Prepare raw data so that it can be readily interpreted.
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Further Explanation:
Further Explanation:
The Prepare rung includes steps to transform raw data to make it readily interpretable. This can take various forms, including but not limited to:
- Making graphs or other visuals
- Aggregating the data to compute various statistics such as the Total, Average, Max, Min etc.
- Filtering data to look at a particular site, class, grade etc.
How does it relate to other rungs?
How does it relate to other rungs?
After the Collect rung we have raw data. In the Prepare rung we transform this raw data so that it can be readily interpreted. In later rungs we will use this now interpretable data to answer "what is happening?" and "why?".
Why is it important?
Why is it important?
When we have large amounts of raw data it is difficult to interpret the data before first preparing visuals, aggregating the data, etc.
What does it look like in the Data Collaborative?
What does it look like in the Data Collaborative?
Within the Data Collaborative, we have some systems to assist in the Prepare rung of the Data Ladder. These include reports in i-Ready, visuals in DataFit, Student Level and Family Reports in Homebase, Student Reading Outcome Reports etc. In these cases the steps to prepare data might look like finding the page and set of filters in DataFit that help you answer your specific questions.
For data visuals that are not prepared in one of these sources Google Sheets is a free resource that can help with data preparation. Some resources on how to perform some data preparation techniques in Google Sheets can be found in the Portal by navigating to Tools & Resources → Workshops → Google Sheets.
Data Collaborative Tools for Prepare:
- Common Assessment Systems:
- i-Ready System (K-3)
- myIGDIs System (Early Childhood)
- DataFit
- Homebase:
- Child/ Student Level Report
- Family Report (Early Childhood)
- Portal: Google Sheets Trainings
Scaffold
Scaffold
- What specific questions do I want to answer using data?
- Specify the Who What When Where of each question
- What does the raw data look like?
- What preparations do I need to make to the raw data to be able to readily interpret the answers to the questions from step 1.
Tools & Resources
Tools & Resources
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