Objective

To develop a machine learning-based system that predicts the likelihood of students dropping out of courses or programs by analyzing academic performance, engagement metrics, attendance, and demographic data, enabling proactive interventions.

Key Features

 Student Panel:

• Secure login to view personal academic dashboard

• Track progress, attendance, and engagement metrics

• Receive personalized recommendations to reduce dropout risk

 Instructor / Mentor Panel:

• View risk predictions for students in their courses

• Monitor at-risk students through dashboards and alerts

• Suggest corrective actions, additional support, or mentoring sessions

 Admin Panel:

• Upload and manage student academic data

• Configure parameters and thresholds for dropout prediction

• Generate detailed reports on risk levels, trends, and departmental statistics

• Track effectiveness of intervention programs

Tech Stack

Workflow (Step-by-Step)

1. Data Collection

• Gather historical data:

  • Academic performance (grades, GPA)

  • Attendance records

  • Engagement metrics (course participation, login frequency)

  • Demographic information (age, socio-economic factors)

2. Data Preprocessing

• Clean and normalize data

• Handle missing or inconsistent values

• Encode categorical variables (one-hot encoding / label encoding)

• Split dataset into training and test sets

3. Model Training

• Select ML algorithms: Logistic Regression, Random Forest, XGBoost, or Neural Networks

• Train models on historical data to classify students as “at risk” or “not at risk”

• Tune hyperparameters using cross-validation

• Evaluate model using metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC

4. Prediction & Risk Scoring

• Apply trained model to current student data

• Assign a dropout risk score to each student

• Categorize students: Low Risk, Medium Risk, High Risk

5. Dashboard & Alerts

• Display risk scores in an interactive dashboard

• Instructors and admins receive alerts for high-risk students

• Recommendations for interventions (tutoring, mentoring, counseling)

6. Optional Advanced Features

• Real-time risk score updates based on continuous engagement

• Predictive analytics to forecast risk trends over semesters

• Integration with Learning Management Systems (LMS)

• AI-based suggestions for personalized learning plans