Access to Carnegie Learning's datasets is conditional upon agreement to the following terms:
You agree to use these data for academic research purposes only. You agree that these data and any analyses of these data will not be used in development or marketing of commercial products.
You will acknowledge Carnegie Learning in any publication resulting from this research and notify Steve Ritter (firstname.lastname@example.org) of any publications resulting from this research. Descriptions of the Cognitive Tutor® software can reference: Ritter, S., Anderson, J.R., Koedinger, K.R., & Corbett, A. (2007) The Cognitive Tutor: Applied research in mathematics education. Psychonomics Bulletin & Review, 14(2), pp. 249--255.
You will not attempt to determine the identity of any individuals (including both teachers and students) or schools represented in the dataset(s).
Any published results must not include the identity of any individuals (either students or teachers) or schools, whether such identities are determined from the data itself or from some other source.
Published data must be in summary or statistical form. You may not disclose or report on any individual student data, even if such data is anonymous.
You agree not to use these data to attempt to discover, implement or reverse engineer the Cognitive Tutor software or any functions of the Cognitive Tutor software.
You are the only person who will access these data. If you are collaborating with others who require access to the data, they must establish accounts with DataShoq and agree to these terms.
If you export data from any dataset(s) you will retain all copies of the data, and you will destroy these copies at the completion of the research project or at the request of Carnegie Learning. For the period of research, you may share exported data only with others who have also expressly agreed to these terms.
If your access to the dataset(s) includes web service access, you will take no actions jeopardizing the availability and accessibility of the dataset(s).
You agree to indemnify and hold harmless Carnegie Learning, its parent, affiliates, subsidiaries and their respective directors, officers, employees and agents of and from any and all claims, demands, losses, causes of action, damage, lawsuits, judgments, including attorneys’ fees and costs, arising out of or relating to your use and analysis of the Carnegie Learning dataset(s) or your violation of this Agreement.
To the extent authorized under the laws of your state, you agree to indemnify and hold harmless Carnegie Learning, its parent, affiliates, subsidiaries and their respective directors, officers, employees and agents of and from any and all claims, demands, losses, causes of action, damage, lawsuits, judgments, including attorneys’ fees and costs, arising out of or relating to your use and analysis of the Carnegie Learning dataset(s) or your violation of this Agreement.
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
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".
use listed on this page, you will be asked to agree to these terms before you can access the
all data in DataShop, including those projects for which there is a project-specific terms of