Age Group: 13-14 years (Grade 8)
Duration: 6-8 weeks (30-40 activity modules)
Teacher(s): Cross-curricular STEAM team
School: [School Name]
Title: Predicting Patterns: Using Data to Understand and Shape Our World
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.
Mathematical models created from data relationships help us make predictions about complex systems, enabling evidence-based decisions that shape our communities and environment.
Causation: How do relationships between variables help us understand cause and effect in complex systems?
Connection: How are mathematical models connected to scientific understanding and social decision-making?
Responsibility: How do data-driven predictions guide responsible choices about environmental and social issues?
Correlation, Prediction, Model, Trend, Relationship, Evidence, System
Mathematical relationships: Understanding scatterplots, lines of best fit, and making predictions from bivariate data
Scientific modeling: How scientists like Dr. Stephanie Evergreen and data artists like W.E.B. Du Bois use visualization to reveal patterns and advocate for change
Environmental applications: Using data relationships to understand climate patterns, biodiversity, and human impact on ecosystems
Social justice through data: How contemporary artists like Ramiro Gómez and David McCandless make invisible social patterns visible through mathematical analysis
Inquirers: Students formulate questions about relationships between variables and design investigations
Thinkers: Students analyze complex data relationships and evaluate the reasonableness of predictions
Communicators: Students explain mathematical relationships and their implications to diverse audiences
Risk-takers: Students make predictions and test them against real-world outcomes
Principled: Students use data responsibly to understand social and environmental justice issues
Caring: Students apply mathematical understanding to address community and global challenges
Formative Assessments:
Daily scatterplot analysis with peer feedback on interpretation accuracy
Digital portfolio documenting progression from basic correlation to complex prediction
Weekly reflection journals connecting mathematical learning to real-world applications
Collaborative analysis of authentic datasets with mathematical reasoning documentation
Summative Assessments:
Week 3: Data Pioneer Research & Mathematical Modeling Project (historical investigation with statistical analysis)
Week 5: Environmental Prediction Challenge (scientific dataset analysis with prediction assessment)
Week 7: Community Data Action Project (authentic local data analysis with presentation to stakeholders)
Mathematical growth tracking through portfolio evidence and self-assessment rubrics
Cross-curricular connections documented through inquiry journal entries
Peer feedback protocols for prediction accuracy and mathematical reasoning
Community impact assessment through stakeholder feedback and project outcomes
WEEK 1: Foundations - From Grade 7 to Scatterplot Thinking
Activity 1.1: Data Relationship Detective (2-3 days) Guiding Question: How do two variables work together to tell a story about our world?
Learning Objectives:
Identify relationships between two quantitative variables in familiar contexts
Distinguish between independent and dependent variables
Build on Grade 7 mean, median, range skills to understand bivariate data
Implementation Options:
Quick Implementation (2 days): Basic two-variable identification with simple real-world examples
Standard Implementation (2-3 days): Include comprehensive variable analysis and prediction reasoning
Extended Implementation (3+ days): Add complex datasets, community data collection, and peer teaching components
Core Activities:
Variable Hunt: Using 7th grade NWEA data insights, students explore relationships between study time and test scores, height and shoe size, temperature and ice cream sales
Prediction Before Analysis: Students make intuitive predictions about relationships before mathematical analysis
Grade 7 Skills Bridge: Calculate mean, median, range for both variables to understand individual patterns before examining relationships
Materials: Real-world datasets, prediction recording sheets, calculators, graph paper Assessment Evidence: Accurate variable identification with justified predictions and statistical calculations Standards Alignment: 8.4.1.1 foundation (two-variable thinking), building on Grade 7 statistical measures Differentiation: Students with strong Number & Operation skills (noted strength in NWEA data) lead calculations while others focus on pattern recognition
Activity 1.2: Introduction to Scatterplots (2-4 days) Guiding Question: How can we visualize relationships between two variables to see patterns our eyes might miss?
Learning Objectives:
Create scatterplots by hand and using technology
Identify positive, negative, and no association patterns
Use appropriate titles, labels, and scales for professional presentation
Implementation Options:
Quick Implementation (2 days): Basic scatterplot creation with simple pattern identification
Standard Implementation (3 days): Include technology integration and association strength analysis
Extended Implementation (4 days): Add artistic scatterplot designs and peer review processes
Core Activities:
Hand-Drawn Scatterplots: Create initial scatterplots on graph paper; emphasize careful scaling and labeling for mathematical accuracy
Google Sheets Scatterplot Mastery: Transition to digital creation; compare hand-drawn accuracy with technology efficiency
Pattern Gallery Walk: Examine multiple scatterplots to identify association types; develop vocabulary for mathematical communication
Materials: Graph paper, rulers, Chromebooks, authentic datasets, pattern identification worksheets Assessment Evidence: Accurate scatterplots with proper scaling and clear association identification Standards Alignment: 8.4.1.1 (Collect, display and interpret data using scatterplots) Technology Integration: Building on student experience with basic spreadsheet functions from Grade 7
Activity 1.3: W.E.B. Du Bois - Data for Social Justice (3-4 days) Guiding Question: How can mathematical visualization become a powerful tool for social change and justice?
Learning Objectives:
Understand W.E.B. Du Bois's revolutionary use of data visualization for social advocacy
Analyze historical scatterplot-style visualizations for mathematical accuracy and social impact
Connect mathematical skills to social responsibility and justice work
Implementation Options:
Quick Implementation (2-3 days): Basic historical analysis with simple mathematical connections
Standard Implementation (3-4 days): Include comprehensive social justice analysis and modern applications
Extended Implementation (4+ days): Add contemporary social justice data projects and community presentations
Core Activities:
Historical Investigation: Examine Du Bois's 1900 Paris Exposition visualizations; analyze mathematical techniques and social objectives
Data Justice Analysis: Research modern examples of data being used to address social inequality; identify mathematical relationships
Personal Reflection Project: Students create scatterplots examining inequalities in their own community (school resources, demographic patterns, access to opportunities)
Materials: Du Bois visualization reproductions, social justice datasets, research materials, reflection journals Assessment Evidence: Historical analysis with mathematical understanding and personal application to social justice issues Standards Alignment: Mathematical analysis within social studies context Data Pioneer Connection: Students understand data visualization as tool for positive social change
WEEK 2: Lines of Best Fit & Dr. Stephanie Evergreen's Accessibility Approach
Activity 2.1: Estimating Lines of Best Fit - The Spaghetti Method (2-3 days) Guiding Question: How can we find the line that best represents the overall trend in our data?
Learning Objectives:
Visually estimate lines of best fit using physical manipulation
Understand that the "best" line minimizes distance to all points
Practice mathematical reasoning about line placement decisions
Implementation Options:
Quick Implementation (2 days): Basic spaghetti line estimation with simple datasets
Standard Implementation (2-3 days): Include multiple datasets and peer comparison of line placement
Extended Implementation (3+ days): Add mathematical reasoning documentation and accuracy assessment
Core Activities:
Spaghetti Line Investigation: Using uncooked spaghetti strands, students physically place lines on printed scatterplots to estimate best fit
Peer Line Comparison: Teams compare their line placements; justify choices using mathematical reasoning about point distribution
"Bad Line" Analysis: Teacher introduces obviously poor line placement; students explain why their lines are mathematically superior
Materials: Printed scatterplots, uncooked spaghetti, tape, poster paper, mathematical reasoning worksheets Assessment Evidence: Reasonable line estimation with mathematical justification for placement decisions Standards Alignment: 8.4.1.1 (Use the shape of the scatterplot to informally estimate a line of best fit) Physical Learning: Hands-on approach supports kinesthetic learners while building conceptual understanding
Activity 2.2: Dr. Stephanie Evergreen - Accessible Data Design (2-4 days) Guiding Question: How can we make our mathematical work accessible and meaningful to all people?
Learning Objectives:
Understand Dr. Evergreen's principles of accessible data visualization
Apply colorblind-friendly design and clear labeling to mathematical work
Connect mathematical accuracy with inclusive communication practices
Implementation Options:
Quick Implementation (2 days): Basic accessibility principles with color-blind friendly design
Standard Implementation (3 days): Include comprehensive design evaluation and improvement processes
Extended Implementation (4 days): Add stakeholder feedback and community accessibility assessment
Core Activities:
Accessibility Audit: Examine existing scatterplots and visualizations for accessibility barriers; identify improvements using Evergreen's frameworks
Inclusive Design Project: Redesign previous scatterplots using colorblind-friendly palettes, clear fonts, and descriptive labels
Community Testing: Share accessible visualizations with community members (parents, younger students) to assess clarity and understanding
Materials: Accessibility design guides, color-blind simulation tools, community feedback forms, design improvement worksheets Assessment Evidence: Improved visualizations with documented accessibility enhancements and community feedback Standards Alignment: Professional data presentation with appropriate titles, labels, and visual design Contemporary Pioneer Connection: Students learn that modern data science prioritizes inclusion and accessibility
Activity 2.3: Equations from Visual Lines (3-4 days) Guiding Question: How can we translate our visual understanding into mathematical equations?
Learning Objectives:
Determine equations for visually estimated lines of best fit
Use slope-intercept form to represent line relationships
Connect algebraic skills to data analysis applications
Implementation Options:
Quick Implementation (2-3 days): Basic equation writing with simple slope calculations
Standard Implementation (3-4 days): Include comprehensive slope and y-intercept analysis
Extended Implementation (4+ days): Add complex equation applications and prediction accuracy assessment
Core Activities:
Slope Detective Work: Using two points on estimated lines, calculate slope using rise-over-run method; connect to algebraic understanding
Y-Intercept Analysis: Identify y-intercepts from scatterplots; discuss real-world meaning in context of original variables
Equation Verification: Write equations in y = mx + b form; test accuracy by calculating predicted values for known data points
Materials: Scatterplots with estimated lines, slope calculation worksheets, graphing calculators, equation verification templates Assessment Evidence: Accurate equations with correct slope and y-intercept identification and real-world interpretation Standards Alignment: 8.4.1.1 (determine an equation for the line of best fit) Algebra Integration: Building on Grade 8 algebra skills within data analysis context
WEEK 3: Predictions & Rate of Change Applications
Activity 3.1: Making Predictions with Mathematical Models (3-5 days) Guiding Question: How can mathematical models help us predict unknown values and future outcomes?
Learning Objectives:
Use lines of best fit to make predictions about values not in original datasets
Distinguish between interpolation (within data range) and extrapolation (beyond data range)
Evaluate prediction accuracy using mathematical reasoning
Implementation Options:
Quick Implementation (3 days): Basic prediction practice with accuracy checking
Standard Implementation (4 days): Include interpolation vs. extrapolation analysis and prediction confidence assessment
Extended Implementation (5 days): Add complex prediction scenarios and statistical accuracy measurement
Core Activities:
Prediction Practice: Use established lines of best fit to predict values for new x-coordinates; calculate using equations and verify graphically
Interpolation vs. Extrapolation Analysis: Compare prediction confidence for values within data range versus beyond data range
Prediction Tournament: Teams make competing predictions for same scenarios; check accuracy against actual outcomes when possible
Materials: Prediction worksheets, graphing calculators, prediction accuracy tracking sheets, tournament scorecards Assessment Evidence: Accurate predictions with understanding of interpolation vs. extrapolation limitations Standards Alignment: 8.4.1.2 (make predictions about values not in the original data set) Competitive Learning: Tournament format engages students while building prediction skills
Activity 3.2: Rate of Change Analysis (2-4 days) Guiding Question: How do lines of best fit help us understand how quickly one variable changes relative to another?
Learning Objectives:
Interpret slope as approximate rate of change in real-world contexts
Make statements about how variables relate to each other quantitatively
Connect mathematical rate of change to scientific and social applications
Implementation Options:
Quick Implementation (2 days): Basic rate of change interpretation with simple contexts
Standard Implementation (3 days): Include complex rate analysis and real-world application projects
Extended Implementation (4 days): Add comparative rate analysis and predictive modeling applications
Core Activities:
Rate Detective: Analyze slopes from various scatterplots; interpret meaning in original context (example: "For every additional hour of study, test scores increase by approximately 3 points")
Comparative Rates: Compare rates of change across different datasets; identify which relationships show stronger or weaker rates
Real-World Rate Applications: Apply rate analysis to environmental data (temperature change over time, species population changes, etc.)
Materials: Multiple datasets with calculated lines, rate interpretation worksheets, environmental data collections, comparative analysis charts Assessment Evidence: Accurate rate interpretation with meaningful real-world application and comparative analysis Standards Alignment: 8.4.1.2 (make statements about approximate rate of change) Environmental Science Integration: Connecting mathematical rates to climate and ecological understanding
Activity 3.3: Major Assessment - Data Pioneer Mathematical Modeling (4-6 days) Guiding Question: How can we combine historical research with mathematical modeling to demonstrate our understanding of data relationships?
Learning Objectives:
Research additional data visualization pioneers and their mathematical techniques
Apply scatterplot and line of best fit skills to historical or contemporary datasets
Create presentations combining historical analysis with mathematical demonstration
Implementation Options:
Quick Implementation (4 days): Basic research project with simple mathematical analysis
Standard Implementation (5 days): Include comprehensive mathematical modeling and peer review
Extended Implementation (6+ days): Add community presentation and expert feedback components
Core Activities:
Pioneer Research: Choose from Florence Nightingale, John Snow, Charles Joseph Minard, or contemporary figures; research their data visualization methods
Mathematical Recreation: Find or create datasets similar to chosen pioneer's work; apply Grade 8 scatterplot and line of best fit techniques
Historical-Mathematical Presentation: Create presentations showing both historical significance and modern mathematical analysis
Materials: Research databases, historical datasets, presentation software, peer review rubrics, mathematical analysis templates Assessment Evidence: Comprehensive presentation demonstrating both historical understanding and mathematical mastery Standards Alignment: Integrated application of 8.4.1.1 and 8.4.1.2 within historical research context Major Assessment: Culminating demonstration of foundational scatterplot and line of best fit mastery
WEEK 4: Environmental Data & Scientific Applications
Activity 4.1: Climate Data Scatterplot Analysis (3-4 days) Guiding Question: How can scatterplots help us understand complex climate relationships and make environmental predictions?
Learning Objectives:
Analyze relationships between climate variables using authentic environmental datasets
Create lines of best fit for climate data and interpret environmental implications
Make climate predictions and assess their reasonableness in environmental context
Implementation Options:
Quick Implementation (3 days): Basic climate data analysis with simple relationships
Standard Implementation (3-4 days): Include comprehensive climate modeling and prediction assessment
Extended Implementation (4+ days): Add long-term climate projection analysis and policy implications
Core Activities:
Climate Variable Investigation: Analyze relationships between temperature and CO2 levels, precipitation and plant growth, ocean temperature and ice cover
Environmental Prediction Modeling: Create lines of best fit for climate data; make predictions about future environmental conditions
Climate Communication: Prepare climate science summaries using mathematical evidence; practice communicating uncertainty and confidence levels
Materials: Climate datasets from NOAA/NASA, environmental analysis worksheets, prediction assessment rubrics, climate communication templates Assessment Evidence: Accurate environmental data analysis with reasonable predictions and effective science communication Standards Alignment: 8.4.1.1, 8.4.1.2, 8.4.1.3 applied to environmental science contexts Environmental Science Integration: Authentic climate data connects mathematics to urgent environmental understanding
Activity 4.2: Biodiversity & Population Studies (3-4 days) Guiding Question: How do mathematical relationships help scientists understand and protect ecosystems?
Learning Objectives:
Analyze population data using scatterplots and trend analysis
Understand relationships between environmental factors and species health
Apply mathematical modeling to conservation decision-making
Implementation Options:
Quick Implementation (3 days): Basic population analysis with simple conservation connections
Standard Implementation (3-4 days): Include comprehensive ecosystem analysis and conservation modeling
Extended Implementation (4+ days): Add field data collection and local conservation project development
Core Activities:
Population Trend Analysis: Examine data on endangered species populations, invasive species growth, habitat loss rates; create scatterplots and trend lines
Ecosystem Relationship Modeling: Analyze relationships between pollution levels and species health, temperature changes and migration patterns
Conservation Prediction Project: Use mathematical models to predict conservation intervention outcomes; assess reasonableness of different conservation strategies
Materials: Biodiversity datasets, population analysis worksheets, conservation case studies, ecosystem modeling software Assessment Evidence: Mathematical analysis supporting conservation conclusions with scientific reasoning Standards Alignment: Mathematical modeling applied to biological systems and conservation science Conservation Biology Integration: Students experience mathematics as tool for environmental protection
Activity 4.3: Technology Integration - Advanced Google Sheets (2-3 days) Guiding Question: How can technology tools enhance our mathematical analysis and professional presentation skills?
Learning Objectives:
Use advanced Google Sheets functions for scatterplot creation and line fitting
Create professional-quality data visualizations for stakeholder communication
Integrate multiple datasets for comprehensive analysis
Implementation Options:
Quick Implementation (2 days): Basic Google Sheets mastery with simple line fitting
Standard Implementation (2-3 days): Include advanced functions and professional formatting
Extended Implementation (3+ days): Add multi-dataset analysis and collaborative sharing features
Core Activities:
Advanced Function Mastery: Learn SLOPE(), INTERCEPT(), and RSQ() functions; compare with hand-calculated lines for accuracy verification
Professional Visualization Creation: Format scatterplots with consistent design principles; apply Dr. Evergreen's accessibility guidelines
Collaborative Analysis Projects: Share datasets and analysis with classmates; practice peer review and collaborative improvement
Materials: Advanced Google Sheets tutorials, professional datasets, formatting guides, collaboration protocols Assessment Evidence: Professional-quality visualizations with accurate mathematical analysis and collaborative documentation Standards Alignment: Technology applications for scatterplot creation and professional data presentation Digital Literacy Integration: Real-world technology skills applicable beyond mathematics classroom
WEEK 5: Ramiro Gómez & Contemporary Data Art
Activity 5.1: Ramiro Gómez - Making the Invisible Visible (3-4 days) Guiding Question: How can mathematical analysis reveal hidden patterns in our society and make invisible people visible?
Learning Objectives:
Understand Ramiro Gómez's approach to using data art for social awareness
Analyze labor and demographic data to understand social patterns
Connect mathematical analysis to social justice and community awareness
Implementation Options:
Quick Implementation (2-3 days): Basic social data analysis with simple pattern identification
Standard Implementation (3-4 days): Include comprehensive social justice analysis and community application
Extended Implementation (4+ days): Add original social justice data art projects and community presentation
Core Activities:
Invisible Labor Analysis: Examine data on domestic workers, agricultural laborers, service industry employees; create scatterplots showing relationships between work conditions and demographics
Social Pattern Investigation: Analyze relationships between education access and income, transportation access and employment, housing costs and family size
Community Visibility Project: Identify overlooked groups in local community; collect and analyze data to make their contributions visible through mathematical presentation
Materials: Labor statistics, demographic datasets, social justice research materials, community data collection tools Assessment Evidence: Mathematical analysis revealing social patterns with thoughtful interpretation and community application Standards Alignment: Mathematical analysis applied to social justice and community awareness Contemporary Artist Connection: Students understand mathematics as tool for social awareness and justice advocacy
Activity 5.2: David McCandless - Beautiful Data Stories (2-4 days) Guiding Question: How can we combine mathematical accuracy with artistic beauty to create compelling data narratives?
Learning Objectives:
Analyze David McCandless's approach to combining aesthetics with mathematical accuracy
Create visually appealing scatterplots that maintain mathematical integrity
Understand design principles for effective data communication
Implementation Options:
Quick Implementation (2 days): Basic design improvement with mathematical accuracy maintenance
Standard Implementation (3 days): Include comprehensive design analysis and artistic data creation
Extended Implementation (4 days): Add collaborative design projects and public presentation preparation
Core Activities:
Design Analysis: Examine McCandless's visualizations for design principles; identify how visual appeal enhances rather than detracts from mathematical accuracy
Beautiful Math Project: Redesign previous scatterplots using color, typography, and layout principles while maintaining all mathematical requirements
Data Story Development: Choose compelling datasets; create visually stunning presentations that tell clear stories through mathematical relationships
Materials: McCandless visualization examples, design software, artistic supplies, storytelling templates Assessment Evidence: Visually appealing presentations with maintained mathematical accuracy and clear narrative structure Standards Alignment: Professional data presentation with artistic enhancement and mathematical integrity Contemporary Artist Connection: Students understand data visualization as combining analytical and creative skills
Activity 5.3: Reasonableness Assessment Deep Dive (3-4 days) Guiding Question: How can we evaluate whether our mathematical predictions make sense in real-world contexts?
Learning Objectives:
Assess the reasonableness of predictions by interpreting them in original contexts
Identify when mathematical models may not apply or extend appropriately
Develop critical thinking skills for evaluating mathematical conclusions
Implementation Options:
Quick Implementation (2-3 days): Basic reasonableness checking with simple scenarios
Standard Implementation (3-4 days): Include comprehensive critical analysis and model limitation assessment
Extended Implementation (4+ days): Add complex scenario analysis and expert consultation
Core Activities:
Prediction Reality Check: Examine extreme predictions generated from lines of best fit; evaluate whether they make sense in real-world contexts
Model Limitation Analysis: Identify situations where linear models break down; understand when relationships might be nonlinear or change over time
Critical Thinking Scenarios: Analyze examples like "predicting 200% test scores" or "1000 female Senators"; develop frameworks for evaluating mathematical reasonableness
Materials: Extreme prediction scenarios, critical thinking worksheets, real-world limitation examples, evaluation rubrics Assessment Evidence: Thoughtful evaluation of prediction reasonableness with sophisticated understanding of model limitations Standards Alignment: 8.4.1.3 (Assess the reasonableness of predictions using scatterplots) Critical Thinking Development: Students develop skills for evaluating mathematical conclusions in context
WEEK 6: Complex Applications & Community Connections
Activity 6.1: Multi-Variable Environmental Analysis (3-5 days) Guiding Question: How can we analyze complex environmental systems using multiple scatterplot relationships?
Learning Objectives:
Create multiple scatterplots to understand complex environmental relationships
Compare rates of change across different environmental variables
Synthesize multiple mathematical analyses into comprehensive environmental understanding
Implementation Options:
Quick Implementation (3 days): Basic multi-variable analysis with simple environmental relationships
Standard Implementation (4 days): Include comprehensive environmental system analysis and policy implications
Extended Implementation (5 days): Add original environmental research and community presentation
Core Activities:
Environmental System Mapping: Analyze relationships between air quality and health outcomes, water quality and ecosystem health, land use and biodiversity
Comparative Rate Analysis: Compare rates of environmental change across different regions, time periods, or intervention strategies
Environmental Policy Recommendations: Use mathematical analysis to support evidence-based recommendations for environmental policy and community action
Materials: Comprehensive environmental datasets, multi-variable analysis worksheets, policy research materials, presentation software Assessment Evidence: Sophisticated environmental analysis with multiple mathematical relationships and policy recommendations Standards Alignment: Advanced application of all Grade 8 scatterplot and line of best fit standards Environmental Policy Integration: Students experience mathematics as tool for environmental decision-making
Activity 6.2: Community Data Action Project (4-6 days) Guiding Question: How can we use mathematical analysis to address real issues in our local community?
Learning Objectives:
Identify community issues amenable to data analysis and mathematical modeling
Collect original data through surveys and observational studies
Present mathematical findings to authentic community stakeholders
Implementation Options:
Quick Implementation (4 days): Basic community analysis with simple presentation
Standard Implementation (5 days): Include comprehensive community research and stakeholder presentation
Extended Implementation (6+ days): Add ongoing community partnership and follow-up analysis
Core Activities:
Community Issue Identification: Research local issues that could benefit from data analysis (transportation patterns, energy use, food access, recreational opportunities)
Original Data Collection: Design and implement data collection strategies; create scatterplots and mathematical models from original community data
Stakeholder Presentation: Present findings to community leaders, school board members, or local organizations; receive feedback and discuss potential applications
Materials: Community research resources, data collection tools, presentation equipment, stakeholder contact information Assessment Evidence: Professional presentation to community stakeholders with accurate mathematical analysis and actionable recommendations Standards Alignment: Authentic application of complete Grade 8 data analysis and mathematical modeling standards Civic Engagement: Students experience mathematics as tool for community participation and positive change
WEEK 7: Advanced Applications & Assessment
Activity 7.1: Complex Dataset Challenge (3-4 days) Guiding Question: How can we apply our mathematical skills to analyze sophisticated, real-world datasets that professionals use?
Learning Objectives:
Navigate complex datasets with multiple variables and large sample sizes
Apply Grade 8 scatterplot and prediction skills to professional-quality data
Demonstrate mastery through independent mathematical analysis
Implementation Options:
Quick Implementation (3 days): Structured analysis of complex datasets with guided support
Standard Implementation (3-4 days): Include independent analysis and peer review processes
Extended Implementation (4+ days): Add expert consultation and advanced analysis techniques
Core Activities:
Professional Dataset Exploration: Work with datasets from Kaggle, government databases, or research institutions; identify meaningful relationships within complex data
Independent Mathematical Analysis: Apply complete scatterplot, line of best fit, and prediction process without step-by-step guidance
Peer Review and Improvement: Exchange analyses with classmates; provide mathematical feedback and suggestions for improvement
Materials: Complex authentic datasets, independent analysis templates, peer review protocols, expert contact information Assessment Evidence: Independent mathematical analysis of complex data with accurate scatterplots, predictions, and reasonableness assessment Standards Alignment: Comprehensive demonstration of 8.4.1.1, 8.4.1.2, and 8.4.1.3 mastery Professional Preparation: Students experience analysis similar to real-world data science applications
Activity 7.2: Teaching Others - Mathematical Communication (3-4 days) Guiding Question: How effectively can we teach others about mathematical relationships and help them understand scatterplot analysis?
Learning Objectives:
Explain mathematical concepts clearly to non-expert audiences
Create educational materials that teach scatterplot and prediction skills
Demonstrate mastery through teaching and supporting others' learning
Implementation Options:
Quick Implementation (3 days): Basic peer teaching with simple explanation practice
Standard Implementation (3-4 days): Include comprehensive teaching material development and community education
Extended Implementation (4+ days): Add formal tutoring programs and educational impact assessment
Core Activities:
Teaching Material Development: Create tutorials, worksheets, and explanation guides for teaching scatterplot analysis to younger students or community members
Peer Teaching Practice: Work with students from lower grades or community members to teach mathematical concepts; practice explaining mathematical reasoning clearly
Educational Impact Assessment: Gather feedback on teaching effectiveness; reflect on how teaching others strengthens personal mathematical understanding
Materials: Teaching material templates, educational resources, community partnership contacts, feedback collection tools Assessment Evidence: Effective teaching demonstrations with clear mathematical explanations and positive learning outcomes for others Standards Alignment: Mathematical communication and application in educational contexts Service Learning Integration: Students contribute to community mathematical literacy while demonstrating mastery
Activity 7.3: Final Assessment - Comprehensive Data Analysis Portfolio (4-5 days) Guiding Question: How can we demonstrate the full scope of our mathematical growth and application abilities?
Learning Objectives:
Compile comprehensive portfolio documenting mathematical learning progression
Reflect on connections between mathematical skills and real-world applications
Set goals for continued mathematical development in high school
Implementation Options:
Quick Implementation (3-4 days): Basic portfolio compilation with simple reflection
Standard Implementation (4-5 days): Include comprehensive reflection and goal-setting processes
Extended Implementation (5+ days): Add expert review, family presentation, and transition planning
Core Activities:
Portfolio Compilation: Organize best examples of scatterplot analysis, prediction projects, and community applications; document mathematical growth throughout unit
Comprehensive Reflection: Write detailed reflection on mathematical learning, cross-curricular connections, and personal growth as mathematical thinker
High School Preparation: Preview Grade 9-11 statistics and data analysis concepts; set goals for continued mathematical development and application
Materials: Portfolio organization tools, reflection prompts, high school preview materials, goal-setting templates Assessment Evidence: Comprehensive portfolio with thoughtful reflection demonstrating mathematical growth and future planning Standards Alignment: Complete demonstration of Grade 8 data analysis mastery with preparation for advanced concepts Transition Preparation: Students prepared for high school statistics and continued mathematical development
Chromebooks/tablets (1:1 ratio for digital analysis and visualization)
Google Sheets with advanced function capabilities
Graphing calculators for verification and complex calculations
Internet connectivity for authentic dataset access and research
Presentation equipment for community and stakeholder presentations
Mathematical tools: graph paper, rulers, colored pencils, chart paper, protractors
Hands-on materials: uncooked spaghetti for line estimation, poster boards for presentations
Art supplies: materials for data visualization design and artistic enhancement projects
Data collection tools: clipboards, surveys, measurement tools for original community research
Curated datasets: age-appropriate environmental, social, and scientific datasets from Kaggle, government sources, research institutions
Historical materials: reproductions of data pioneer visualizations and biographical resources
Contemporary examples: printed works and digital access to Ramiro Gómez, David McCandless, Dr. Stephanie Evergreen
Professional databases: access to climate, demographic, and scientific data sources
Environmental scientists for dataset validation and career connections
Community leaders for authentic stakeholder engagement during action projects
Local artists for consultation on data visualization aesthetics and social justice applications
High school statistics teachers for transition planning and advanced concept previews
Standard 8.4.1.1: Collect, Display, and Interpret Scatterplots
Exceeds: Creates sophisticated scatterplots with optimal scaling and formatting; expertly estimates lines of best fit; determines accurate equations independently
Meets: Creates accurate scatterplots with appropriate titles, labels, and units; visually estimates reasonable lines of best fit; determines equations with support
Approaching: Creates basic scatterplots with guidance; estimates lines with some accuracy; attempts equation determination with significant support
Beginning: Attempts scatterplot creation with extensive support; limited understanding of line estimation and equation concepts
Standard 8.4.1.2: Predictions and Rate of Change
Exceeds: Makes sophisticated predictions for interpolation and extrapolation; expertly interprets rate of change in complex contexts; compares rates across datasets
Meets: Makes accurate predictions within and beyond data range; interprets slope as rate of change in original context; understands prediction uncertainty
Approaching: Makes basic predictions with support; beginning understanding of rate of change interpretation; limited grasp of prediction limitations
Beginning: Attempts predictions with extensive guidance; minimal understanding of rate of change concepts and prediction applications
Standard 8.4.1.3: Reasonableness Assessment
Exceeds: Demonstrates sophisticated critical thinking about prediction limits; expertly evaluates model appropriateness; identifies complex limitation scenarios
Meets: Assesses prediction reasonableness in original context; understands when extrapolation may be inappropriate; identifies obvious limitation examples
Approaching: Basic assessment of prediction reasonableness with guidance; limited understanding of model limitations and context interpretation
Beginning: Minimal ability to assess prediction reasonableness; requires extensive support for context interpretation and limitation recognition
Understanding:
Students demonstrate deep comprehension of bivariate relationships through mathematical modeling
Students explain scatterplot analysis and prediction processes using precise mathematical vocabulary
Students connect mathematical relationships to scientific, environmental, and social applications
Inquiry:
Students formulate meaningful questions about variable relationships in authentic contexts
Students design appropriate data collection methods for community and environmental investigations
Students pursue independent mathematical analysis extending beyond lesson requirements
Communication:
Students explain mathematical reasoning clearly to diverse audiences including community stakeholders
Students adapt mathematical presentations for different contexts while maintaining accuracy
Students integrate mathematical analysis with narrative storytelling and visual design
Reflection:
Students analyze their mathematical growth from basic statistics to complex modeling
Students identify connections between mathematical skills and real-world problem-solving
Students set goals for continued mathematical development and application
Independent research projects investigating additional data pioneers or contemporary statistical applications
Leadership roles in peer teaching and community presentation facilitation
Advanced statistical concepts including correlation coefficients and multiple regression as enrichment
Mentorship opportunities working with high school statistics students or community professionals
Manipulative support for slope calculation and line estimation with gradual release to abstract thinking
Simplified datasets with clear relationships and fewer variables for initial practice
Technology assistance for complex calculations while maintaining conceptual understanding
Multiple representation options allowing students to demonstrate understanding through various formats
Bilingual mathematical vocabulary resources with visual representations and cultural connections
Translation support for community presentations while maintaining mathematical accuracy and independence
Cultural mathematics connections highlighting data traditions from students' home countries
Collaborative grouping with strategic language support while ensuring individual accountability
Extended time for complex analysis with alternative assessment formats maintaining rigor
Assistive technology for data entry, visualization creation, and calculation support
Multiple intelligence approaches incorporating visual, auditory, and kinesthetic learning preferences
Choice in demonstration format maintaining mathematical standards while accommodating individual strengths
Family Data Night during Week 4 featuring student presentations of environmental analysis projects
Community action presentations involving families in local data collection and stakeholder engagement
Cultural data sharing inviting families to share traditional approaches to pattern recognition and prediction
High school transition planning helping families understand Grade 9-11 mathematics preparation requirements
Statistical vocabulary development materials for continued practice and family engagement
Data collection projects for ongoing mathematical exploration connecting home and school learning
Technology tutorials for parents to support home use of Google Sheets and data analysis tools
High school preparation guides with specific information about advanced statistics and data science pathways
Grade 8 Standards mastery with specific attention to scatterplot analysis, prediction, and reasonableness assessment
IB PYP methodology expertise focusing on inquiry-based learning and authentic assessment in mathematics
Technology integration skills including advanced Google Sheets functions and data visualization tools
Community partnership development for authentic stakeholder engagement and real-world application
Cross-curricular coordination ensuring authentic integration with environmental science, social studies, and language arts
Assessment calibration for consistent evaluation across complex, multi-week projects and community presentations
Community engagement planning establishing sustainable partnerships with local organizations and professionals
Student transition support coordinating with high school mathematics teachers for smooth Grade 9 preparation
Months 1-2: Curate authentic datasets, establish community partnerships, prepare physical and digital materials
Month 3: Pilot technology systems, train community partners, develop assessment rubrics and feedback protocols
Months 4-5: Implement unit with ongoing documentation, student work analysis, and community feedback collection
Month 6: Comprehensive evaluation, community impact assessment, and material refinement for future implementation
Students completing this unit enter high school statistics with strong foundations in:
Bivariate data analysis enabling focus on correlation coefficients and formal regression techniques
Mathematical modeling supporting advanced statistical inference and hypothesis testing
Critical thinking about data facilitating evaluation of statistical claims and research methodology
Technology proficiency enabling independent use of statistical software and advanced analysis tools
Communication skills supporting collaborative research and professional presentation development
Advanced environmental science using statistical modeling for climate change research and environmental policy analysis
Social justice projects applying data analysis to ongoing community advocacy and systemic change initiatives
Economics and business analyzing market trends, consumer behavior, and economic indicators through mathematical modeling
Health sciences investigating public health trends, epidemiological patterns, and intervention effectiveness
Environmental advocacy through ongoing student-generated analysis supporting local environmental protection efforts
Social policy influence using student research to inform community decision-making and resource allocation
Educational outreach with students serving as mathematics tutors and data literacy advocates in community
Civic engagement preparation developing skills for evidence-based participation in democratic processes and community leadership
Week 1: Foundation Assessment
Daily formative assessment through scatterplot creation accuracy and mathematical reasoning documentation
Peer feedback protocols for variable identification and prediction justification
Self-assessment using Grade 8 vocabulary and conceptual understanding checklists
Week 2: Line of Best Fit Mastery
Spaghetti line estimation assessment with mathematical justification requirements
Technology integration assessment through Google Sheets proficiency and professional visualization creation
Collaborative assessment of Dr. Evergreen's accessibility principles applied to mathematical work
Week 3: Prediction and Historical Analysis (Major Assessment)
Data Pioneer Research & Mathematical Modeling presentation combining historical research with Grade 8 statistical analysis
Prediction accuracy assessment through interpolation and extrapolation challenges
Mathematical communication assessment through peer teaching and explanation effectiveness
Week 4: Environmental Applications
Scientific dataset analysis requiring sophisticated scatterplot interpretation and environmental prediction
Rate of change analysis assessment in climate and ecological contexts
Technology mastery assessment through advanced Google Sheets functions and collaborative analysis
Week 5: Social Justice and Contemporary Applications
Social justice data analysis combining Ramiro Gómez's advocacy approach with mathematical rigor
Reasonableness assessment mastery through complex prediction evaluation and model limitation analysis
Artistic data visualization balancing David McCandless's aesthetic principles with mathematical accuracy
Week 6-7: Community Engagement and Comprehensive Assessment (Major Assessment)
Community Data Action Project requiring original data collection, analysis, and stakeholder presentation
Independent complex dataset analysis demonstrating complete Grade 8 standards mastery
Comprehensive portfolio compilation with reflection on mathematical growth and future goal setting
Mathematical progression documentation showing development from basic scatterplots to sophisticated prediction and assessment
Cross-curricular integration examples demonstrating connections between mathematical analysis and environmental, social, and artistic applications
Community engagement evidence including stakeholder feedback, presentation documentation, and impact assessment
Reflection and goal-setting documents connecting current mathematical mastery to high school preparation and continued development
Peer collaboration examples showing mathematical communication skills and collaborative analysis capabilities
Week 1-2: Basic scatterplot creation, formatting, and simple trend line insertion
Week 3-4: Advanced functions including SLOPE(), INTERCEPT(), and RSQ() for verification of hand calculations
Week 5-6: Collaborative sharing, commenting, and peer review protocols for mathematical accuracy
Week 7: Professional presentation formatting and stakeholder communication through digital platforms
Data privacy awareness when collecting community data and sharing analysis results
Source verification skills for evaluating authenticity and reliability of online datasets
Ethical data use understanding appropriate applications of mathematical analysis and prediction
Digital communication maintaining professional standards when presenting to community stakeholders online
Peer tech support protocols for collaborative problem-solving and skill sharing
Backup planning for presentation technology and digital tool accessibility
Alternative technology accommodations for students with different device access or technical skill levels
Primary terms: scatterplot, line of best fit, correlation, interpolation, extrapolation, rate of change
Supporting terms: independent variable, dependent variable, positive association, negative association, reasonableness
Advanced terms: bivariate data, linear relationship, trend line, prediction interval, model limitations
Week 1-2: Basic description of relationships and patterns using mathematical vocabulary
Week 3-4: Detailed explanation of mathematical reasoning with justification for mathematical choices
Week 5-6: Adaptation of mathematical explanations for different audiences while maintaining accuracy
Week 7: Professional presentation skills including mathematical precision with accessible communication
Environmental science: ecosystem, biodiversity, climate variables, conservation, sustainability
Social justice: demographics, inequality, representation, advocacy, systemic patterns
Technology: data visualization, digital literacy, collaborative platforms, presentation software
This comprehensive Grade 8 STEAM Data Visualization Unit provides students with essential mathematical foundations in bivariate data analysis while engaging them in authentic, inquiry-based learning that addresses real community and environmental challenges. Building strategically on the Grade 7 strengths demonstrated in student NWEA data—particularly strong number sense and improving data analysis skills—the unit develops sophisticated mathematical modeling capabilities that prepare students for high school statistics and data science.
Through integration of historical data pioneers like W.E.B. Du Bois, contemporary accessibility advocates like Dr. Stephanie Evergreen, and social justice artists like Ramiro Gómez, students experience mathematics as a living discipline that connects to their values, community engagement, and future academic success. The emphasis on environmental applications, community data action projects, and stakeholder presentations ensures that students develop both technical scatterplot and prediction skills and the communication abilities necessary for mathematical citizenship in an increasingly data-driven society.
The unit's flexible activity module structure accommodates diverse implementation needs while maintaining rigorous attention to Minnesota Grade 8 standards and MCA preparation. By combining traditional IB PYP inquiry methodology with specific attention to authentic community engagement and advanced technology integration, this unit serves multiple educational objectives without sacrificing mathematical depth or real-world applicability.
Most importantly, students experience mathematics as a powerful tool for understanding complex relationships, making evidence-based predictions, and contributing meaningfully to environmental protection and social justice efforts. This foundation serves them well not only in their continued mathematical education but in their development as informed, engaged citizens capable of using mathematical reasoning to understand and improve their world. The unit's emphasis on reasonableness assessment and critical evaluation of mathematical models prepares students to navigate an increasingly complex information landscape with mathematical sophistication and ethical responsibility.