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Building Blocks for Data Literacy: Essential Ideas
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This work is in development by K. Hunter-Thomson & M. Schauffler (CC4.0, 2021), Creative Commons Attribution-ShareAlike 4.0 International License
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*Provide feedback at: https://forms.gle/RcXFC6xMyehRPbXq5 Last updated 7/17/21
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Essential Ideas offer a sense of purpose, function, and context for data tasks that students might undertake in a classroom.
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RealmsFunctionsTasksEssential Ideas
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Get DataAsk Questions & Consider Possible OutcomesConnect data, questions, and expectationsData are observations and measurements collected systematically in a context and for a reason. Questions and expectations may precede or follow data collection; the three are interdependent in a shared context.
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Formulate questions in a given contextQuestions drive the process of working with data. Questions may form from curiosity (Which design works best?), process (How should we measure?), or interpretation (What does the pattern mean?). Statistical questions anticipate variability in data, and invite reasoning to explain an answer, whereas factual questions anticipate a single correct answer.
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Generate new questionsInvestigations often give rise to new questions. Asking new questions and revising old ones, when needed, are part of what drives the process of inquiry.
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Generate DataDesign investigationsInvestigations are designed to address a particular question or purpose. They are designed to collect samples in a way that reasonably represents a whole population or phenomenon.
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Collect dataMeasurements that are made and recorded systematically according to a plan are more likely to produce a repeatable (and thus more convincing) result. The level of precision and accuracy in measurement should be appropriate for the question and the context.
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Quantify DataIdentify cases & valuesA 'case' refers to an instance when data are collected from a single sample or situation, such as weather data collected at a particular moment in a particular place. Values are the actual measurements.
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Acknowledge attributesAttributes (sometimes called variables) are aspects of a population or situation that are measured, such as date, time, location, temperature. Attributes may be quantitative measures or categorical aspects, such as site name.
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Specify unitsUnits specify the scale of measurement. Units of measure are necessary to know in order to make legitimate comparisons and to understand the scale of the situation.
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Organize & Process DataArrange data in tablesTables (hand drawn and digital spreadsheets) organize measurement values in cells according to attributes (columns) and cases (rows). A table designed for data collection may be structured differently than a table designed for data visualization and analysis.
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Interact with tables dynamically Data tables can be reorganized according to need. Digital spreadsheets can be sorted, searched, filtered, or transformed to enhance options for exploring a given question.
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Explore DataVisualize DataUnderstand the structure of graphs and mapsGraphs and maps are tools to visualize patterns in data that are not easy to see in a table. Data values are placed along a single scale, along two scales (XY), or in geographic space (lat/long, with a map underlay). Points or areas can be highlighted in color or symbols to indicate values or categories.
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Create data visualizationsCreating graphs and maps (by hand and digitally) involves deciding what kind of graph or map to make in a given situation, drawing the axis/axes, scaling the axis/axes, and plotting values accurately.
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Filter, simplify, or transform data to reveal patternsCalculate statistical valuesStatistics are used to summarize properties of data. For example, 'average' represents what is typical for a group, but it does not show how much the group varies. It is important to understand what statistics show and what they do not show about the data.
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Compute or transform attributesSometimes it is strategic to transform attributes (e.g., to a different scale) or derive a new one (e.g., ratio between two attributes) to reveal patterns related to the question at hand.
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Describe & Analyze PatternsRecognize & describe variabilityPopulations and phenomena vary naturally. Thus, data that are collected about them are also expected to vary. How (and how much) a group varies reveals important information about the nature of the group or phenomenon.
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Describe visual patternsPatterns may be strong or weak, or there might be smaller patterns within a larger pattern. Descriptors for patterns include: increasing, decreasing, abrupt, stable, cyclic, noisy, flat, twice as much as, shifted, symmetrical, skewed...or no pattern.
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Do things to reveal or highlight patternsEvidence presented in graphs and maps can be emphasized by highlighting or labeling important patterns or points, identifying thresholds or meaningful ranges for reference, or adding a descriptive title or caption.
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Model (quantify) patternsModeling patterns quantitatively communicates the scale of a pattern and what it means in the context of the data. For example, models can quantify a range of values that are typical for a group, the strength of a relationship, the rate of change taking place, or whether a difference is statistically significant or not.
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Infer Meaning from DataInterpret Data to Learn SomethingInterpret patterns in context
(e.g., engage questions, conjectures, claims, predictions, & models to think statistically)
What a pattern means has to do with the scale of the pattern, how the pattern relates to the question, and the relationship of one pattern to other patterns in the data. Interpreting patterns with a statistical mindset engages questions, conjectures, claims, predictions, and models.
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Justify an interpretationData can usually be interpreted in more than one way. Justifying an interpretation means explaining how patterns and other features of a graph or map support the interpretation, and how the interpretation relates to the question or context of the data.
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Infer broader meaningHumans collect data to gain understanding of the world. Yet any investigation measures just a small part of that world. What can be inferred about a wider context from the results of that investigation? What do results suggest about a bigger picture, beyond what was measured or analyzed?
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Articulate UncertaintyRefer to uncertainty when reasoning about dataA given collection of data comes from a subset of a phenomenon or a population. Therefore, some degree of uncertainty is inherent in any claims, forecasts, or inferences made from exploring the data. Degree of uncertainty is important to consider.
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Evaluate limitations of data and claimsFactors that contribute to limitations of data (and thus uncertainty) may include sampling (selection, how many, timing), methods of measurement or analysis, natural variability, or unmeasured confounding factors.
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Use or Build on New KnowledgeDecide what to do with new information Data are collected to gain new information for a purpose, such as to understand more about how something works, to predict future events, to improve a policy or design, to inform decisions, dialog, or a new investigation, or to satisfy curiosity. What will you do with what you learned?
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Follow through with an action (e.g., communicate findings, justify a decision, propose a new investigation)Transforming data to action might lead to sharing findings with peers or community, developing an evidence-based argument to support a decision or idea, proposing a new investigation, redesigning and retesting a policy, model, or device, adopting a change in behaviour or strategy, telling a story, or simply raising new questions.
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