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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.
Calculate and compare the slopes and intercepts of multiple trend lines within the same graph to analyze differences between categories and their relationships.
6-8
30
Ask or identify a question that can be verified with data collected through observations.
6-8
30
State a guess or potential answer to a question for later verification or testing via a hypothesis.
6-8
30
Predict whether the variability of one variable tends to increase or decrease in relation to another variable, providing evidence and reasoning to support the prediction.
6-8
30
State a prediction or answer to an investigation question at the beginning, midway, and at the end of the analysis exercise while asking why this may be true each time.
6-8
30
Assess the data to determine which aspects of the original question can be answered and identify which areas still require further investigation for a confident conclusion.
6-8
30
Generate an original statement that answers the original investigation question in a direct way and provides relevant statistical data to support one's statistical conclusion.
6-8
30
Identify a statement that does NOT answer the original investigation question in a direct way and provides relevant and sufficient data to support one's statistical conclusion.
6-8
30
Identify various factors that may cause data in a dataset to insufficiently represent or apply to other situations.
6-8
30
Identify characteristics of data-based predictions that easily and do not easily generalize to many situations.
6-8
30
Evaluate a population based on a sample by making informal arguments for the sample's sufficiency in answering the question.
6-8
30
Identify potential weaknesses in a given sample that may limit its ability to represent a broader population or phenomenon.
6-8
30
Recognize that a sample must be sufficiently large to well-represent a broader population, based on the concept of the Law of Large Numbers. e.g., flipping a coin 10 times might give 7 heads, but 1000 flips will trend towards 50/50
6-8
30
Identify examples of too-small sample sizes in the media or other real-world examples. e.g., medical drug drials, prior debunked research
6-8
30
Acknowledge that a sample may be systematically skewed due to collection methods, data availability, survey design, or other factors, as demonstrated in a direct data collection activity.
6-8
30
Identify additional possible scenarios for which a data-based conclusion may apply, beyond the original question or inquiry.
6-8
30
Summarize variables in a dataset with measures of central tendency with both the full data and with subsets (e.g., occupation, race, gender, income, zipcode, education).
6-8
30
Acknowledge that examining the same data with identical methods can yield different results due to varying factors, and that a "fact” is not always quickly or easily proven. e.g., data collection issues, analysis approaches, analysis errors, model assuptions
6-8
30
Create data visualizations that use multiple variables.
6-8
30
Create a data visualization, collect feedback from the target audience, and revise the visualization based on feedback.
6-8
30
Create map visualizations to display location data. e.g., events at certain spots on a map, data by state or region
6-8
30
Use visualizations of common data distributions to identify potential errors in the data. e.g., outliers, out-of-bounds values
6-8
30
Visualize the distribution of data to illustrate the shape, spread, and measures of center informally.
6-8
30
Create scatterplots for pairs of numerical variables in the data set and evaluate whether the relationships or non-relationships are as expected.
6-8
30
Clearly label a data visualization to demonstrate what the data is, what the unit of measure is, and where it came from.
6-8
30
Choose or create a representation and color palette for one or two-variable data, and explain or defend their choice.
6-8
30
Answer questions about continuous numerical scaled data, location data, and/or categorical data represented visually with multiple variables.
6-8
30
Describe the relationships (or lack thereof) represented in scatterplots (e.g., direct vs. inverse, positive vs. negative).
6-8
30
Review non-standard data representations that appear in popular media, identify the key visual elements and what they mean, and describe the intent and evaluate whether or not it is successful.
6-8
30
Compare and/or contrast various representations of data sets with multiple features and describe what is emphasized, de-emphasized, or obscured in each representation.
6-8
30
Describe how different ways of representing data can improve clarity or mislead.
6-8
30
Describe and discuss the typical visualization characteristics of numeric, categorical, and string data while identifying and outlining the differences between them.
6-8
30
Evaluate the degree to which visualizations and their surrounding text and context match and support one another.
6-8
30
Recognize that data visualizations need explanations to tell their story.
6-8
30
Explain what the data reveals and whether it supports or contradicts any claims initially made.
6-8
30
Create a visualization based on a 3-5 sentence narrative describing a particular environmental phenomenon involving multiple variables.
6-8
30
Create a provocative question, support that question with relevant data, and reveal the story the data is telling, including connections with real-life scenarios and potential solutions.
6-8
30
Present data in a way that is accessible and engaging, while considering the specific needs, interests, and knowledge level of the audience.
6-8
30
Use visuals to enhance understanding and/or incorporate interactive discussion about the data and the narrative.
6-8
30
Identify the reason a data representation was created. e.g., to persuade, present factual information
6-8
30
Identify potential biases of the source of data used to create a visualization.
6-8
30
Communicate the limitations of data visualizations based on the source of data used in to create it.
6-8
30
Collect personal data and use it to benefit their family or classroom.
6-8
30
Assess a current events news story featuring a data visualization and evaluate how effectively the graphic communicates the situation while allowing for a valid comparison.
6-8
30
Recognize that data collection practices, tools, representations and resulting consequences are unevenly distributed across the globe.
6-8
30
Define "qualitative" and "quantitative" and understand how they relate to categorical and numeric data.
9-10
40
Understand that forms of media (e.g., photographs, written text, audio recordings) can be represented in quantitative and qualitative terms.
9-10
40
Explain how data-based decisions are revisited as new evidence or societal needs emerge (e.g., blood pressure cut-off numbers, dietary guidance, medical benchmarks).
9-10
40
Evaluate why data models require updates to maintain accuracy and relevance.
9-10
40
Recognize the different types of variability (e.g., natural, measurement, sampling).
9-10
40
Evaluate claims derived from data by questioning how phenomena are measured, categorized, or represented.
9-10
40
Describe the basic mathematical features of an AI model in terms of independent variables (e.g., inputs), dependent variables (e.g., outputs), and predictors or weights (e.g., slopes of many variables). e.g., AI models use math to weigh inputs, such as a music recommendation model might calculate:(play_count × weight₁) + (listen_duration × weight₂) + (skip_count × weight₃) = recommendation_score, and weights are adjusted automatically to minimize mismatches between predicted and actual user preferences.
9-10
40
Describe and explore how it is possible for data in a variety of formats (e.g., images) to be translated into organized, numerical information for an AI model to process.
9-10
40
Identify how biases in training data can lead to biases in AI models by directly affecting predictors or weights. e.g., If AI only sees pictures of cats in sunlight, it would fail to recognize cats in shadows
9-10
40
Analyze how data use can perpetuate biases or systemic inequities (e.g., predictive policing, hiring algorithms).
9-10
40
Evaluate context-specific risks and benefits of data interpretations (e.g., health tracking for improving care vs. privacy concerns).
9-10
40
Recognize how biases can obscure inferences drawn from data.
9-10
40
Consider how the consolidation or combination of different data can create additional biases.
9-10
40
Evaluate how data drives innovation in fields and informs community choices.
9-10
40
Design and refine investigations to address contextual problems (e.g., social, educational, business, medical, governmental issues), evaluating limitations and biases.
9-10
40
Employ iteration in an investigation to strengthen interpretations or inspire new investigations.
9-10
40
Use digital tools to test and refine inferences from large or complex datasets.
9-10
40
ReInterpret data from multiple perspectives, disciplines, and historical frames of reference.
9-10
40
Compare and contrast problem solving approaches and the resulting findings.
9-10
40
Utilize data science tools and methods to engage in personal and collective inquiry relevant to one’s own life and interests.
9-10
40
Use data dictionaries to identify codes for missing or incomplete data (e.g., NA, 99999, 0, " "), and either recode or filter data to remove those observations.
9-10
40
Apply basic cross-validation techniques to verify data quality across multiple sources, including source comparison, split sampling, internal consistency checks, and domain range validation.
9-10
40
Create and manage complex data structures with multiple related tables, understanding primary and foreign key relationships between datasets.
9-10
40
Transform and restructure hierarchical or nested data into normalized tabular formats suitable for analysis.
9-10
40
Design efficient organizational schemas for large datasets with multiple variables and complex relationships.
9-10
40
Use calculations and logic statements to create new categorical variables based on existing categorical (e.g., if(employment=”employed”, Yes, No)) or quantitative variables (e.g., if(weight<30, light, if(weight>60,heavy ,medium))
9-10
40
Filter data based on groups or subsets of data relevant to the problem and context.
9-10
40
Create summary measures for groups that can then be used as a measure at the group level (e.g., for salary data, compute average salary for different occupational groups).
9-10
40
Construct data-based questions about the design of a study to determine causality and make predictions.
9-10
40
Identify comparison and association data-based questions appropriate for addressing problems of interest.
9-10
40
Describe benefits and drawbacks of using proxy variables.
9-10
40
Use and/or change parameters of simulations to generate data to address a problem of interest.
9-10
40
Design sensor-based experiments or automated data collection scenarios to explore a problem or question and identify the scenarios' limitations and trade-offs.
9-10
40
Describe the features, benefits, limitations, and ethical thinking that went into a data collection process.
9-10
40
Design and implement traditional data collection methods (e.g., surveys, observations, field studies) to investigate research questions and evaluate their strengths and limitations compared to automated approaches.
9-10
40
Develop comprehensive data collection plans that address potential limitations, specify quality control measures, and include contingency strategies.
9-10
40
Locate and retrieve relevant datasets from publicly available scientific, civic, or government databases using search tools and filters.
9-10
40
Evaluate datasets from multiple sources to determine which best addresses a research question, considering factors such as data quality, sample size, and collection methods.
9-10
40
Use data catalogs, repositories, and open data portals to find datasets that meet specific criteria for investigations.
9-10
40
Create a data dictionary to document the data collection process.
9-10
40
Make use of metadata and data dictionary to understand a data set.
9-10
40
Consider who or what was included in the data collection and who or what was not.
9-10
40
Evaluate and address ethical implications of data collection choices, including privacy, bias, and representation.
9-10
40
Analyze existing datasets for potential bias, discrimination, or unfair representation.
9-10
40
Work with datasets requiring multiple types of cleaning such as missing values, errors, and anomalies.
9-10
40
Clean and prepare datasets before merging multiple sources.
9-10
40
Work with datasets that include time-series data at different intervals to detect various patterns.
9-10
40
Understand and work with different observation structures beyond individual units.
9-10
40
Work with merged datasets that align different time scales and observation structures.
9-10
40
9-10.B.4.3a Work with datasets with over 20 variables and over 1000 observations.
9-10
40
Transform data between wide and long formats based on analysis needs.
9-10
40
Merge multiple datasets while maintaining appropriate observation structure.
9-10
40
Transform complex variables into more interpretable forms using student-relatable benchmarks.
9-10
40
Identify appropriate ways to summarize numerical or categorical data using frequency tables, graphical displays, and numerical summary statistics.
9-10
40
