We only had half a class today. As a part of the CS Principles Pilot we have to do pre and post surveys about the course and that ate up a lot of teaching time.
Their activity for the rest of class was to start forming questions around data.
Data is Big Idea III: Data and information facilitate the creation of knowledge.
A. People use computer programs to process information to gain insight andknowledge.B. Computing facilitates exploration and the discovery of connections ininformation.
- Computers can be used to find patterns in, and test hypotheses about, digitally represented information.
- Insight and knowledge can result from translating and transforming digitally represented information.
C. Computational manipulation of information requires consideration ofrepresentation, storage, security, and transmission.
- Big Data (use of large datasets) provides new opportunities and new challenges for extracting information and knowledge.
- Scalability, of systems and analytical approaches, is an important consideration when datasets are large.
- Metadata can increase the effective use of data or a dataset by providing additional information about various aspects of that data.
- There are trade-offs involved in the many possible ways to represent digital and non-digital information as digital data.
- Data is stored in many formats depending on its characteristics—such as size and intended use—so that it can be manipulated computationally.
Again, be careful. Only one of the Key Concepts mentions Big Data. There is a lot of talk about the Big Data aspects of CS Principles. Yes, the students need to work with big data as a part of the portfolio, but not everything in data needs to be BIG.
(You can see all the CS Principles documents here)
We are on Srpring Break next week, so I am trying to get them ready so when we get back they can finish the Data Portfolio item.
Today I had them work with a partner with the crime data set from last class and come up with five questions they might answer with the data.
Talk about unexpected results. I had assumed it was obvious what kinds of questions we could answer with a data set. How many times have you done this as a teacher? Made an assumption about what they knew and then were totally surprised by the results.
This is one of the major benefits of regular journaling as a part of the course. It helps you spot these underlying assumptions early and correct them.
Having them write out questions very quickly showed who was on the tight track and who needed some more direction. In general I got two types of questions.
First type of question:
- Is the funding for the local police departments increasing or decreasing within the small cities?
- What types of violent crimes were committed?
Second type of question:
- Does higher amounts of college education result in lower crime rate?
- Does an increase in annual police funding relate to educational level?
See the difference between the two?
Many of the students were asking questions for more data, or information not given in the spreadsheet.
This shows that we need to talk a little bit before we start this part of the unit about what kinds of questions can and cannot be answered with data sets. The first set of questions are really more for reporters, not statisticians. Our focus here is really looking for connections and correlations between the data already collected.
And think about what would have happened had we started right into the data portfolio. That process asks them to develop questions then find an appropriate data set to answer those questions. Clearly I need to include some instruction on what types of questions are appropriate.
Next class is a half day so I will only see them for 45 minutes. we will spend the time really looking at the questions they will develop as they start on their portfolios after break.
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