As education has grown to rely more on technology, vast amounts of data has become available for examination and prediction. Logs of student activities, grades, interactions with teachers and fellow students, and more, are now captured in real time through learning management systems like Canvas and Edmodo. This is especially true for online classrooms, which are becoming popular even at the primary and secondary school level. Within all levels of education, there exists a push to help increase the likelihood of student success, without watering down the education or engaging in behaviors that fail to improve the underlying issues. Graduation rates are often the criteria of choice, and educators seek new ways to predict the success and failure of students early enough to stage effective interventions.
This project uses the following software and Python libraries:
- Python 2.7
- NumPy
- pandas
- scikit-learn (v0.17)
You will also need to have software installed to run and execute a Jupyter Notebook.
This project contains three files:
- student_intervention.ipynb
This is the main file where you will be performing your work on the project. - student-csv.csv:
The project dataset. You’ll load this data in the notebook.
In the Terminal or Command Prompt, navigate to the folder containing the project files, and then use the command:
jupyter notebook student_intervention.ipynb