Web application

School Management System (SMS) Project Documentation

Overview

The School Management System (SMS) is an integrated platform designed to manage various aspects of an educational institution efficiently. The system encompasses user management, course and class management, admissions, attendance, grading, communication, financial management, library resources, examination management, and statistical analysis, including predictive analytics for student success.

## Key Features

1. **User Management**

- **Administrators**: Complete control over the system.

- **Teachers**: Access to classes, grades, and attendance management.

- **Students**: Access to courses, grades, and assignments.

- **Parents**: Monitor children's performance and attendance.

2. **Course and Class Management**

- Creation and management of courses.

- Assignment of courses to teachers.

- Timetable and classroom management.

3. **Admissions and Enrollment Management**

- Online registration process.

- Management of student records.

- Automation of admissions and payments.

4. **Grading and Evaluation Management**

- Entry of grades by teachers.

- Automatic calculation of averages and rankings.

- Access to report cards for students and parents.

5. **Attendance Management**

- Recording of attendance by teachers.

- Automatic notifications to parents for unexplained absences.

- Attendance reports for administrators.

6. **Communication and Notifications**

- Internal messaging between students, parents, teachers, and administrators.

- Email/SMS notifications for important events, exams, and assignments.

- Announcements and newsletters.

7. **Financial Management**

- Tracking of tuition fee payments.

- Generation of receipts and invoices.

- Financial reports for administrators.

8. **Library and Resource Management**

- Management of book loans and returns.

- Online catalog of available resources.

- Online reservation of books and materials.

9. **Examination Management**

- Scheduling of exams.

- Creation and publication of exam timetables.

- Entry and publication of results.

10. **Reports and Statistics**

- Generation of customized reports (academic, financial, attendance, etc.).

- Analysis of academic performance and trends.

- Dashboards for administrators.

## Technologies and Tools

1. **Backend**

- **Languages**: Python (Django), JavaScript (Node.js), PHP (Laravel).

- **Databases**: PostgreSQL, MySQL, MongoDB.

- **API**: RESTful API for integration with other systems.

2. **Frontend**

- **Frameworks**: React.js, Vue.js, Angular.

- **UI/UX**: Use libraries like Material-UI or Bootstrap for a responsive and attractive user interface.

3. **Mobile**

- Development of mobile applications for Android and iOS (React Native, Flutter).

4. **Hosting and Security**

- Hosting on cloud platforms like AWS, Google Cloud, or Azure.

- Data security with SSL, encryption of sensitive data, and two-factor authentication (2FA).

## Development Plan

### 1. Research and Analysis Phase

- Understand the specific needs of the school.

- Analyze existing systems and identify gaps.

### 2. Design

- Create wireframes and prototypes for the user interface.

- Define the system architecture and database structures.

### 3. Development

- Iterative development with beta versions for testing.

- Integration of key functionalities as a priority.

### 4. Testing and Deployment

- Unit, integration, and system testing.

- User testing to validate the interface and functionalities.

- Gradual deployment with monitoring of performance and bugs.

### 5. Maintenance and Updates

- Ongoing technical support.

- Regular updates to add features and improve security.

## Best Practices

- **Intuitive User Interface**: Easy to use for all users, including those who are not tech-savvy.

- **Data Security**: Protect sensitive student and school information.

- **Scalability**: Design the system to handle a large number of users and data.

- **Customization**: Provide customization options to meet the specific needs of each institution.

- **Multi-Platform Support**: Ensure compatibility with various devices and operating systems.

## Statistical Analysis and Prediction

### Statistical Analysis Features

1. **Attendance Analysis**

- **Absence Rate by Class and Student**: Calculate and display the percentage of absences for each class and student.

- **Gender Analysis**: Compare absence and presence rates between boys and girls.

- **Visualization**: Bar charts, line charts, and pie charts to visualize attendance data.

2. **Performance Analysis by Subject and Student**

- **Average Scores by Subject**: Calculate the average scores for each subject.

- **Overall Pass Rate**: Percentage of students passing in each subject.

- **Gender Comparison**: Compare the performance of boys and girls in each subject.

- **Visualization**: Graphs and tables to display academic results.

3. **Comparative Analysis**

- Compare academic performance across different classes, subjects, and periods (terms, years).

### Predictive Success Algorithm

1. **Data Collection and Preparation**

- **Data Used**: Scores by subject, absenteeism rates, performance histories, etc.

- **Preparation**: Clean and normalize data to make it suitable for machine learning algorithms.

2. **Algorithm Selection**

- **Linear Regression**: To predict future scores based on past scores and absenteeism.

- **Random Forest**: For more robust and complex predictions.

- **Neural Networks**: For more precise predictions with larger and more complex data sets.

3. **Algorithm Implementation**

- **Training**: Use historical data to train the algorithm.

- **Validation**: Test the algorithm with validation data to evaluate its accuracy.

- **Deployment**: Integrate the algorithm into the application for real-time predictions.

## Technologies and Tools for Analytics

1. **Backend**

- **Language**: Python is ideal for data processing and machine learning.

- **Machine Learning Frameworks**: Scikit-learn, TensorFlow, PyTorch.

- **Database**: PostgreSQL or MySQL for storing school data.

2. **Frontend**

- **Frameworks**: React.js, Vue.js, Angular for creating an interactive user interface.

- **Visualization Libraries**: D3.js, [login to view URL], Plotly for displaying interactive graphs and tables.

## Development Plan for Analytics

### 1. Research and Analysis Phase

- Identify available data and specific needs for statistics and prediction.

### 2. Design

- Define system architecture and database structure.

- Create wireframes for statistical and predictive features.

### 3. Development

- **Backend**: Implement APIs for data processing and analysis.

- **Frontend**: Develop interfaces to display statistics and predictions.

- **Machine Learning**: Train and validate predictive models.

### 4. Testing and Deployment

- Test statistical features and predictions with real data.

- Deploy the application and monitor performance and prediction accuracy.

### 5. Maintenance and Updates

- Update algorithms and visualizations based on user feedback.

- Improve predictive models with additional data and advanced techniques.

## Examples of Visualizations

1. **Absence Rate Graphs**

- Stacked bars to compare absences by class and gender.

2. **Academic Performance Curves**

- Line graphs showing the evolution of average scores by subject over time.

3. **Dashboards**

- Interactive dashboards with filters to visualize specific statistics for a class, student, or period.

## Example Code for Simple Analysis in Python

```python

import pandas as pd

import [login to view URL] as plt

from sklearn.linear_model import LinearRegression

# Load data

data = pd.read_csv('[login to view URL]')

# Calculate average scores by subject

mean_scores = [login to view URL]('subject')['score'].mean()

# Visualize average scores by subject

[login to view URL](kind='bar')

[login to view URL]('Average Scores by Subject')

[login to view URL]('Subject')

[login to view URL]('Average Score')

[login to view URL]()

# Predict success with linear regression

# Assume we have columns 'attendance' and 'past_scores' for training

X = data[['attendance', 'past_scores']]

y = data['future_scores']

model = LinearRegression()

[login to view URL](X, y)

# Prediction for new students

new_data = [login to view URL]({'attendance': [90, 85], 'past_scores': [80, 75]})

predictions = [login to view URL](new_data)

print(predictions)

Science des données Machine Learning (ML) Design de site internet Développement de logiciel Administration de Bases de données

Nº du projet : #38361477

À propos du projet