4. Animal Behavior Computational Lab: Part 1

Teresa Granito, Sugat Dabholkar, Mandy Peel, Shruti Researcher
Biology
4 class periods (45 minute periods)
High School Advanced Placement (AP) Biology
v5
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Overview

In this lesson, you will start the computational lab.

Please keep in mind that some of the activities/questions are designed expecting that you have done the physical lab. You can attempt those questions based on your expectations about the physical lab or you can skip those questions.

In this lesson, you will learn the following science practices using computational tools:

Science Practice 1

The student can use representations and models to communicate scientific phenomena and solve scientific problems.

Science Practice 2

The student can use mathematics appropriately.

Science Practice 3

The student can engage in scientific questioning to extend thinking or to guide investigations within the context of the AP course.

Science Practice 4

The student can plan and implement data collection strategies in relation to a particular scientific question. (Note: Data can be collected from many different sources, e.g., investigations, scientific observations, the findings of others, historic reconstruction and/or archived data.)

Science Practice 5

The student can perform data analysis and evaluation of evidence.

Underlying Pages

Standards

Next Generation Science Standards
  •   Life Science
    • [HS-LS2] Ecosystems: Interactions, Energy, and Dynamics
    • [HS-LS4] Biological Evolution: Unity and Diversity
  •   NGSS Crosscutting Concept
    • Patterns
    • Causation
    • Scale
    • Systems
    • Stability and Change
  •   NGSS Practice
    • Analyzing Data
    • Communicating Information
    • Constructing Explanations, Designing Solutions
    • Asking Questions, Defining Problems
    • Using Models
    • Using Mathematics
    • Arguing from Evidence
    • Conducting Investigations
Computational Thinking in STEM
  •   Data Practices
    • Analyzing Data
    • Collecting Data
    • Creating Data
    • Manipulating Data
    • Visualizing Data
  •   Modeling and Simulation Practices
    • Assessing Computational Models
    • Designing Computational Models
    • Using Computational Models to Find and Test Solutions
    • Using Computational Models to Understand a Concept
    • Constructing Computational Models
  •   Computational Problem Solving Practices
    • Assessing Different Approaches/Solutions to a Problem
    • Creating Computational Abstractions
    • Developing Modular Computational Solutions
    • Computer Programming
    • Troubleshooting and Debugging
    • Preparing Problems for Computational Solutions
  •   Systems Thinking Practices
    • Communicating Information about a System
    • Investigating a Complex System as a Whole
    • Thinking in Levels
    • Understanding the Relationships within a System
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