Tuva Labs' data sets are accessible for upper elementary through high school classrooms and across many subject levels. There are a large number of cross-curricular applications and Tuva Labs seems to focus on these connections as much as possible; their content library is laid out so that it's searchable across a number of different categories and most link at least two subject areas. You'll want to set aside prep time to explore the data and (probably) create your own activity.
Are your fourth-graders studying colonial America? Link math and history goals using the "Tobacco and Jamestown Economy" data set. Working on natural disasters in MS? Access data sets on hurricanes and earthquakes while also exploring CCSS on statistical variability and distribution. Secondary science teachers will find lots of real-world data related to human impact (energy consumption, greenhouse gases, and so on). For more focused math practice (graphs, linear functions, data analysis) have small groups of students interact with data sets they (pre-)select. Turn to the "Model Shop" collection for sense-making examples of mathematical modeling.
Consider uploading your class' own data sets for further exploration; just don't forget a coordinating worksheet or journal entry. And be sure to check out the instructional support resources and Data Literacy 101 course if students are struggling to interpret the data.Continue reading Show less
Tuva Labs provides access to more than 400 curated data sets -- with coordinating online student activities -- on topics as varied as gender identity and atmospheric CO2. Filters are available for subject, grade range, data set size, and more. The amount of data per set varies from less than 10 cases to thousands and from two to more than 20 attributes. There are also a few quick Data Stories prompts, as well as collections and NGSS tasks that offer themed activities across multiple data sets.
Activities provide guided questions to support data set manipulation and interpretation. Teachers can use a provided template to create new activities or modify existing ones. Teachers and students can also upload their own data sets, although activities can’t be made for these. The teacher dashboard has tabs for each class, providing management for classes as well as students. Video tutorials, webinars, a glossary, and instructional support for data literacy are available on the resources page.
Data literacy is crucial, and Tuva Labs has huge potential for developing these skills. Their curated offerings make real data accessible, parceling it into chunks more manageable than many source sites. You can start with data on only 3,500 women from 1900–2000, instead of tackling the entire U.S. Census Bureau. Further, the intuitive interface makes data-play feasible and fun for kids.
There are over 400 activities currently available that provide at least an introduction to what data can show, and in some cases offer more detailed exploration -- though many of the data sets still lack coordinated activities. Teachers may need to add to or expand on some of these activities depending on students' level and expected outcomes, and it appears answer keys are up to teachers to create themselves. There are lots of teacher resources available, and Tuva has made an effort to provide materials around data interpretation, but it may take some time to get up to speed on the full functionality of the program. Many of the activities are created by Tuva staff; more of these expert-created activities would support in-class learning, and posting (and vetting) activity keys could reassure any hesitant educators.
Key Standards Supported
Expressions And Equations
Graph proportional relationships, interpreting the unit rate as the slope of the graph. Compare two different proportional relationships represented in different ways. For example, compare a distance-time graph to a distance-time equation to determine which of two moving objects has greater speed.
Compare properties of two functions each represented in a different way (algebraically, graphically, numerically in tables, or by verbal descriptions). For example, given a linear function represented by a table of values and a linear function represented by an algebraic expression, determine which function has the greater rate of change.
Interpret the equation y = mx + b as defining a linear function, whose graph is a straight line; give examples of functions that are not linear. For example, the function A = s2 giving the area of a square as a function of its side length is not linear because its graph contains the points (1,1), (2,4) and (3,9), which are not on a straight line.
Construct a function to model a linear relationship between two quantities. Determine the rate of change and initial value of the function from a description of a relationship or from two (x, y) values, including reading these from a table or from a graph. Interpret the rate of change and initial value of a linear function in terms of the situation it models, and in terms of its graph or a table of values.
Describe qualitatively the functional relationship between two quantities by analyzing a graph (e.g., where the function is increasing or decreasing, linear or nonlinear). Sketch a graph that exhibits the qualitative features of a function that has been described verbally.
Interpreting Categorical And Quantitative Data
Represent data with plots on the real number line (dot plots, histograms, and box plots).
Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets.
Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve.
Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data.
Statistics And Probability
Recognize a statistical question as one that anticipates variability in the data related to the question and accounts for it in the answers. For example, “How old am I?” is not a statistical question, but “How old are the students in my school?” is a statistical question because one anticipates variability in students’ ages.
Understand that a set of data collected to answer a statistical question has a distribution which can be described by its center, spread, and overall shape.
Recognize that a measure of center for a numerical data set summarizes all of its values with a single number, while a measure of variation describes how its values vary with a single number.
Display numerical data in plots on a number line, including dot plots, histograms, and box plots.
Summarize numerical data sets in relation to their context, such as by:
Reporting the number of observations.
Describing the nature of the attribute under investigation, including how it was measured and its units of measurement.
Giving quantitative measures of center (median and/or mean) and variability (interquartile range and/or mean absolute deviation), as well as describing any overall pattern and any striking deviations from the overall pattern with reference to the context in which the data were gathered.
Relating the choice of measures of center and variability to the shape of the data distribution and the context in which the data were gathered.
Informally assess the degree of visual overlap of two numerical data distributions with similar variabilities, measuring the difference between the centers by expressing it as a multiple of a measure of variability. For example, the mean height of players on the basketball team is 10 cm greater than the mean height of players on the soccer team, about twice the variability (mean absolute deviation) on either team; on a dot plot, the separation between the two distributions of heights is noticeable.
Use measures of center and measures of variability for numerical data from random samples to draw informal comparative inferences about two populations. For example, decide whether the words in a chapter of a seventh-grade science book are generally longer than the words in a chapter of a fourth-grade science book.
Construct and interpret scatter plots for bivariate measurement data to investigate patterns of association between two quantities. Describe patterns such as clustering, outliers, positive or negative association, linear association, and nonlinear association.
Know that straight lines are widely used to model relationships between two quantitative variables. For scatter plots that suggest a linear association, informally fit a straight line, and informally assess the model fit by judging the closeness of the data points to the line.
Use the equation of a linear model to solve problems in the context of bivariate measurement data, interpreting the slope and intercept. For example, in a linear model for a biology experiment, interpret a slope of 1.5 cm/hr as meaning that an additional hour of sunlight each day is associated with an additional 1.5 cm in mature plant height.
Understand that patterns of association can also be seen in bivariate categorical data by displaying frequencies and relative frequencies in a two-way table. Construct and interpret a two-way table summarizing data on two categorical variables collected from the same subjects. Use relative frequencies calculated for rows or columns to describe possible association between the two variables. For example, collect data from students in your class on whether or not they have a curfew on school nights and whether or not they have assigned chores at home. Is there evidence that those who have a curfew also tend to have chores?
Key Standards Supported
Earth and Human Activity
Analyze and interpret data on natural hazards to forecast future catastrophic events and inform the development of technologies to mitigate their effects.
Apply scientific principles to design a method for monitoring and minimizing a human impact on the environment.
Construct an argument supported by evidence for how increases in human population and per-capita consumption of natural resources impact Earth’s systems.
Ask questions to clarify evidence of the factors that have caused the rise in global temperatures over the past century.
Evaluate competing design solutions for developing, managing, and utilizing energy and mineral resources based on cost-benefit ratios.
Use a computational representation to illustrate the relationships among Earth systems and how those relationships are being modified due to human activity.
Earth’s Place in the Universe
Develop and use a model of the Earth-sun-moon system to describe the cyclic patterns of lunar phases, eclipses of the sun and moon, and seasons.
Collect data to provide evidence for how the motions and complex interactions of air masses results in changes in weather conditions.
Ecosystems: Interactions, Energy, and Dynamics
Analyze and interpret data to provide evidence for the effects of resource availability on organisms and populations of organisms in an ecosystem.
Construct an argument supported by empirical evidence that changes to physical or biological components of an ecosystem affect populations.
Construct, use, and present arguments to support the claim that when the kinetic energy of an object changes, energy is transferred to or from the object.
Motion and Stability: Forces and Interactions
Ask questions about data to determine the factors that affect the strength of electric and magnetic forces.