The Ed-Fi Alliance's sample data sets have realistic but fictional names, attached to realistic but fictional schools and local education agencies. Do these data sets unduly perpetuate any demographic biases or demographic skew with respect to key student performance indicators?
To this end, we have developed a Jupyter Notebook for performing rigorous statistical analysis on an ODS database:
A report on findings was presented at the Ed-Fi Alliance Summit 2022, focused on the Glendale data set.
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Requires Python 3.9 or 3.10
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Requires Poetry
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Clone this repository, install dependencies, and launch Jupyter Notebooks
git clone https://github.com/Ed-Fi-Exchange-OSS/Sample-Data-Equity-Analysis cd Sample-Data-Equity-Analysis poetry install poetry run jupyter notebook- ❗ The notebook uses IPyWidgets and does not work well in VS Code.
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A browser window will open with the Jupyter interface. Select the notebook there and follow the instructions in the notebook.
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Licensed under the Apache License, Version 2.0 (the "License").
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.