Critical aspects to working with data according to Rubin (2020)
By: Kristin Hunter-Thomson
Andee Rubin put together an accessible and timely commentary for the January 2020 special issue in The Journal of Learning Sciences focused on “Situating Data Science: Exploring How Relationships to Data Shape Learning”. In it she articulates what she calls “five critical aspects of working with data” based on learning sciences research and statistical education research to date. The aspects are:
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Context
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Variability
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Aggregate
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Visualization
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Inference
Rubin’s belief is that these 5 aspects are key components to working with and making sense of data no matter what the context or task at hand is with the data. She also posits that these aspects are critical regardless of what technology you are using, or that may become available in the future.
Through well crafted descriptions of each aspect, Rubin identifies why it is important and provides examples of the need for each aspect with examples from the literature. If you are looking for a quick read on key things to keep in mind when teaching students with data I would highly recommend reading Rubin’s “Learning to Reason with Data: How Did We Get Here and What Do We Know?”.
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Variability:
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Embracing Variability (Partners in Data Literacy)
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Visualization:
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Inference: Drawing Conclusions & Making Inferences resources
