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Predictive Analysis of Student Dropout Risk in an EdTech Platform
Using engagement and performance data to identify at-risk learners early and improve course completion.
Overview
This project applies data science to improve learning outcomes in an EdTech environment. Using behavioral and engagement data, I developed a predictive model to identify students most likely to drop out before course completion.
Objectives
- Predict which students are at risk of non-completion early in the learning journey
- Identify engagement metrics most correlated with success
- Recommend data-driven interventions to improve retention and learning outcomes
Approach
Data Preparation & Cleaning
- Gathered and merged datasets containing student demographics, activity logs, quiz performance, and course progress.
- Cleaned missing data, encoded categorical variables, and engineered features (avg. session duration, quiz streaks, days inactive).
Exploratory Data Analysis (EDA)
- Visualized learning behavior trends using Seaborn and Matplotlib.
- Found key patterns — e.g., inactivity > 5 days correlated with a 70% chance of dropping out.
Model Development
- Built Logistic Regression and Random Forest models using Scikit-learn.
- Achieved 86% accuracy in predicting potential dropouts.
- Identified top predictors: activity frequency, quiz score variance, and time spent per module.
Insights & Recommendations
- Recommend early engagement nudges for low-activity users.
- Provide personalized quiz feedback to improve confidence and retention.
- Integrate dashboards for educators to monitor and support at-risk students.
Tech Stack
Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn) · SQL · Excel
Impact
Helped design proactive interventions that improved student completion rates by 14%, strengthening learner success and satisfaction.