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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.
Understand how random assignment in comparative experiments is used to control for characteristics that might affect responses.
3-5
20
Locate and retrieve simple datasets from educational resources and child-friendly data repositories to investigate specific questions.
3-5
20
Identify basic criteria for determining whether a dataset is relevant to a given question (e.g., topic match, timeframe, geographic relevance).
3-5
20
Understand that a variable measures the same characteristic on several individuals or objects.
3-5
20
Recognize and apply measurement precision, including why repeated measurements may vary and how to choose appropriate precision levels.
3-5
20
Identify the characteristics of an event or object that can be measured.
3-5
20
Plan and conduct measurements by identifying measurable characteristics and collecting both categorical and numerical variables of objects/events.
3-5
20
Consider the reasonable values for each of the variables and note those that are suspect.
3-5
20
Recognize that personal information needs to be used respectfully and that this hasn’t always been done in the past.
3-5
20
Consider how data categories might affect different people in different ways. e.g., asking students about the language they speak at home and not including that language as an option may make students feel excluded
3-5
20
Work with datasets that require some cleaning (e.g., resolution of missing data or blank cells).
3-5
20
Verify data by comparing recorded values to original sources when possible.
3-5
20
Use datasets that include only variables necessary to answer the stated question.
3-5
20
Use datasets with both numerical and categorical variables.
3-5
20
Work with datasets with up to 4 variables and up to 50 observations.
3-5
20
Recognize the difference between numerical and categorical data and choose the appropriate type for a particular measurement.
3-5
20
Combine information from two simple datasets about the same objects or events.
3-5
20
Create new variables through simple calculations or combinations of existing data.
3-5
20
Convert data between different basic formats (e.g., from tally marks to numbers).
3-5
20
Calculate summaries for categorical and numeric data, focusing on total and typical values.
3-5
20
Calculate the range for numerical data.
3-5
20
Describe the number of clusters, symmetric or not, and gaps. e.g., dot plot of test scores might show a cluster at 80-90% meaning most students did well and a gap at 50-60% meaning few students struggled
3-5
20
Summarize data with fractions, relative frequencies, proportions, or percentages to make comparisons.
3-5
20
Categorically describe the absence of data.
3-5
20
Understand the definition and use of metadata (e.g., data and time, text, continuous, geolocation).
3-5
20
Observe whether or not there appears to be an association between two variables. e.g., student height compared to shoe size vs. student height compared to favorite color
3-5
20
Understand that the distribution of a categorical or numerical variable represents how often a specific outcome occurs.
3-5
20
Recognize that distributions can be used to compare two groups.
3-5
20
Create time-series graphs to determine change in variable over time
3-5
20
Use data collected through surveys or experiments (e.g., heights of fellow classmates) and use spreadsheets to visualize trends and relationships
3-5
20
Use no-code or low-code data science tools. e.g., CODAP, Desmos, Google sheets
3-5
20
Identify word frequencies from a simple text (e.g., paragraph or story).
3-5
20
Collect and analyze simple sensor data (e.g., temperature readings over a day).
3-5
20
Use data from surveys (e.g., favorite snacks) and then have students use this data to build a decision tree.
3-5
20
Sort, order, group, or otherwise organize objects or their representations to answer questions.
3-5
20
Categorically describe the center, spread, and shape of a simple distribution and understand what each of these descriptions refer to.
3-5
20
Understand how data varies by exploring spread (e.g., range) and comparing qualities (e.g., brightness or temperature).
3-5
20
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.
Identify and explain simple measurement error. e.g., different students' get varying results when measuring the same object
3-5
20
Identify potential sources of natural variability in a given measure based on knowledge of the data context. e.g., plants can be different heights, plants grow taller over time, plants grow differently in different areas in the garden
3-5
20
Summarize data that is represented in a digital tool.
3-5
20
Select a no-code digital tool that is suited for the intended task.
3-5
20
Identify tools that make data more accessible, such as screen-readers, captions, or tactile graphs.
3-5
20
Understand that grouping objects by shared characteristics creates rules that can be used to classify and categorize new objects.
3-5
20
Recognize that patterns and relationships in data provide different kinds of information.
3-5
20
Discuss how data relationships help describe real-world phenomena. e.g., taller plants tend to be older
3-5
20
Predict whether an object belongs to a group or category based on its characteristics.
3-5
20
Distinguish patterns from relationships in data.
3-5
20
Formulate a guess or hypothesis and identify informal vocabulary to convey your level of confidence. e.g., I strongly believe most of my classmates ride the bus to school because XX or YY
3-5
20
Record a guess about the world, compare the initial assumption to new findings from data, and assess the extent to which the original assumption should change in light of new evidence.
3-5
20
Discuss the relationship between magnitude and probability. e.g., is a small chance of a large-sized event equivalent to a medium chance of a medium-sized event
3-5
20
Describe how "unusual" a result may be compared to an otherwise expected outcome in a given situation. e.g., flipping a coin 10 times and getting 10 heads is highly unlikely
3-5
20
Recognize that a sample of a group may or may not reflect the entire group. e.g., if the class's favorite drink for lunch is chocolate milk, does that mean the school's favorite drink for lunch is chocolate milk?
3-5
20
Relate the effect of repeated samples to the representativeness of an entire group. e.g., pulling 10 jellybeans from a jar 5 times gives a better estimate of the color distribution than just one handful
3-5
20
Using graphical displays, informally assess whether or not there is an association between two phenomenon in a data visualization and discuss whether one observation or trend may affect the other.
3-5
20
Recognize that randomization ensures fairness in selection processes and consider the potential consequences of non-blind selection methods. e.g., picking a raffle champion, prizes from a jar, candy of different sizes from a treat bag without looking
3-5
20
Describe patterns in two-variable data, such as data that show trends that increase or decrease, or relationships shown in different types of graphs. e.g., side-by-side bar charts and line graphs
3-5
20
Ask or identify a question that you answer by counting or measuring results from different groups.
3-5
20
Identify from among a set of given examples what types of questions can be answered with real-world data (e.g., values, opinions, non-observables).
3-5
20
Estimate the total count of a characteristic within a group, providing several reasons to support the accuracy of your estimate.
3-5
20
Evaluate whether the count of a characteristic in one group differs from that in another group, considering various reasons for this difference.
3-5
20
Identify reasons to support and refute conclusions when drawing insights from data.
3-5
20
Propose types of data and/or data comparisons that are relevant for answering a given investigation question.
3-5
20
Identify types of data and/or data comparisons that are NOT relevant for answering a given investigation question.
3-5
20
Recognize that a result or pattern from data does not always extend to other situations.
3-5
20
Recognize that in some situations, a small amount of data can represent or estimate a larger unknown, saving time and effort. e.g., dice rolling, jars of jelly beans
3-5
20
Recognize that in a scenario of random chance (e.g., dice rolls, jar of jelly beans), too few trials can skew conclusions. e.g., flipping a coin twice and getting heads both times doesn't mean it's always heads and more flips will provide a clearer picture
3-5
20
Acknowledge that errors can arise in analysis due to both human and technological factors, especially when the analysis is duplicated. e.g., different sensors, multple data collections, mutliple people
3-5
20
Create data visualizations to summarize categorical data.
3-5
20
Display groups or categories in visualizations using complementary or contrasting colors to highlight differences.
3-5
20
Display continuously scaled data in visualization using shading.
3-5
20
Recognize how frequency distributions can help identify outliers and errors in the data. e.g., data contains values that shouldn't be possible
3-5
20
Organize and present collected data visually to highlight relationships and to support a claim.
3-5
20
Identify and support how different colors and/or patterns can be used in visualizations to represent different groups/categories/scales in the data.
3-5
20
Reliably use the parts (e.g., titles, labels, legends, colors) of bar graphs, picture graphs, and line graphs.
3-5
20
Answer questions about fractional valued numerical data or categorical data represented visually with one or two variables.
3-5
20
Recognize unusual data points and consider reasons why they might appear.
3-5
20
Work with a variety of data types, including numerical data, charts, graphs, and visual representations to draw conclusions and understand the story the data is telling.
3-5
20
Compare and/or contrast various visualizations of the same data by altering different features (e.g., reordering bars, changing colors), and explain how these changes affect what is highlighted or obscured in each representation. e.g., bar graph sorted by size highlights the most popular option, while sorting alphabetically can make comparison challenging
3-5
20
Visualize multiple types of data (e.g., numeric, categorical, string data) during in-class data collection exercises.
3-5
20
Evaluate the effectiveness of text, visualization, and text plus a visualization to communicate a particular story.
3-5
20
Make a prediction based on a visualization using the terms: “likely, unlikely, certain, and impossible.”
3-5
20
Describe the data clearly by identifying any trends or patterns found using descriptive language and terms such as “most,” “least,” “greater than,” “less than,” and “equal to."
3-5
20
When describing the data, decide whether any claim made about the data makes sense.
3-5
20
Find ways to generate interest in the story by crafting a hook that captivates the audience, then supporting it with data examples that reveal the narrative the data conveys.
3-5
20
Understand various audiences and adapt storytelling to suit their needs and comprehension levels. e.g., using straightforward language with peers vs. more analytical explanations with teachers
3-5
20
Provide the appropriate level of context for various audiences.
3-5
20
Assess the purpose and effectiveness of a data story by identifying why it is being told, its goal, and whether it achieves that goal.
3-5
20
Identify situations when data can be used to make decisions at school or at home.
3-5
20
Develop creative data visualizations to depict an aspect of the student's community or social connections.
3-5
20
Draw simple conclusions about the data from a narrative.
3-5
20
Use familiar examples of energy consumption (e.g., tablets, laptops, cell phones) to draw conclusions about the energy use of large data centers and systems like AI. e.g., one laptop charging uses 50 watts and an AI data center uses as much energy as 50 million laptops running together
3-5
20
Analyze the way categorical and numeric data shapes its interpretation and analysis.
6-8
30
Recognize that numerical variables may be either discrete or continuous.
6-8
30
Ask questions regarding the origins of specific automated measures (e.g., webtracking, email meta-data, user accounts).
6-8
30
Recognize the limits of the information the data can provide and the story it can tell.
6-8
30
Recognize that conclusions may need to be revised in the future as more knowledge and data become available.
6-8
30
Make sense of the variability of data through an iterative process of refinement by questioning.
6-8
30
Specify ways that data provide incomplete information relative to the object being studied.
6-8
30
Approach data and evidence-based claims with reasonable skepticism and apply the process of evaluating the validity of claims while remaining open-minded.
6-8
30
Describe in plain language how AI uses and builds upon data in multiple ways. e.g., AI systems identify patterns in data by processing thousands of input-output pairs, and the system adjusts its internal mathematical model to minimize error, enabling it to predict outputs for new inputs such as a spam filter
6-8
30
