So far the Data Portfolio has gone really well. Students are excited about their data sets and questions. Today we moved on to using these data sets to explore the questions they posed last class.
|Number Crunching on the Eniac|
Today I also realized I have been worrying about this too much. I was coming at this as a math major that hated statistics and absorbed as little of it as possible. I was worried about all the high end statistical analysis they need to do. Really I was making it too complicated.
By giving them some positive experiences with data early in the unit, and steering the process of asking questions and selecting a data set they were in a position to handle the computation part of it with minimal trouble.
I have also realized there are some institutional barriers to getting this portfolio done. Some things teachers really need to think about ahead of time:
- Computer Equipment and software. Due to budget cuts we still use Office 2003. Students have had difficulty with some of the larger data sets and Excel.
- Internet filtering: Many data sets were blocked by our school filters. Also sites like Google Docs are blocked, which is making collaboration between students difficult.
- Limited storage space on the school network makes it hard to store the datasets.
- Many of my students do not have Internet access at home. In addition they live an hour or more away from school and staying after is not possible for many families. This is limiting their ability to collaborate outside of school. I am having to provide a lot of class time for them to write.
|Bumpass Virginia - a real place|
(You can see all the CS Principles documents here)
Here is the listing of what we did:
- Work with Partner: today they started looking at their data and answering their questions. Today was a half day due to Kindergarten registration so we only had 45 minutes. Several groups are having to come back to the lab to finish up.
- The prompt for the Data Portfolio rubric is:
apply computational tools and techniques to answer your questions, e.g., by finding patterns in the data, by transforming or translating the data, or by finding connections between the data and other sources of knowledge