Age Group: 12-13 years (Grade 7)
School: ________________
Teachers: ________________
Date: ________________
Proposed Duration: 6 weeks / Number of Hours: 30 hours
How We Organize Ourselves
An inquiry into the interconnectedness of human-made systems and communities; the structure and function of organizations; societal decision-making; economic activities and their impact on humankind and the environment.
Data visualization transforms raw information into powerful stories that help us understand patterns, make predictions, and solve real-world problems in our interconnected world.
Function: How do data displays work to communicate information effectively?
Connection: How are data points connected to reveal larger patterns and relationships?
Perspective: How do different visualizations of the same data tell different stories?
Causation: How do data patterns help us understand cause and effect relationships?
Pattern
Evidence
Systems
Communication
Innovation
Representation
The Language of Data: How data scientists organize and represent information to reveal hidden patterns
Stories in Numbers: How different visualization methods communicate different messages about the same data
Data for Change: How data visualization influences decisions and drives social change
Primary Focus:
Inquirers: Developing curiosity about data patterns and relationships in real-world contexts
Thinkers: Using critical thinking to analyze and interpret complex data displays
Communicators: Expressing findings through multiple data visualization formats adapted for diverse audiences
Secondary Development:
Risk-takers: Experimenting with new visualization techniques and challenging conventional data presentations
Reflective: Considering the effectiveness and ethics of data presentations in society
Caring: Using data to understand and address community and environmental issues
How do data scientists transform raw numbers into meaningful stories?
What makes some data visualizations more effective than others?
How can we use data to make our community better?
What ethical responsibilities do we have when presenting data to others?
How do measures of central tendency help us understand datasets?
How can probability help us make predictions about future events?
Week 1: Foundation Building - The Data Around Us (5-8 activities, 7-12 days)
Activity 1.1: Data Pioneers Gallery Walk (1-3 days) Guiding Question: How have data visualization pioneers changed the way we understand our world?
Learning Objectives:
Identify key historical figures in data visualization (Marie Tharp, Beverly Archer)
Understand how mathematical concepts apply to real-world problem solving
Recognize coordinate relationships and scale interpretation
Implementation Options:
Quick (1-2 days): View portraits and create timeline; analyze Tharp's ocean mapping techniques
Standard (3-4 days): Research presentations; compare historical vs. modern methods; create depth charts
Extended (5-7 days): Museum exhibition; community interviews; additional pioneer research
Materials: Historical images, coordinate grid paper, depth measurement datasets, presentation tools
Assessment Evidence: Students explain how mathematical visualization techniques solve real problems using evidence from historical examples
Minnesota Standards Integration:
7.4.1.1: Foundation for using data displays to draw conclusions
7.4.2.1: Understanding different chart types and their purposes
Activity 1.2: Data Trace Investigation (2-4 days) Guiding Question: How much data do we create every day, and what stories does it tell?
Learning Objectives:
Understand ubiquity of data generation in modern life
Calculate measures of central tendency from personal data (7.4.1.1)
Use spreadsheet technology for basic statistical analysis (7.4.1.2)
Implementation Options:
Quick (2 days): List daily data creation; calculate class mean for text messages; create simple bar graph
Standard (3-4 days): 24-hour tracking; infographic creation; Google Sheets introduction; compare measures of central tendency
Extended (4+ days): Week-long collection; family interviews; "Anatomy of My AI System" project; administration presentation
Materials: Data collection logs, Google Sheets access, smartphones for tracking, infographic tools
Assessment Evidence: Students accurately calculate mean, median, and range from collected data and explain what each measure reveals
Minnesota Standards Integration:
7.4.1.1: Design experiments, calculate mean/median/range, make predictions
7.4.1.2: Use spreadsheets to examine data impact
Activity 1.3: Contemporary Data Artists Exploration (2-3 days) Guiding Question: How do artists use data to create beautiful and meaningful communications?
Learning Objectives:
Analyze contemporary data art approaches (Crawford/Joler, Lupi/Posavec)
Understand relationship between aesthetic design and mathematical accuracy
Begin incorporating artistic elements into data displays
Implementation Options:
Quick (2 days): View examples; identify design principles; create artistic chart redesign
Standard (3 days): Design analysis; create personal data postcards inspired by "Dear Data"; present findings
Extended (3+ days): Artist interview simulation; advanced design workshop; community art installation
Materials: Contemporary data art examples, art supplies, design software, personal data collection materials
Assessment Evidence: Students create mathematically accurate visualizations incorporating artistic design principles
Minnesota Standards Integration:
7.4.2.1: Choose appropriate data displays and create using technology
Foundation for effective visual communication
Week 2: Measures of Center - Finding the Heart of Data (4-5 activities, 8-12 days)
Activity 2.1: Outlier Impact Investigation (2-4 days) Guiding Question: How do extreme values change our understanding of a dataset?
Learning Objectives:
Understand how individual data points affect measures of central tendency (7.4.1.2)
Use spreadsheet technology to examine data impacts systematically (7.4.1.2)
Develop critical thinking about data reliability and outliers
Implementation Options:
Quick (2 days): Given dataset with outlier; calculate statistics with/without; observe differences
Standard (3 days): Google Sheets exploration; multiple outlier scenarios; real-world examples
Extended (4+ days): Real-world outlier research; interactive presentation; decision-making scenarios
Materials: Datasets with outliers, Google Sheets, real-world examples, calculators
Assessment Evidence: Students predict and explain how adding/removing data points affects mean and median with supporting calculations
Minnesota Standards Integration:
7.4.1.2: Describe impact of inserting/deleting data points on mean and median; use spreadsheets
Activity 2.2: Environmental Data Deep Dive (3-5 days) Guiding Question: How can statistical analysis help us understand environmental changes in our community?
Learning Objectives:
Apply statistical analysis to authentic environmental data (7.4.1.1)
Use data to draw conclusions about environmental patterns (7.4.1.1)
Make predictions based on environmental data trends (7.4.1.1)
Implementation Options:
Quick (3 days): Local temperature data; calculate monthly means; compare to averages
Standard (4 days): NOAA database access; seasonal analysis; prediction creation; Marie Tharp connections
Extended (5+ days): Multi-year analysis; community presentation; citizen science project; artistic interpretation
Materials: NOAA climate data, spreadsheet software, scientific calculators, presentation tools
Assessment Evidence: Students use measures of central tendency to analyze environmental trends and make supported predictions with clear mathematical reasoning
Minnesota Standards Integration:
7.4.1.1: Design experiments, determine mean/median/range, draw conclusions, make predictions
Authentic scientific application of statistical concepts
Week 3: Visual Communication - Charts That Tell Stories (4-5 activities, 8-12 days)
Activity 3.1: Circle Graph Proportion Mastery (2-4 days) Guiding Question: How can we use proportional reasoning to create accurate and compelling circle graphs?
Learning Objectives:
Use proportional reasoning to create circle graphs (7.4.2.1)
Choose appropriate data displays for different purposes (7.4.2.1)
Create data displays using spreadsheet technology (7.4.2.1)
Implementation Options:
Quick (2 days): Class lunch preferences; calculate percentages; create circle graph
Standard (3 days): Proportional reasoning practice; digital circle graphs; news analysis
Extended (4+ days): School survey design; administration presentation; accessible chart design
Materials: Survey data, protractors, Google Sheets, news articles with charts
Assessment Evidence: Students create accurate circle graphs using proportional reasoning and explain when circle graphs are most appropriate
Minnesota Standards Integration:
7.4.2.1: Use reasoning with proportions for circle graphs; choose appropriate displays; use technology
Activity 3.2: Histogram Construction and Analysis (2-3 days) Guiding Question: How do histograms help us understand the distribution and frequency of data?
Learning Objectives:
Create and interpret histograms effectively (7.4.2.1)
Understand how data grouping affects interpretation
Use technology for sophisticated data displays (7.4.2.1)
Implementation Options:
Quick (2 days): Student height histogram; predetermined intervals; textbook examples
Standard (3 days): Multiple datasets; variable intervals; Google Sheets creation; scientific examples
Extended (3+ days): Research study design; animated histograms; science collaboration; museum display
Materials: Class measurement data, Google Sheets, scientific journals, presentation tools
Assessment Evidence: Students create accurate histograms and explain how interval choices affect data interpretation
Minnesota Standards Integration:
7.4.2.1: Display and interpret data using histograms; use technology for creation
Week 4: Probability and Predictions - Understanding Uncertainty (4-5 activities, 8-12 days)
Activity 4.1: Sample Space and Basic Probability (2-4 days) Guiding Question: How can we determine all possible outcomes and calculate their likelihood?
Learning Objectives:
Calculate probability as fraction of sample space (7.4.3.2)
Express probabilities as fractions, decimals, and percentages (7.4.3.2)
Design experiments with known probabilities
Implementation Options:
Quick (2 days): Simple experiments; basic calculations; fraction/decimal/percent conversion
Standard (3 days): Complex sample spaces; tree diagrams; real-world applications
Extended (4+ days): Probability carnival; expected values; research applications; artistic probability
Materials: Dice, coins, cards, calculators, game materials, tree diagram templates
Assessment Evidence: Students accurately calculate probabilities and convert between multiple representations
Minnesota Standards Integration:
7.4.3.2: Calculate probability as fraction of sample space; express as percents, decimals, fractions
Activity 4.2: Area-Based Probability (2-3 days) Guiding Question: How can geometric area help us determine probability in spatial situations?
Learning Objectives:
Calculate probability as fraction of area (7.4.3.2)
Connect geometry concepts to probability applications
Design real-world area probability scenarios
Implementation Options:
Quick (2 days): Simple shapes; basic area calculations; spinner probability
Standard (3 days): Dartboard design; coordinate geometry; geometric probability problems
Extended (3+ days): Real-world scenarios; artistic installations; architecture research; advanced calculations
Materials: Geometric shapes, measuring tools, coordinate grid paper, design software
Assessment Evidence: Students accurately calculate area-based probabilities and create original geometric probability problems
Minnesota Standards Integration:
7.4.3.2: Calculate probability as fraction of area; express in multiple forms
Activity 4.3: Proportional Reasoning for Predictions (2-4 days) Guiding Question: How can we use probability to make reasonable predictions about future events?
Learning Objectives:
Use proportional reasoning to predict relative frequencies (7.4.3.3)
Understand relationship between probability and experimental outcomes
Make data-based predictions about future events
Implementation Options:
Quick (2 days): Simple predictions; 100-trial scenarios; comparison with theoretical
Standard (3 days): Experimental design; prediction accuracy analysis; real-world applications
Extended (4+ days): Comprehensive models; professional field research; Monte Carlo simulation; administration presentation
Materials: Experimental materials, calculators, real-world data sources, presentation tools
Assessment Evidence: Students use proportional reasoning to make accurate predictions and explain the relationship between probability and outcomes
Minnesota Standards Integration:
7.4.3.3: Use proportional reasoning to predict relative frequencies based on probabilities
Week 5: Advanced Applications - Data in the Real World (3-4 activities, 10-15 days)
Activity 5.1: Environmental Data Storytelling (3-5 days) Guiding Question: How can we use environmental data to tell compelling stories that inspire action?
Learning Objectives:
Apply statistical analysis to authentic environmental issues
Use data to make informed environmental predictions (7.4.1.1)
Create compelling visualizations for environmental advocacy
Connect mathematical skills to environmental stewardship
Implementation Options:
Quick (3 days): Local air quality analysis; basic charts; simple recommendations
Standard (4 days): EPA databases; comprehensive presentation; multiple chart types; prediction creation
Extended (5+ days): Community partnerships; city council presentation; citizen science; data art installation
Materials: Environmental databases, spreadsheet software, presentation tools, scientific equipment
Assessment Evidence: Students create meaningful environmental analysis with accurate statistics and appropriate visualizations
Minnesota Standards Integration:
7.4.1.1: Use statistics to draw conclusions and make predictions
7.4.2.1: Choose appropriate displays for environmental data
Real-world application of all probability and statistics standards
Activity 5.2: Community Health Data Investigation (4-6 days) Guiding Question: How can data analysis help us understand and improve our community's health?
Learning Objectives:
Apply statistical analysis to community health issues
Use data to identify health disparities and solutions
Create compelling health communications using data visualization
Connect mathematical skills to community service
Implementation Options:
Quick (4 days): Class health data; basic recommendations; simple charts
Standard (5 days): County health data; statistical comparisons; comprehensive visualizations; proportional analysis
Extended (6+ days): Health department partnership; community campaign; school board presentation; monitoring app design
Materials: Health databases, survey tools, spreadsheet software, presentation tools, community contacts
Assessment Evidence: Students create meaningful community health analysis with accurate statistics and actionable recommendations
Minnesota Standards Integration:
Application of all Data Analysis & Probability standards (7.4.1.1, 7.4.1.2, 7.4.2.1, 7.4.3.2, 7.4.3.3)
Authentic community engagement context
Week 6: Synthesis and Action - Becoming Data Storytellers (3-4 activities, 8-12 days)
Activity 6.1: Capstone Data Investigation Project (5-10 days) Guiding Question: How can we use everything we've learned to investigate an important issue and propose data-driven solutions?
Learning Objectives:
Synthesize all unit learning in authentic application
Demonstrate mastery of all MCA standards through real-world problem solving
Develop advanced project management and presentation skills
Create positive community impact through data science
Implementation Options:
Quick (5 days): School-based problem; basic data collection; simple presentation
Standard (7 days): Complete data cycle; professional presentation; authentic audience; action plan
Extended (10 days): Major community partnership; extensive investigation; implementation; lasting impact
Materials: Project planning tools, data collection materials, analysis software, presentation equipment, community partnerships
Assessment Evidence: Students complete comprehensive data investigation demonstrating mastery of all unit standards and create meaningful community impact
Minnesota Standards Integration:
Comprehensive demonstration of Standards 7.4.1.1, 7.4.1.2, 7.4.2.1, 7.4.3.2, and 7.4.3.3
Authentic application in real-world context
Activity 6.2: Data Storytelling Exhibition (3-5 days) Guiding Question: How can we effectively communicate our mathematical learning to diverse community audiences?
Learning Objectives:
Communicate mathematical learning to diverse audiences
Celebrate achievement and inspire continued learning
Share knowledge with broader community
Reflect on growth and future learning goals
Implementation Options:
Quick (3 days): Classroom gallery; classmate presentations; portfolio reflection
Standard (4 days): School exhibition; parent/community audiences; interactive displays; reflection component
Extended (5+ days): Community data science fair; museum partnership; media coverage; lasting resources
Materials: Exhibition space, display materials, presentation equipment, community partnerships, documentation tools
Assessment Evidence: Students effectively communicate data science learning to authentic audiences and reflect meaningfully on growth
Minnesota Standards Integration:
Communication of mastery across all unit standards to authentic audiences
Demonstration of mathematical communication skills
Mathematics Teacher: Primary instruction and assessment
Science Teacher: Environmental data applications and scientific method integration
Arts Teacher: Visual design principles and artistic data representation
Technology Coordinator: Google Sheets training and troubleshooting support
Community Partners: Environmental scientists, health professionals, data professionals
Family Volunteers: Exhibition support and authentic audience participation
1:1 Chromebook/tablet access for all students
Google Workspace for Education with Sheets access
Internet connectivity for database access and research
Presentation equipment (projectors, speakers, extension cords)
Calculators (scientific preferred)
Digital cameras for documentation
Mathematical tools: rulers, protractors, graph paper, colored pencils
Art supplies: chart paper, markers, colored paper, scissors, glue
Manipulatives: dice, coins, spinners, cards for probability experiments
Display materials: poster boards, easels, bulletin board space
Community exhibition space for final presentations
Curated databases: NOAA Climate Data, EPA Air Quality, County Health Rankings
Historical materials: Marie Tharp biography and ocean floor maps, Beverly Archer research
Contemporary examples: Crawford/Joler "Anatomy of AI," Lupi/Posavec "Dear Data"
Assessment rubrics aligned with Minnesota MCA standards
Family communication materials in multiple languages
Week 3: Circle Graph and Histogram Mastery Assessment
Students create both circle graphs and histograms for same dataset
Justify choice of display type for different audiences
Demonstrate proportional reasoning and technology skills
Standards: 7.4.2.1 (display and interpret data in variety of ways)
Week 4: Probability Application Project
Calculate probabilities using both sample space and area methods
Create prediction scenarios using proportional reasoning
Express probabilities in multiple forms (fractions, decimals, percentages)
Standards: 7.4.3.2, 7.4.3.3 (calculate probabilities, use proportional reasoning)
Week 6: Capstone Data Investigation (Major Assessment)
Complete data cycle: question formulation, collection, analysis, interpretation, presentation
Use all statistical measures appropriately (mean, median, range)
Create professional visualizations using technology
Present findings to authentic community audience
Standards: Comprehensive application of 7.4.1.1, 7.4.1.2, 7.4.2.1, 7.4.3.2, 7.4.3.3
Daily:
Exit tickets with statistical problems
Peer review of data visualizations
Mathematical journal reflections
Quick concept checks during activities
Weekly:
Google Sheets skill demonstrations
Statistical reasoning explanations
Chart interpretation tasks
Real-world application problems
Ongoing:
Digital portfolio development
Self-assessment using unit rubrics
Peer feedback protocols
Teacher observation checklists
Understanding Mathematical Concepts:
Excellent: Demonstrates deep understanding of statistical concepts through multiple applications; explains reasoning clearly
Proficient: Shows solid understanding of statistical measures and probability; makes appropriate calculations
Developing: Shows basic understanding with support; makes some calculation errors
Beginning: Shows limited understanding; needs significant support for calculations
Data Visualization Skills:
Excellent: Creates sophisticated, accurate visualizations; chooses optimal display types; uses technology effectively
Proficient: Creates accurate basic visualizations; makes appropriate display choices; uses technology competently
Developing: Creates simple visualizations with support; makes display choices with guidance
Beginning: Attempts visualization with significant support; limited technology skills
Communication and Application:
Excellent: Communicates mathematical thinking clearly to diverse audiences; applies learning to real-world problems effectively
Proficient: Explains mathematical concepts clearly; makes basic real-world connections
Developing: Communicates mathematical ideas with support; makes limited real-world connections
Beginning: Limited mathematical communication; needs support for application
Mathematical Growth:
How has your understanding of data and statistics changed during this unit?
Which statistical concepts do you feel most confident about? Which need more work?
How do you see statistics being used in the world around you now?
Inquiry and Investigation:
What questions about data are you most curious about now?
How has your research and investigation process improved?
What would you investigate differently if you could start over?
Communication and Impact:
How effectively can you explain statistical concepts to others?
What impact do you hope your data projects will have on your community?
How might you continue using data for positive change?
Standards Mastery:
Are students demonstrating mastery of MCA standards through authentic applications?
Which students need additional support with specific statistical concepts?
How effectively do assessments measure both mathematical understanding and application skills?
Inquiry Effectiveness:
Which activities generated the most authentic engagement and deep learning?
How well did community connections enhance mathematical learning?
What adjustments would improve the inquiry process?
Differentiation and Support:
How effectively did the unit meet needs of diverse learners?
Which scaffolding strategies were most successful?
What additional supports are needed for struggling learners?
Immediate Goals (End of Grade 7):
Solid foundation in measures of central tendency for Grade 8 advanced statistics
Comfort with probability concepts for Grade 8 compound probability
Proficiency with data visualization technology
Confidence in mathematical communication
Long-term Goals (High School Preparation):
Statistical reasoning skills for advanced data analysis
Research and investigation capabilities for independent projects
Technology fluency for sophisticated data tools
Mathematical advocacy skills for civic participation
Primary Standards Addressed:
7.4.1.1: Design simple experiments and collect data; determine mean, median, and range; use to draw conclusions and make predictions
7.4.1.2: Describe impact of inserting/deleting data points on mean and median; use spreadsheets
7.4.2.1: Use proportional reasoning for circle graphs and histograms; choose appropriate displays; use technology
7.4.3.2: Calculate probability as fraction of sample space or area; express as percents, decimals, fractions
7.4.3.3: Use proportional reasoning to predict relative frequencies based on probabilities
Supporting Standards:
7.1.2.3: Solve problems involving ratios and proportional reasoning
7.2.2.1: Represent proportional relationships with tables, graphs, equations
Flexible Timeline:
Quick Implementation: 6 weeks minimum
Standard Implementation: 8-10 weeks recommended
Extended Implementation: 10-12 weeks with full community integration
Technology Requirements:
Essential: Google Sheets access, basic internet connectivity
Preferred: 1:1 device ratio, presentation equipment
Alternative: Shared devices with extended lab time
Community Partnership Development:
Start outreach 1 month before unit begins
Focus on authentic audience opportunities
Maintain ongoing relationships for future classes
Assessment Balance:
40% Formative assessment (daily/weekly checks)
35% Applied projects (data investigations)
25% Traditional assessment (calculations, concepts)
For Advanced Learners:
Independent research with complex datasets
Leadership roles in community presentations
Extension into high school statistical concepts
Mentoring partnerships with struggling learners
For Struggling Learners:
Simplified datasets with clear patterns
Additional practice with foundational calculations
Alternative demonstration methods
Extended time and calculator support
For English Language Learners:
Bilingual mathematical vocabulary resources
Visual supports for statistical concepts
Collaborative grouping strategies
Translation support for community presentations
For Students with Learning Differences:
Multiple representation options
Assistive technology for calculations
Flexible assessment formats
Choice in final presentation methods
Basic data collection and organization
Simple probability concepts and sample spaces
Fraction, decimal, and percentage relationships
Basic ratio and proportion understanding
Science: Environmental systems, scientific method, data collection
Social Studies: Community demographics, historical trend analysis
Language Arts: Research skills, persuasive writing with evidence
Arts: Design principles, visual communication, cultural perspectives
Advanced probability and compound events
Sampling techniques and population inference
Linear relationships and correlation
Advanced statistical measures and distributions
Environmental Science: Climate monitoring, conservation data
Public Health: Community health statistics, epidemiology basics
Civic Engagement: Evidence-based policy discussions, democratic participation
Technology: Data privacy, algorithmic decision-making, digital citizenship
Unit Evaluation Date: ________________
Student Teacher Feedback: ________________
Changes for Next Time: ________________