What is the DPIF?
The Data Practices Integration Framework is a tool for teachers who are interesting in integrating data practices in their currently existing curriculum. From a review of literature, 6 data characteristics and 5 data themes form 20 components of the framework each with their unique definition and suggested prompts for integration into lessons and tasks.
Why use the DPIF?
- Comprehensive but brief overview of key data practices
- Does not require teachers to learn/implement a curriculum from scratch
- Multipurpose tool for planning, scaffolding, and reflecting
- Complements teacher’s existing curriculum and resources
- Provides support AND agency
How to use the DPIF?
*Note that Steps 1 and 2 are interchangeable.
1. Look for materials in your lesson that intersect with data characteristics.
Examples for…
- Measures – investigative question, data collecting plan and procedures
- Variability – differences between two or more objects and/or data points
- Aggregate – mean, median, mode, sums of data, patterns
- Visualization – tables, graphs
- Inference – claims about a population, general statements about a phenomena
2. Consider skills in the lesson that relate to data themes.
Examples for…
- Methods – investigating a phenomena, conducting an experiment
- Technology – using a tool for information collection, processing, analysis, and presentation
- Communications – presenting findings, writing and explaining claims
- Ethics – considering questions of morality and fairness, considering how one or others may be affected by a phenomena
3. Use the suggested prompts associated with each characteristic-practice link to create activities and questions for the selected area of the lesson.
Click the GRAY links in the table to see the suggested prompts.
Watch this instructional video for how to get started!
| Evaluations | |||||
Context | Methods | Technology | Communications | Ethics | |
| Measures | Measures – Methods What are the procedures used to plan and execute measurements? | Measures – Technology What are the tools used to plan and execute measurements? | Measures – Communications How are measurement procedures communicated to audiences? | Measures – Ethics What ethical concerns are brought up given the measurements used? | |
| Variability | Variability – Methods How do investigative procedures create differences within data? | Variability – Technology How do the tools employed create differences within data? | Variability – Communications How are differences within data communicated to audiences? | Variability – Ethics What ethical concerns might be brought up given the differences within data? | |
| Aggregate | Aggregate – Methods What are the procedures used to create the sums and patterns seen in data? | Aggregate – Technology What are the tools that can be used to create sums and patterns seen in data? | Aggregate – Communications How are sums and patterns communicated to audiences? | Aggregate – Ethics What ethical concerns are brought up given the aggregate in data? | |
| Visualization | Visualization – Methods How might existing visualizations influence the design of the investigation? | Visualization -Technology What tools can help create the intended data visualizations? | Visualization – Communications What are the visuals communicating about the data? | Visualization – Ethics What ethical concerns are brought up given the visuals? | |
| Inference | Inference – Methods How do investigative procedures affect the inferences made? | Inference – Technology What tools can help in inference making? | Inference – Communications What is the general claim being made based on the data? | Inference – Ethics What ethical concerns are brought up given the inferences made? | |
References
- Drozda, Z., Johnstone, D., Van Horne, B. (2022). Previewing the national landscape of K-12 data science implementation. Commissioned Paper for the Workshop on Foundations of Data Science For Students in Grade K-12, Valhalla Foundation.
- Lee, H.S., Mojica, G.F., Thrasher, E.P., & Baumgartner, P. (2022). Investigating data like a data scientist: Key practices and processes. Statistics Education Research Journal, 21(2), 1-23.
- Rubin, A. (2020). Learning to reason with data: How did we get here and what do we know?. Journal of the Learning Sciences, 29(1), 1-11.
- Wild, C.J. & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International Statistical Review, 67(3), 223-265.
- Wise, A.F. (2020). Educating data scientists and data literate citizens for a new generation of data. Journal of the Learning Sciences, 29(1), 165-181.