This project analyzes the anonymised Open University Learning Analytics Dataset (OULAD). It contains data about courses, students and their interactions with Virtual Learning Environment (VLE) for seven selected courses (called modules). This data consists of six CSV files, which may be found here: https://analyse.kmi.open.ac.uk/open_dataset, along with metadata.
The project found in aje_OULAD_exploration.ipynb utilizes the following packages:
- Zipfile
- Matplotlib
- Seaborn
- Scikit-Learn
- StatsModels
The organization of the notebook is broken down into the following three parts:
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Part I: Descriptive Analysis
Help the audience understand the student body by creating descriptive analysis and highlight interesting trends among among various demographic cohorts.
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Part II: Data Preparation & Feature Engineering
Track student assessment submissions. Prepare an analysis to create a metric to calculate percentage of late submissions for each student registration.
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Part III: Predictive Analytics
Explore two predictive modelling approaches to help identify students who are at risk of failure.