Building Blocks for Data Literacy (v3, July 2021)

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1			Building Blocks for Data Literacy: Essential Ideas
2			This work is in development by K. Hunter-Thomson & M. Schauffler (CC4.0, 2021), Creative Commons Attribution-ShareAlike 4.0 International License
3			*Provide feedback at: https://forms.gle/RcXFC6xMyehRPbXq5 Last updated 7/17/21
4			Essential Ideas offer a sense of purpose, function, and context for data tasks that students might undertake in a classroom.
5	Realms	Functions	Tasks	Essential Ideas

6	Get Data	Ask Questions & Consider Possible Outcomes	Connect data, questions, and expectations	Data 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.
7			Formulate questions in a given context	Questions 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.
8			Generate new questions	Investigations often give rise to new questions. Asking new questions and revising old ones, when needed, are part of what drives the process of inquiry.
9		Generate Data	Design investigations	Investigations 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.
10		Generate Data	Collect data	Measurements 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.
11		Quantify Data	Identify cases & values	A '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.
12			Acknowledge attributes	Attributes (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.
13			Specify units	Units 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.
14		Organize & Process Data	Arrange data in tables	Tables (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.
15		Organize & Process Data	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.
16	Explore Data	Visualize Data	Understand the structure of graphs and maps	Graphs 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.
17		Visualize Data	Create data visualizations	Creating 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.
18		Filter, simplify, or transform data to reveal patterns	Calculate statistical values	Statistics 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.
19		Filter, simplify, or transform data to reveal patterns	Compute or transform attributes	Sometimes 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.
20		Describe & Analyze Patterns	Recognize & describe variability	Populations 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.
21			Describe visual patterns	Patterns 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.
22			Do things to reveal or highlight patterns	Evidence 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.
23			Model (quantify) patterns	Modeling 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.
24	Infer Meaning from Data	Interpret Data to Learn Something	Interpret 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.
25			Justify an interpretation	Data 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.
26			Infer broader meaning	Humans 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?
27		Articulate Uncertainty	Refer to uncertainty when reasoning about data	A 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.
28		Articulate Uncertainty	Evaluate limitations of data and claims	Factors 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.
29		Use or Build on New Knowledge	Decide 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?
30		Use or Build on New Knowledge	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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