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Data & AI
Recognize that data “fuels” AI, that AI can be compared to a function machine (math), algorithm (CS), or a prediction model (statistics) that relies on data to both operate and improve itself, and that AI tools can also be used to analyze complex data in research.
Data & AI
Recognize that data “fuels” AI, that AI can be compared to a function machine (math), algorithm (CS), or a prediction model (statistics) that relies on data to both operate and improve itself, and that AI tools can also be used to analyze complex data in research.
Apply context
Recognize that the context surrounding the data and the investigation shapes interpretation. Many fields (biology vs. psychology; economics vs. sociology) have created very different frameworks to organize problems. Considering multiple approaches may reveal useful insights from the same data.
Apply context
Recognize that the context surrounding the data and the investigation shapes interpretation. Many fields (biology vs. psychology; economics vs. sociology) have created very different frameworks to organize problems. Considering multiple approaches may reveal useful insights from the same data.
Frequency tables
Organize data into frequency tables based on shared characteristics. Summarize data using counts, fractions, relative frequencies, or proportions to enable comparisons and generalizations. Understand the implications of choices made when creating and interpreting frequency tables.
Frequency tables
Organize data into frequency tables based on shared characteristics. Summarize data using counts, fractions, relative frequencies, or proportions to enable comparisons and generalizations. Understand the implications of choices made when creating and interpreting frequency tables.
Comparing variability
Examine differences between groups by analyzing measures of spread, such as range and standard deviation. Utilize visualizations like box plots and apply statistical methods, including mean, median, and standard deviation, to compare datasets, assess variability, and uncover patterns in data distributions and models.
Comparing variability
Examine differences between groups by analyzing measures of spread, such as range and standard deviation. Utilize visualizations like box plots and apply statistical methods, including mean, median, and standard deviation, to compare datasets, assess variability, and uncover patterns in data distributions and models.
Explaning significance
Clearly describe the basic logic of statistical significance to others, differentiating between significance, the size of an effect, and the statistical power of an analysis. Recognize what statistical significance can reveal and cannot reveal about a phenomenon.
Explaning significance
Clearly describe the basic logic of statistical significance to others, differentiating between significance, the size of an effect, and the statistical power of an analysis. Recognize what statistical significance can reveal and cannot reveal about a phenomenon.
Sense-making with visualizations
Practice creating visualizations to summarize many things at once, relationships between things in one place, or exceedingly complex ideas in one place. Recognize that visuals can be more efficient or compelling than other forms of communication.
Sense-making with visualizations
Practice creating visualizations to summarize many things at once, relationships between things in one place, or exceedingly complex ideas in one place. Recognize that visuals can be more efficient or compelling than other forms of communication.
Recognize the environmental cost of running large data centers and AI/ML models while considering the costs versus benefits of nuclear power and evaluating solar and wind options for clean energy.
9-10
40
Evaluate an impactful data story and its societal implications. e.g., historical heart disease research impacts for men and women
9-10
40
Clearly state a result or finding, along with the degree of certainty, using two or more advanced statistical methods (e.g., probability distributions, t-tests, z-tests, or bootstrapping/simulation), while justifying the conclusions with evidence (e.g., dataset or source characteristics, similar findings in alternative data) quality indicators like dataset characteristics, source reliability, and corroborating findings from alternative data.
Advanced
60
Apply Bayes Theorem to an example result in an academic research finding or discussion.
Advanced
60
Explain Bayes Theorem in formal conditional probability statements: P(A|B) = (P(A) * P(B|A)) / P(B), where A is the event in question and B is the event of new evidence related to A.
Advanced
60
Describe a p-value to without using the language of the "null hypothesis" or "alternative hypothesis."
Advanced
60
Identify examples of p-value misuse in the media or academic research.
Advanced
60
Describe the relationship between the margin of error, confidence intervals, and standard deviation, in both words and in their formal mathematical definitions.
Advanced
60
Execute and correctly interpret the margin of error, confidence interval, and standard deviation in a data analysis software for a given summary statistic.
Advanced
60
Explain why a chosen analysis method effectively isolates an effect.
Advanced
60
Justify a causal relationship in a multivariable dataset with real-world data, including additional datasets gathered from outside sources and connect the analysis to existing research literature.
Advanced
60
Implement randomization using a random seed in a simulation technique using a computer-based analysis tool to compare sampling techniques (e.g., sampling with replacement or without replacement).
Advanced
60
Analyze and interpret the regression coefficients to understand the effect of the categories on the model.
Advanced
60
Create an “ideal” multi-variable model for real-world data in a computer-based software that explains as much variance as possible, without overfitting a model. Justify how you have found the “ideal” model by comparing R^2, covariance, and the number of variables chosen in relation to their real-world context.
Advanced
60
Use computer software to incorporate categorical variables into a linear regression model.
Advanced
60
Document analysis steps and errors while implementing validation checks in the software for data wrangling.
Advanced
60
Execute an alternative analysis plan to validate a significantly different result from the initial method.
Advanced
60
Identify machine learning methods such as supervised, unsupervised, and reinforcement learning, and discuss the pros and cons of each when data on the entire population or a very detailed sample with many variables is available.
Advanced
60
Make a formal Power Analysis by identifying a sufficient sample size for a real-world data exploration. Students should mathematically isolate “n” in a t-test or z-test, and estimate Power with a software tool.
Advanced
60
Estimate bias by interpreting and applying the formula for a biased estimator.
Advanced
60
Demonstrate presentation skills to fully communicate depth and breadth of a visualization to an audience.
Advanced
60
Present both 1) basic visual summaries of the data2) additional visualizations that “go deeper” into the story the data is telling, and relationships discovered within the data visualizatione.g., new relationships within subsets, significant outliers, complex or overlapping control variables
Advanced
60
Apply design principles such as balance, emphasis, and simplicity to make visualizations clear and engaging.
Advanced
60
Understanding the basics of interactive visualizations (e.g., tooltips, zooming) and their advantages in data exploration.
Advanced
60
Describe the potential relationships (or lack thereof) represented in scatterplots (including linear, exponential, logarithmic, polynomial, and piecewise) and debate which function is the best representation for the shape and context.
Advanced
60
Visualize confidence intervals or margins of error using error bars with computer-based software.
Advanced
60
Visualize margins of error of a continuous variable using error bands with a computer-based software.
Advanced
60
Compare and/or contrast 2D and 3D bar graphs and pie charges and identify how unnecessary use of three dimensions obfuscates the relative frequencies and/or proportions of the data.
Advanced
60
Compare and/or contrast varying bin sizes to demonstrate how different degrees of granularity in a histogram or other visualization type can lead to different interpretations.
Advanced
60
Use complex visualizations like multivariable graphs, scatter plots, heat maps, or interactive dashboards to present data clearly. Then, develop a research paper or presentation to explain the background, methodology, and context of the data, using visualizations to provide evidence of their findings and conclusions.
Advanced
60
Pick a local issue of student interest and draft a Letter to the Editor (LTE) to a local news outlet or to a local politician based on conclusions from public-access datasets.
Advanced
60
Utilize both categorical and numeric data.
K-2
10
Recognize that data can be derived from many different forms of sources (e.g., photographs, written text, audio recordings, videos, people, and other non-traditional places).
K-2
10
Understand that data can be used to ask and answer questions.
K-2
10
Recognize the importance of asking questions about how data were collected.
K-2
10
Observe that data can have many different answers or results.
K-2
10
Understand that data can show some things but not others. e.g., categorizing the colors worn in a kindergarten class does not indicate the clothing item or size
K-2
10
Recognize that computing tools (e.g., computers, smartphones, IoT buttons, sensors) and AI need data from human inputs (e.g., a function machine: if x input, then y action) to perform actions. e.g., smart thermostat turns on heat when the temperature sensor detects the room temperature is colder than the temperature a human programmed, such as 68°F
K-2
10
Understand that AI tools use data from people to do tasks. e.g., chatbots learn from typed questions
K-2
10
Recognize how data can be useful in understanding the world around us. e.g., counting rainy days to plan outdoor activities
K-2
10
Understand that some data about people should not be collected or shared with technology.
K-2
10
Recognize how data are affected by decisions made around data design, collection, and interpretation.
K-2
10
Use data to answer questions and see how it improves guesses.
K-2
10
Recognize there is an investigative process for exploring questions about the world.
K-2
10
Utilize different views such as pictures, tallies, or charts to help answer questions and notice patterns.
K-2
10
Utilize a single set of data to generate multiple inferences for various inquiries.
K-2
10
Share data and interpretations with others.
K-2
10
Develop curiosity about data and how it can be used in the world.
K-2
10
Exhibit the capacity to work with open-ended problems.
K-2
10
Recognize and explain any missing data (e.g., a student was absent when data was collected) or data recording errors (e.g., "10" recorded as a "1").
K-2
10
Record responses so that you can tell if everyone has been asked.
K-2
10
Collect and record data on case cards, wherein each card represents a single observation.
K-2
10
Create categories from individual categorical responses (e.g., scary things).
K-2
10
Define the categories used to measure the qualities of an object (e.g., color, shape).
K-2
10
Sort case cards so that observations with similar values for a variable are grouped together.
K-2
10
Order case cards so that a numerical variable is ordered from smallest to largest or largest to smallest.
K-2
10
Count the number of items in different groups when data is organized into simple categories (e.g., counting how many students chose each favorite color).
K-2
10
Formulate simple questions that guide data collection and analysis about familiar contexts, using appropriate support.
K-2
10
Recognize that simulations and models can act as sources of data.
K-2
10
Recognize the relative value and tradeoffs of data collection tools including sensors, surveys, etc.
K-2
10
Recognize that data can be found in various sources such as books, websites, and classroom resources to help answer questions.
K-2
10
Explore simple, age-appropriate data sources provided by teachers or educational websites that show information about familiar topics.
K-2
10
Anticipate variability in measurement.
K-2
10
Use either standard (e.g., inches, feet, miles) or nonstandard (e.g., paperclips, shoes) units to determine a physical quantity (e.g., width of a table) and understand the importance of standard units for consistency.
K-2
10
Begin to coordinate multiple variables of the same observation (e.g., measure more than one variable of an object or event).
K-2
10
Understand how other people measured their data.
K-2
10
Understand that collecting data about people requires their permission. e.g., asking before writing down a classmates favorite color
K-2
10
Ask permission before sharing others’ information.
K-2
10
Work with datasets that are relatively clean (e.g., don't have missing data or errors).
K-2
10
Use datasets that include only numerical or only categorical variables.
K-2
10
Work with datasets with 1 - 2 variables and 10 - 30 observations (e.g., size of a class).
K-2
10
Work with datasets already formatted into structure necessary for analysis.
K-2
10
Create new categories from existing data through basic grouping rules.
K-2
10
Recognize that categorical data does not have a measure of center.
K-2
10
Describe the center of numeric data categorically using phrases like “most popular”.
K-2
10
Describe the upper and lower bounds of a set of objects. e.g., tallest and shortest, biggest and smallest
K-2
10
Describe the shape of the data categorically. e.g., "all grouped together", "spread out", "lots of small groups"
K-2
10
Sort objects into a frequency table based on shared characteristics.
K-2
10
Identify the absence of data.
K-2
10
Discuss the context of data. e.g., where or when it was collected
K-2
10
Describe similarities or differences across two variables.
K-2
10
Work with visual aids (e.g., colorful charts) and hands-on activities to sort objects (e.g., color).
K-2
10
Use primary data (e.g., favorite fruit) and represent data with tally marks or pictographs.
K-2
10
Organize objects by size, color, shape, etc.
K-2
10
Use language like “goes with” “belongs to”, or “matches” to group items together.
K-2
10
Analyze sensory data by counting occurrences of sounds (e.g., claps or animal noises).
K-2
10
Categorize sensory data by type (e.g., loud vs. soft).
K-2
10
Sort and compare objects based on textures (e.g., smooth, rough, or bumpy).
K-2
10
Describe how similar objects can differ based on characteristics such as color, shape, and size.
K-2
10
Describe how two things or groups are different from one another (e.g., more or less, bigger or smaller).
K-2
10
Examine differences between groups by analyzing measures of spread, such as range and standard deviation. Utilize visualizations like box plots and apply statistical methods, including mean, median, and standard deviation, to compare datasets, assess variability, and uncover patterns in data distributions and models.
Recognize that some digital tools help people who have difficulty seeing, hearing, or using technology.
K-2
10
Understand that objects can be grouped based on similar characteristics. e.g., "all blue items go here," "this group has ground shapes"
K-2
10
Recognize that criteria for sorting objects helps to organize them and identify patterns. e.g., grouping buttons by shape reveals which shapes are most common
K-2
10
Predict whether an object belongs to a group or category based on its characteristics.
K-2
10
Articulate simple rules for sorting. e.g., "all blue items go here," "this group has ground shapes"
K-2
10
Classify objects based on their observed similarities and characteristics.
K-2
10
Extend simple patterns based on observable characteristics. e.g., arranging objects by size
K-2
10
Recognize that some situations are not binary and find appropriate vocabulary to describe them. e.g., a classmate riding the bus to school could be "always," "sometimes," or "never"
K-2
10
Discuss a guess or hypothesis before an investigation, compare the initial guess to findings, and informally express how new data changes a prior guess.
K-2
10
Describe characteristics of a population and recognize that variability exists within any population. e.g., jelly beans in a jar
K-2
10
