—and energy, matter, and information flows within systems. 4C: Models are limited in that they only represent certain aspects of the system under study.
5B: Within a natural or designed system, the transfer of energy drives the motion and/or cycling of matter.
7A: Explanations of stability and change in natural or designed systems can be constructed by examining the changes over time and forces at different scales, including the atomic scale. 7C: Stability might be disturbed either by sudden events or gradual changes that accumulate over time. 7D: Systems in dynamic equilibrium are stable due to a balance of feedback mechanisms. |

CSTA K-12 Computer Science Standards

CT |
Abstraction |
2-12 |
Use abstraction to decompose a problem into sub problems. |

CT |
Algorithms |
3A-3 |
Explain how sequence, selection, iteration and recursion are the building blocks of algorithms. |

CT |
Connections to other fields |
2-15 |
Provide examples of interdisciplinary applications of computational thinking. |

CT |
Data representation |
3A-12 |
Describe how mathematical and statistical functions, sets, and logic are used in computation. |

CT |
Modeling & simulation |
1:6-4 |
Describe how a simulation can be used to solve a problem. |

CT |
Modeling & simulation |
2-9 |
Interact with content-specific models and simulations to support learning and research. |

CT |
Modeling & simulation |
3A-8 |
Use modeling and simulation to represent and understand natural phenomena. |

CT |
Modeling & simulation |
3B-8 |
Use models and simulation to help formulate, refine, and test scientific hypotheses. |

CT |
Modeling & simulation |
3B-9 |
Analyze data and identify patterns through modeling and simulation. |

CPP |
Data collection & analysis |
2-9 |
Collect and analyze data that are output from multiple runs of a computer program. |

CPP |
Data collection & analysis |
3B-7 |
Use data analysis to enhance understanding of complex natural and human systems. |

**Responsiveness to various student needs**

In Project GUTS, we integrate teaching strategies found to be effective with learners with various backgrounds and characteristics such as economically disadvantaged students (EDS), students from groups that are underrepresented in STEM (URG), students with disabilities (DIS), English Language learners (ELL), girls and young women (FEM), students in alternative education (ALT), and gifted and talented students (GAT).

In each lesson we describe the accommodations and differentiation strategies that are integrated in the activities to support a wide range of learners.

Module 3 Lesson 5: Finishing your model and using your model as an experimental test bed.

**(EDS)** *Analyzing real-world
phenomena in their schools and neighborhoods, and asking authentic questions
using project-based learning, are effective teaching strategies to engage
economically disadvantaged students.*

**(URG)(FEM)
***Students are
given “agency” as the creators of their own models, and as researchers seeking
to answer a question or understand a phenomenon.*** ***The models that students build
are their own creations. Having students generate the problems and possible
solutions, an important scientific practice, is also very motivating for all
students, including girls.*

**(FEM)
***Careful
planning of partners for the on-computer activity is a strategy that encourages
participation for the girls in science.*

**(DIS)(GAT)*** There is ample opportunity to
extend level of content and time for practice by compacting areas already
mastered and to allow more time for students to complete previous experiments
and customizations. This
differentiation strategy of pacing can benefit a wide range of students
including those with learning disabilities and/or gifted and talented
students.) *

**(GAT)*** The teacher can promote autonomy
and the forging of authentic connections to the ecosystems content and to the
practice of computer modeling.*

**(GAT)*** Grouping students of similar
interests and ability, incorporating standards from a higher grade, providing
opportunities for self-directed projects are successful strategies for gifted
and talented students.*