Bug Bites and Bites: A Camp Health Investigation
Created byAnna Myers
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Bug Bites and Bites: A Camp Health Investigation

Grade 6ScienceMath6 days
In this project, sixth-grade students investigate a mysterious illness outbreak at a summer camp, exploring the relationship between bug bites and illness using data analysis, probability, and technology. Students analyze camper data to determine correlations between bug bites, sleeping location, food consumed and the likelihood of getting sick, while distinguishing between correlation and causation. They create interactive dashboards to visualize their findings and present a final report summarizing their conclusions about the potential causes of the outbreak. This interdisciplinary project integrates science and math to address real-world problem-solving.
Data AnalysisProbabilityTechnologyCorrelationCausationBug BitesIllness
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Inquiry Framework

Question Framework

Driving Question

The overarching question that guides the entire project.To what extent can data analysis, probability, and technology help us understand and address the relationship between bug bites and illness among the campers, while considering the difference between correlation and causation?

Essential Questions

Supporting questions that break down major concepts.
  • How can we represent the data to show the relationship between bug bites and illness?
  • What are the different ways technology can help us collect, analyze, and present data on bug bites and illness?
  • How can probability help us understand the likelihood of getting sick based on the number of bug bites?
  • How do we determine if there is a correlation or causation between bug bites and illness?

Standards & Learning Goals

Learning Goals

By the end of this project, students will be able to:
  • Analyze data to determine the relationship between bug bites and illness.
  • Use probability to assess the likelihood of illness based on bug bite frequency.
  • Utilize technology to collect, analyze, and present data.
  • Distinguish between correlation and causation in the context of bug bites and illness.

Common Core Standards

CCSS.MATH.CONTENT.6.SP
Primary
Develop understanding of statistical variability.Reason: This standard aligns with the project's focus on data analysis and understanding the relationship between bug bites and illness.
CCSS.MATH.CONTENT.7.SP
Primary
Use random sampling to draw inferences about a population.Reason: This standard aligns with using data to make inferences about the camper population.

Next Generation Science Standards

NGSS.MS-LS1-4
Secondary
Use argument based on empirical evidence and scientific reasoning to support an explanation for how characteristic animal behaviors and specialized plant structures affect the probability of successful reproduction of animals and plants respectively.Reason: This standard can be connected to the project by discussing how behaviors (sleeping inside vs. outside) influence the probability of getting sick.
NGSS.MS-LS2-2
Supporting
Construct an explanation that predicts patterns of interactions among organisms across multiple ecosystems.Reason: This standard can be applied by examining the interaction between humans and insects in an ecosystem context, influencing health outcomes.

Entry Events

Events that will be used to introduce the project to students

Data Dive Challenge

Present students with a dataset that includes information about campers (sleeping location, number of bug bites, symptoms, etc.) but is incomplete and messy. The challenge is to clean, organize, and visualize the data using spreadsheets or other data visualization tools. Students develop initial theories based on patterns they discover within the data, setting the stage for deeper investigation.

Mystery Illness Outbreak

Campers are getting sick with a mysterious illness! Students receive an urgent message from the camp director detailing the outbreak and its symptoms, along with initial data on affected campers. Students must analyze the data to formulate initial hypotheses about the cause of the illness, focusing on potential environmental factors and correlations.
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Portfolio Activities

Portfolio Activities

These activities progressively build towards your learning goals, with each submission contributing to the student's final portfolio.
Activity 1

Probability Primer: Understanding Likelihood

This activity introduces the fundamental concepts of probability. Students will learn how to calculate probability, understand the difference between independent and dependent events, and apply these concepts to simple scenarios.

Steps

Here is some basic scaffolding to help students complete the activity.
1. Define probability and discuss its importance in understanding uncertain events.
2. Introduce the formula for calculating probability: P(event) = Number of favorable outcomes / Total number of possible outcomes.
3. Work through example problems involving calculating probabilities of simple events (e.g., rolling a die, flipping a coin).
4. Discuss the difference between independent and dependent events, providing examples of each.
5. Solve probability problems related to independent and dependent events.

Final Product

What students will submit as the final product of the activityA worksheet with completed probability calculations and explanations of key concepts.

Alignment

How this activity aligns with the learning objectives & standardsLays the foundation for CCSS.MATH.CONTENT.7.SP by introducing basic probability concepts necessary for making inferences about the camper population.
Activity 2

Data Dive: Exploring Camper Data

Students will explore an expanded dataset containing information about campers, including sleeping location, number of bug bites, illness status, food consumed, drinks consumed, and number of days camped.

Steps

Here is some basic scaffolding to help students complete the activity.
1. Receive and examine the expanded camper dataset, noting the variables included (sleeping location, bug bites, illness, food, drinks, days camped).
2. Create frequency tables for categorical variables (e.g., sleeping location, food consumed, drinks consumed).
3. Calculate descriptive statistics (mean, median, mode) for numerical variables (e.g., number of bug bites, days camped).
4. Create histograms or box plots to visualize the distribution of numerical variables.
5. Write a brief summary of the key characteristics of the camper population based on the data.

Final Product

What students will submit as the final product of the activityA summary report with frequency tables, descriptive statistics, and visualizations of the camper dataset.

Alignment

How this activity aligns with the learning objectives & standardsAddresses CCSS.MATH.CONTENT.6.SP by developing an understanding of statistical variability through data organization and initial analysis of an expanded dataset. It also lays the groundwork for analyzing the relationship between variables, aligning with the project's focus on data analysis and understanding the relationship between potential factors and illness.
Activity 3

Probability Predictor: Likelihood of Illness

Students calculate the probability of illness based on various factors, including the number of bug bites, sleeping location, food consumed, drinks consumed and days camped. They will use the cleaned dataset to determine the frequency of illness among campers with varying conditions and use this information to calculate probabilities.

Steps

Here is some basic scaffolding to help students complete the activity.
1. Group campers based on different factors (e.g., bug bites, sleeping location, food consumed, drinks consumed, days camped).
2. Calculate the number of campers in each group who contracted the illness.
3. Calculate the probability of illness for each group by dividing the number of sick campers by the total number of campers in that group.
4. Represent the probabilities in a table or chart, clearly showing the relationship between different factors and the likelihood of illness.
5. Write a short explanation of how the probability changes based on different factors.

Final Product

What students will submit as the final product of the activityA table or chart showing the probability of illness for different groups based on different conditions, along with a written explanation of the findings.

Alignment

How this activity aligns with the learning objectives & standardsAligns with CCSS.MATH.CONTENT.7.SP by using data to make inferences about the camper population, specifically the likelihood of illness based on various factors. It directly addresses the learning goal of using probability to assess the likelihood of illness based on bug bite frequency.
Activity 4

Tech-Enhanced Analysis: Interactive Data Dashboard

Students use technology to create an interactive data dashboard that allows users to explore the relationship between bug bites, sleeping location, food consumed, drinks consumed, days camped and illness. They will use data visualization tools (e.g., Tableau Public, Google Data Studio) to create interactive charts, graphs, and maps that highlight key findings.

Steps

Here is some basic scaffolding to help students complete the activity.
1. Choose a data visualization tool (e.g., Tableau Public, Google Data Studio).
2. Import the expanded camper dataset into the chosen tool.
3. Create interactive visualizations that allow users to explore the relationship between various factors and illness status. Visualizations might include interactive maps showing the distribution of bug bites and illnesses across the campsite.
4. Add filters and controls that allow users to drill down into the data and explore specific subgroups of campers.
5. Write a brief user guide explaining how to use the dashboard and interpret the visualizations.

Final Product

What students will submit as the final product of the activityAn interactive data dashboard that allows users to explore the relationship between various factors and illness, along with a user guide.

Alignment

How this activity aligns with the learning objectives & standardsSupports the learning goal of utilizing technology to collect, analyze, and present data. It also reinforces CCSS.MATH.CONTENT.6.SP and CCSS.MATH.CONTENT.7.SP by providing a dynamic platform for data exploration and inference.
Activity 5

Correlation vs. Causation: The Investigation Report

Students conduct a deeper investigation into the relationship between bug bites, sleeping location, food consumed, drinks consumed, days camped and illness, focusing on the critical distinction between correlation and causation. They will research potential confounding variables and design a simulated experiment to test their hypotheses.

Steps

Here is some basic scaffolding to help students complete the activity.
1. Research the difference between correlation and causation, providing real-world examples.
2. Identify potential confounding variables that might influence the relationship between bug bites and illness (e.g., individual immune response, types of bugs).
3. Design a simulated experiment to test whether bug bites directly cause the illness. Consider ethical implications.
4. Write a report summarizing the findings, clearly explaining whether the data supports a causal relationship between bug bites and illness, or if the observed correlation is likely due to other factors.
5. Present findings.

Final Product

What students will submit as the final product of the activityA report that differentiates between correlation and causation in the context of bug bites, sleeping location, food consumed, drinks consumed, days camped and illness.

Alignment

How this activity aligns with the learning objectives & standardsAddresses the learning goal of distinguishing between correlation and causation in the context of bug bites and illness. It requires students to apply their understanding of data analysis and probability to make informed judgments about the nature of the relationship between variables.
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Rubric & Reflection

Portfolio Rubric

Grading criteria for assessing the overall project portfolio

Comprehensive Rubric: Camper Illness Investigation

Category 1

Data Representation and Analysis

This category assesses students' ability to effectively represent and analyze camper data using frequency tables, descriptive statistics, and visualizations.
Criterion 1

Data Organization and Accuracy

Demonstrates the ability to organize and accurately represent camper data in frequency tables, descriptive statistics (mean, median, mode), and visualizations (histograms, box plots).

Exemplary
4 Points

Data is meticulously organized and presented with exceptional accuracy, revealing deep insights into the camper population. Calculations are flawless, and visualizations are highly effective.

Proficient
3 Points

Data is well-organized and presented accurately, providing a clear overview of the camper population. Calculations are correct, and visualizations are effective.

Developing
2 Points

Data is organized with some inconsistencies or inaccuracies, impacting the clarity of the camper population overview. Calculations contain minor errors, and visualizations require refinement.

Beginning
1 Points

Data is poorly organized and contains significant inaccuracies, making it difficult to understand the camper population. Calculations contain major errors, and visualizations are ineffective or missing.

Criterion 2

Insightful Interpretation

Demonstrates the ability to draw meaningful conclusions and insights from the data analysis, relating them to the context of the camper illness investigation.

Exemplary
4 Points

Provides insightful interpretations of the data, connecting patterns and trends to potential causes of the camper illness with sophisticated reasoning and depth.

Proficient
3 Points

Provides clear interpretations of the data, identifying patterns and trends relevant to the camper illness investigation.

Developing
2 Points

Provides basic interpretations of the data, but struggles to connect patterns and trends to the camper illness investigation.

Beginning
1 Points

Fails to provide meaningful interpretations of the data or connect them to the camper illness investigation.

Category 2

Probability and Likelihood

This category evaluates students' ability to calculate and interpret probabilities related to camper illness based on various factors.
Criterion 1

Accurate Probability Calculations

Demonstrates the ability to accurately calculate probabilities of illness based on different factors (e.g., bug bites, sleeping location, food consumed).

Exemplary
4 Points

Calculations are flawless, demonstrating a deep understanding of probability concepts and their application to complex scenarios.

Proficient
3 Points

Calculations are accurate and demonstrate a solid understanding of probability concepts.

Developing
2 Points

Calculations contain some errors, indicating a partial understanding of probability concepts.

Beginning
1 Points

Calculations contain significant errors, demonstrating a limited understanding of probability concepts.

Criterion 2

Meaningful Interpretation of Probabilities

Demonstrates the ability to interpret the calculated probabilities and explain their significance in the context of camper illness.

Exemplary
4 Points

Provides insightful and nuanced interpretations of probabilities, connecting them to potential causes and implications for camper health with sophisticated reasoning.

Proficient
3 Points

Provides clear interpretations of probabilities, explaining their relevance to camper illness and potential risk factors.

Developing
2 Points

Provides basic interpretations of probabilities, but struggles to connect them to the broader context of camper illness.

Beginning
1 Points

Fails to provide meaningful interpretations of probabilities or connect them to the camper illness scenario.

Category 3

Technology Integration and Dashboard Design

This category assesses students' ability to effectively use technology to create an interactive data dashboard that explores the relationships between various factors and camper illness.
Criterion 1

Effective Dashboard Design

The data dashboard is well-designed, user-friendly, and effectively communicates key findings about the relationship between variables and illness.

Exemplary
4 Points

Dashboard is exceptionally well-designed, intuitive, and visually appealing, providing a seamless user experience and highlighting key insights with clarity and sophistication.

Proficient
3 Points

Dashboard is well-designed, easy to navigate, and effectively communicates key findings.

Developing
2 Points

Dashboard design has some flaws, making it somewhat difficult to navigate or interpret the data effectively.

Beginning
1 Points

Dashboard is poorly designed, difficult to use, and fails to effectively communicate key findings.

Criterion 2

Insightful Data Visualization

The visualizations within the dashboard effectively represent the data and allow users to explore the relationships between different factors and illness.

Exemplary
4 Points

Visualizations are highly insightful and effectively reveal complex relationships within the data, demonstrating a sophisticated understanding of data representation and its implications.

Proficient
3 Points

Visualizations are clear, accurate, and effectively represent the data, allowing users to explore key relationships.

Developing
2 Points

Visualizations have some flaws or inaccuracies, making it difficult to fully explore the relationships within the data.

Beginning
1 Points

Visualizations are ineffective, inaccurate, or missing, hindering the exploration of relationships within the data.

Category 4

Correlation vs. Causation

This category assesses students' understanding of the difference between correlation and causation and their ability to apply this understanding to the camper illness investigation.
Criterion 1

Understanding of Concepts

Demonstrates a clear understanding of the difference between correlation and causation, including the role of confounding variables.

Exemplary
4 Points

Demonstrates a sophisticated understanding of correlation and causation, including nuanced insights into confounding variables and alternative explanations for observed relationships.

Proficient
3 Points

Demonstrates a clear and accurate understanding of correlation and causation, including the role of confounding variables.

Developing
2 Points

Demonstrates a partial understanding of correlation and causation, but struggles to explain the role of confounding variables.

Beginning
1 Points

Demonstrates a limited or inaccurate understanding of correlation and causation.

Criterion 2

Application to Camper Illness

Applies the understanding of correlation and causation to the camper illness investigation, evaluating whether the data supports a causal relationship between bug bites (or other factors) and illness.

Exemplary
4 Points

Provides a compelling and well-supported argument about the relationship between variables and illness, clearly differentiating between correlation and causation with sophisticated reasoning and evidence.

Proficient
3 Points

Provides a clear and logical argument about the relationship between variables and illness, differentiating between correlation and causation.

Developing
2 Points

Attempts to differentiate between correlation and causation in the context of camper illness, but the argument is not fully developed or supported by evidence.

Beginning
1 Points

Fails to differentiate between correlation and causation in the context of camper illness or provide a clear argument about the relationship between variables and illness.

Reflection Prompts

End-of-project reflection questions to get students to think about their learning
Question 1

How has your understanding of data analysis and probability changed throughout this project?

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Question 2

What was the most challenging part of this project? Why? How did you overcome the challenge?

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Question 3

To what extent do you think technology enhanced your ability to analyze and present data in this project? Use a scale from 1-5, with 1 being 'not at all' and 5 being 'significantly'.

Scale
Required
Question 4

Which activity (Probability Primer, Data Dive, Probability Predictor, Interactive Data Dashboard, Correlation vs. Causation Investigation) was most helpful in understanding the relationship between bug bites and illness, and why?

Multiple choice
Required
Options
Probability Primer
Data Dive
Probability Predictor
Interactive Data Dashboard
Correlation vs. Causation Investigation
Question 5

If you were to conduct this project again, what adjustments would you make to the data collection or analysis process to improve the accuracy or reliability of your conclusions?

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