Search Results
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.
Identify how issues in data, such as bias, missing data, and errors, can affect the output of an AI tool and the training of an AI tool from the input-output pairs it learns from. e.g., If AI only sees pictures of cats in sunlight, it would fail to recognize cats in shadows
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Describe how social groups can be inadequately represented by existing data and data schemes. e.g., city planners using traffic data collected from weekday commuters overlook nighttime workers' needs, such as poor bus schedules for nurses on night shifts
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Acknowledge that options and choices are available for data collected about individuals, and recognize that what is gathered or excluded can have consequences. e.g., a survey claiming "most teens love math" is biased if only the math club members completed the survey
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Identify how biases in data affect inferences and questions.
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Analyze how data is used to solve problems, persuade, and discover new ideas.
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Investigate real-world questions by cleaning, analyzing, and interpreting data to draw conclusions.
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Recognize that the investigative process is non-linear, often cycling between phases in various orders multiple times.
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Revise initial conclusions when new data emerges and use evidence to support claims.
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Explain data interpretations from various disciplinary and community perspectives (e.g., social studies, families).
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Embed data practices into everyday life and advocate for the benefits of doing so.
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Assess the accuracy, perspective, credibility, and relevance of various resources (e.g., information, media, data).
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Informally identify anomalies and outliers in a distribution of data and make an informed decision as to whether those observations should be removed or filtered out for analysis.
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Use categorical variables or bins/groups of numerical variables in a dataset to restructure data into groups.
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Make sense of and use a dataset arranged in nested or hierarchical format.
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Use existing numerical variables to create bins or groups based on benchmark values appropriate for the context, or bins based on numerical ranges (e.g., 0-4, 5-10, 11-15, etc...).
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Create a new variable from an existing variable that transforms (e.g., uses a formula to convert units of measure) or recodes data (e.g., blue-->B, red--> R).
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Use summary measures of groups within a nested or hierarchical dataset.
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Construct data-based questions that explore relationships between variables and consider how data collection methods affect the quality of evidence.
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Use data generated from simulations and models to investigate a question of interest.
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Deploy or trigger sensors or automated data collection methods and use the generated data to investigate a pre-defined problem or question.
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Apply an appropriate data collection plan when collecting primary data or gathering secondary data for the question of interest.
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Evaluate data limitations and generalizability, including which questions can and cannot be answered with available data.
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Recognize the role of random assignment in experiments and its implications for cause-and-effect interpretations.
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Analyze potential sources of bias and error in collection processes and evaluate their impact on findings.
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Search for and retrieve appropriate datasets from educational repositories and curated sources designed for middle school investigations.
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Evaluate potential datasets based on relevance, timeliness, and credibility of the source for answering specific questions.
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Use metadata and documentation to understand the context and limitations of secondary datasets.
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Create an ordinal scale of measurement.
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Understand that data is information collected and recorded with a purpose.
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Distinguish between human-derived data from images, sounds, and text vs. computer-derived data from images, sounds, and text.
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Consider how the data were measured, with what tool and precision.
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Consider who collected these data and for what purpose.
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Consider when and where the data were collected.
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Design data collection methods that address privacy, consent, and fair representation of different groups.
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Examine historical examples of harmful data practices to inform ethical data use.
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Identify and handle missing values marked by special codes (-99) or blank cells.
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Distinguish between true zero values and blank cells.
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Work with datasets that include rates and derived variables that combine multiple measurements.
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Work with datasets that have multiple variables that can suggest or answer different questions.
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Work with datasets that show natural variation and understand why values differ.
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Work with datasets with up to 20 variables and over 100 observations.
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Understand how categorical variables can be used to create meaningful subsets.
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Work with datasets where the row isn't a single observation but something more complex (e.g., average, nested cases).
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Work with datasets that include derived or transformed variables, including creating categorical variables from numerical data.
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Understand how categorical variables can be used to create meaningful subsets.
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Identify measures of center as statistical values that represent the central tendency of data sets.
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Explain what measures of center are useful for and their limitations.
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Categorically identify the presence of potential outliers in a dataset.
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Describe whether data is symmetric or asymmetric and the number of modes.
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Generate a frequency table to summarize raw categorical data.
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Numerically measure missing data.
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Recognize the difference between the absence of data, and "zero."
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Comprehend, in an informal sense, the value of information contained in metadata (e.g., data and time, text, continuous, geolocation).
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Use reasoning about distributions to compare two groups based on quantitative variables.
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Represent the variability of numerical variables using appropriate displays (e.g., dotplots, boxplots).
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Employ complex graphs (e.g., bar graphs, line graphs) and basic statistical concepts (e.g., mean, median, mode) to describe patterns and identify trends, similarities, and differences within data.
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Create scatterplots and add line of best fit.
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Compare word frequencies across multiple texts to identify patterns and create simple visualizations from that text data.
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Explore patterns in audio data (e.g., analyzing sound waves for volume and frequency).
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Learn to use simple diagrams (e.g., decision trees using small relatable examples) to make important decisions for everyday choices.
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Identify probabilistic processes that simulate various forms of categorical variability, including uniform and normal distributions. e.g., spinner, dice, random draw
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Illustrate variability in a dataset by determining how key descriptive features are represented.
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Evaluate how visualizations, models, or predictions account for variation at an appropriate level.
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Use visualizations (e.g., box plots) to compare variability across datasets.
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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.
Consider both context and the characteristics of a dataset to determine whether a given data point is reasonable. e.g., meaningful outliner, erroneous outlier
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Relate sources of variability to domain-specific phenomena as described in the relevant domain standards. e.g., Next Gen Science Standards, Mathematics Common Core State Standards, C3 Framework
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Conceptualize how the output of AI models such as LLMs vary along a variety of dimensions.
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Determine how labeling happens and how it affects the variability of the output of models. e.g., training set that labels dogs vs. cats, consider connections to bias
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Summarize data across multiple categories using a digital tool.
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Create single variable visualizations using a digital tool.
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Identify relationships and patterns using a digital tool.
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Describe how digital tools can be used to provide equitable access to learning experiences.
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Select a no-code or low-code digital tool that is suited for the intended task.
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Explore the basics of block coding in data investigation processes.
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Explore the basics of block coding in data analysis processes.
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Explore why it’s important to present data in multiple formats to ensure all learners can understand it.
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Explore how digital tools support diverse learners to analyze data. e.g., immersive readers, speech-to-text, translators, sonification
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Discuss how the lack of accessible digital tools can exclude people from participating in data analysis.
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Explore how relationships in data connect characteristics, including patterns like increasing, decreasing, or no connection.
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Recognize that models simplify complex systems and have limitations.
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Recognize that relationships in data do not always imply causation. e.g., ice cream sales and shark attacks both increase in the summer, but one doesn't cause the other
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Identify relationships between variables and represent them using tables, graphs, or diagrams (e.g., decision trees, flowcharts).
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Use simple mathematical or computational models (e.g., statistical summaries, spreadsheet formulas) to describe patterns and relationships in data.
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Test and refine models by comparing predictions to actual data values.
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Express a finding and quantify your confidence in it by stating the degree of certainty regarding the result. e.g., I am highly confident that a majority of students in my area ride the bus to school, based on separate sources' estimations of 60%, 62.3%, and 65% of students ride the bus to school
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Recognize that an assumption should change somewhat, but may not need to change entirely, based on the "strength of" or degree of confidence in new evidence.
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Connect previous assumptions about a problem to the level of certainty in a finding by using the terms “prior assumption,” “new data/evidence," and “my updated assumption.”
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Numerically compare the impact of two events with different magnitudes and probabilities to determine which scenario is preferable. e.g., financial problem; 10% chance of receiving $100 vs. 50% chance of receiving $50
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Recognize and describe random chance in a given situation, and explain whether a result is unusual by comparing it to what is expected from random chance. e.g., flipping a coin 10 times and getting 8 heads is less common than 5 heads and 5 tails, but still possible due to random chance
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Recognize that a unique result may be considered significant if it is substantially different from outcomes in similar situations.
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Recognize that a unique result may be considered significant if it falls far from the typical range of outcomes in a visualized distribution of results.
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Evaluate how different sampling methods impact the accurate representation of a population and their ability to generalize findings to other groups.
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Assess how sample size impacts the accuracy of estimates representing population characteristics.
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Identify the sources of potential bias in a sample or population, and describe how bias may impact the results of an investigation.
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Describe what it means for an event to be likely or unlikely using probability. e.g., probability of 0 is unlikely, 1 is very likely, 1:2 is neither likely or unlikely
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Assess simple data to identify potential associations between two variables while considering that correlations do not imply causation and may arise from unobserved factors.
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Using graphical displays, identify and categorize the type of potential relationship between pairs of numerical variables with terms such as independent, dependent, and covariate.
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Explain why randomization is an effective way to reduce other potential influences, and as a result, successfully isolate the impact of an independent variable.
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Distinguish direct vs. inverse relationships in multivariate data, such as associations between two categorical groups within the same visualization.
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