📘 Student Attention Prediction System

A machine learning system that models and predicts student attention levels using behavioral simulation and regression modeling.

---

📌 Overview

This project builds a student attention modeling and prediction system using:

• Behavioral simulation
• Synthetic dataset generation
• Machine Learning regression
• Visualization
• Flask-based web interface

The system models how a student’s attention varies during the day based on practical real-world factors such as sleep schedule, activity type, fatigue, environmental distractions, and biological focus cycles.

The goal is to:

• Simulate realistic student attention data
• Train a machine learning model
• Predict attention levels
• Recommend optimal study hours

---

🎯 Objectives

• Model individual student attention using hidden behavioral variables
• Generate realistic datasets using practical assumptions
• Train a machine learning model to predict attention
• Identify best study times for students
• Provide visualization through graphs and a web interface

---

🧠 Core Idea

Student attention is influenced by multiple factors:

Personal Capacity

Hidden individual ability to focus.

Sleep Schedule

Sleep duration and timing affect next-day attention.

Activity Type

• Textbook study → low distraction
• Mobile study → high distraction
• Coding / revision → moderate distraction

Fatigue

Long continuous study reduces attention while breaks help recovery.

Environment

Certain hours naturally have higher distractions.

Circadian Rhythm

Biological focus peaks during late morning hours.

⚙️ Dataset Generation

The dataset is generated using practical behavioral rules.

Parameters Included

• day
• student id
• hour
• activity type
• continuous study hours
• capacity (hidden variable)
• sleep start & end
• sleep duration
• sleep factor
• environmental factor
• biological factor
• fatigue factor
• final attention %

Behavioral Rules

• Attention drops after long study sessions
• Mobile usage causes distraction spikes
• Breaks reduce fatigue
• Late sleep reduces morning attention
• Attention peaks naturally around late morning

---

🤖 Machine Learning Model

Model Used

Random Forest Regressor

Input Features

• hour
• activity
• continuous study duration
• sleep factor
• environment factor
• biological factor
• fatigue factor

Output

Predicted attention percentage.

---

📊 Features Implemented

• ✅ Synthetic realistic dataset generation
• ✅ Hidden-variable attention modeling
• ✅ Regression model training
• ✅ Attention prediction
• ✅ Best study hour recommendation
• ✅ Daily attention graph visualization
• ✅ Flask-based web interface

---

🚀 How to Run

1️⃣ Generate Dataset

python generate_attention_dataset.py

Creates:

attention_dataset.csv

2️⃣ Train Model

python train_model.py

Creates:

attention_model.pkl

3️⃣ Run Flask App

python app.py

Open in browser:

http://127.0.0.1:5000

📈 Output Examples

• Daily attention graphs
• Predicted attention values
• Recommended best study hour

🔬 Future Improvements

• Real student data integration
• Deep learning models
• Personalized study recommendations
• Mobile app interface
• Real-time attention tracking

👨‍💻 Authors

• Siddharth
• Raghav
• Kishor Kumar
• Shri Vishwa D