Smart Academic Productivity Analyzer (SAPA) is a Java-based command-line application designed to help students manage their academic tasks while analyzing their productivity patterns.
Unlike traditional task managers that only organize tasks, SAPA goes a step further by tracking time spent on each task and generating meaningful insights such as productivity score, subject-wise effort distribution, and time-based performance trends.
Students often create study plans but fail to track how effectively they use their time. This leads to:
- Poor time management
- Lack of awareness of study patterns
- Inefficient allocation of effort across subjects
SAPA addresses this problem by combining task management + time tracking + productivity analysis into a single system.
- Add new academic tasks
- View all tasks
- Delete tasks
- Mark tasks as completed
- Start and stop study sessions
- Automatically calculate time spent on tasks
- Productivity Score calculation
- Most studied subject
- Least studied subject
- Longest duration task
- Average study time
Categorizes productivity into:
- Morning (5 AM – 12 PM)
- Afternoon (12 PM – 5 PM)
- Evening (5 PM – 9 PM)
- Night (9 PM – 5 AM)
- Saves task data to a file
- Loads data automatically on startup
- Java (Core)
- Object-Oriented Programming (OOP)
- Java Collections Framework (ArrayList, HashMap)
- File Handling
- Exception Handling
The project follows a modular architecture:
- Task Class → Represents task data
- TaskManager → Handles task operations
- AnalyticsEngine → Generates insights
- FileHandler → Manages data storage
- Main Class → CLI interface
- Java JDK installed (version 8 or above)
- Clone the repository:
git clone https://github.com/your-username/SAPA.git- Navigate to project folder:
cd SAPA- Compile the program:
javac Main.java- Run the application:
java Main- Run the application
- Choose options from the menu
- Add tasks with subject and priority
- Start and stop study sessions
- View analytics to understand your productivity
1. Add Task
2. View Tasks
3. Start Study Session
4. Stop Study Session
5. View Analytics
6. Delete Task
7. Exit
Productivity Score: 75%
Most Studied Subject: DSA
Most Productive Time: Evening
Average Study Time: 2.5 hours
- Object-Oriented Programming (Encapsulation, Modularity)
- Data Structures and Collections
- File Handling for persistent storage
- Exception Handling for robust execution
- Real-world problem modeling
- Implementing accurate time tracking
- Managing file-based data persistence
- Designing meaningful productivity insights
- Handling invalid user inputs
- GUI using Java Swing or JavaFX
- AI-based productivity recommendations
- Mobile or web-based version
- Cloud storage integration
Ayank Kumar Giri Registration Number: 24BAI10030 Course: Programming in Java Institution: VIT
This project is for academic purposes only.
Feel free to fork, modify, and improve this project.