Sample Selector

Sample Selector is a tool for creating and editing samples, or groups of data you compare across—they're not "samples" in the statistical sense, but more like filters.

By default, a single sample exists: "All Data". With the Sample Selector, you can create new samples to organize your data.

You can use samples to:

• Compare across conditions
• Narrow the scope of data analysis to a specific time range, set of students, problem category, or unit of a curriculum (for example)

A sample is composed of one or more filters, specific conditions that narrow down your sample.

Creating a sample

The general process for creating a sample is to:

• Add a filter from the categories at the left to the composition area at the right
• Modify the filter to select the subset of data you're interested in, saving it when done
• View the sample preview table to see the effect of adding your filter, making sure you don't have an empty set (ie, a filter or combination of filters that exclude all transactions).
• Name and describe the sample
• Decide whether to share the sample with others who can view the dataset
• Save the sample

The effect of multiple filters

DataShop interprets each filter after the first as an additional restriction on the data that is included in the sample. This is also known as a logical "AND". You can see the results of multiple filters in the sample preview as soon as all filters are "saved".

Read more about the Sample Selector

DataShop Home (not logged in)

Welcome to Datashop!

Log in to get started. DataShop supports InCommon and Google single sign-on (SSO) options. You will be able to link your DataShop account with your university id or Google account. The first time you log in you will be asked to create a DataShop account.

Datasets in DataShop are grouped into a few categories:

• Public Datasets are datasets you (or anyone) can access once you log in. You can use any of the DataShop tools on data in these datasets.
• Private Datasets are datasets that can only be accessed once you've gotten permission from the Principal Investigator (PI) of the project.

Projects are groupings of datasets. Each project name appears above its table.

• indicates a public project.
• indicates that papers that reference these data have been published by members of this project. Mouse-over the star to see the number of papers.

Dataset status, the last column of each dataset table, is often one of the following:

• complete: data collection is complete
• on-going: data collection is on-going
• files-only: there is no tutor interaction data in this dataset, only files and papers