When you create a new topic in this category, you should include the following sections. For an example of what your topic should look like, see the Cereal Nutritional Information topic.
If you use data that someone else posted, we highly encourage you to reply in that topic with your experience using that data in the classroom.
Data Source
Include a link to the original data source. You can also upload csv files (4 MB limit) as attachments.
Data Description
This section should briefly introduce the data.
Data Provenance and Purpose
This section should answer the following questions:
- Who collected the data?
- When was it collected?
- How was it collected?
- Why was it collected?
Variable Names and Descriptions
For data in rectangular format (e.g., csv files), this should be a bulleted list:
- Column Name: what the column represents
For other data (e.g., images, text, unstructured data), this section may not be useful.
Classroom Uses
This section should describe specific ideas for using this data in the classroom as part of either a lecture/demonstration/lecture or a student-centered activity.
We would like you to address the following sub-sections, in keeping with the PIPE-LINE tenets:
Data Science Content
What data science content do/can you cover with this data? Describe specific techniques (e.g., “linear discriminant analysis”, not just “classification”) for which this data works particularly well or highlights a potential stumbling block.
Content-with-Context
What do students need to understand about the context of the data before working with it? What would a real data scientist do with this data, and how can you imitate that in a classroom setting?
Culturally Responsive Pedagogy
How can lessons using this dataset incorporate these four elements of culturally responsive pedagogy within the data science classroom?
- Share authority
- Engage and value identities
- Support deep learning (the student kind, not the ML kind)
- Assess concept mastery, real-life implementation, and reflection