There are several ways to continue developing and expanding this project. Here are some suggestions for extending the functionality and improving the system:
### 1. **Database Integration**
You can extend the project by integrating a database (like SQLite, PostgreSQL, or MySQL) to store the users' answers, scores, and track their progress over time.
#### Steps:
- Create a database schema to store user data and their responses.
- Update the `app.py` to interact with the database, storing answers and scores for each student.
- Allow users to log in and track their progress over multiple attempts.
Example for SQLite:
```python
Code: Select all
import sqlite3
def create_db():
conn = sqlite3.connect('adaptation_test.db')
c = conn.cursor()
c.execute('''CREATE TABLE IF NOT EXISTS results
(id INTEGER PRIMARY KEY AUTOINCREMENT,
username TEXT, group_score INTEGER,
academic_score INTEGER, teacher_score INTEGER, total_score INTEGER)''')
conn.commit()
conn.close()
```
Modify the `app.py` to store and retrieve results:
```python
Code: Select all
def store_results(username, group_score, academic_score, teacher_score, total_score):
conn = sqlite3.connect('adaptation_test.db')
c = conn.cursor()
c.execute("INSERT INTO results (username, group_score, academic_score, teacher_score, total_score) VALUES (?, ?, ?, ?, ?)",
(username, group_score, academic_score, teacher_score, total_score))
conn.commit()
conn.close()
```
### 2. **User Authentication**
Implement user authentication (login and registration system) to allow students to save their results and return later to view their progress.
#### Steps:
- Create a registration and login form.
- Store user credentials in a secure way (e.g., using password hashing).
- Protect certain pages (like the results page) to only allow registered users to access them.
You could use Flask extensions such as `Flask-Login` and `Flask-WTF` to implement authentication.
### 3. **Personalized Recommendations**
Based on the test results, you could provide personalized recommendations for each student. For example, if a student struggles with the academic adaptation, you could suggest specific resources (e.g., study guides, tutoring services, or time management tips).
#### Steps:
- Analyze the scores for each student in each category.
- Based on the score thresholds, provide personalized suggestions.
Example:
```python
Code: Select all
def get_recommendations(total_score):
if total_score < 10:
return "We recommend more engagement with group activities and academic support."
elif total_score < 20:
return "You are doing well, but try to engage more with the teachers for better academic performance."
else:
return "Great adaptation! Keep up the good work."
```
### 4. **Data Visualization**
To help students understand their results better, you could implement charts or graphs (e.g., pie charts, bar graphs) showing their scores in each category.
#### Steps:
- Use a library like `matplotlib` or `plotly` to generate visual representations of the student's results.
- Display the chart on the result page.
Example:
```python
Code: Select all
import matplotlib.pyplot as plt
import io
import base64
def create_chart(group_score, academic_score, teacher_score):
labels = ['Group Adaptation', 'Academic Adaptation', 'Teacher Adaptation']
scores = [group_score, academic_score, teacher_score]
plt.bar(labels, scores)
plt.xlabel('Adaptation Categories')
plt.ylabel('Scores')
plt.title('Adaptation Test Results')
# Save it to a BytesIO object
img = io.BytesIO()
plt.savefig(img, format='png')
img.seek(0)
img_data = base64.b64encode(img.read()).decode('utf-8')
return img_data
```
### 5. **Multi-Language Support**
Make the app available in multiple languages. This can be helpful if your student body speaks different languages. You can use `Flask-Babel` to implement localization.
#### Steps:
- Install Flask-Babel.
- Mark the text in templates and Python code as translatable.
- Create translation files for each language.
Example:
```bash
pip install Flask-Babel
```
Then, in `app.py`:
```python
Code: Select all
from flask import Flask, render_template, request
from flask_babel import Babel, _
app = Flask(__name__)
app.config['BABEL_DEFAULT_LOCALE'] = 'en'
babel = Babel(app)
@babel.localeselector
def get_locale():
return request.accept_languages.best_match(['en', 'es', 'fr'])
@app.route('/')
def home():
return render_template('index.html', questions=questions)
```
### 6. **Export Results**
Allow students to download their results in PDF or Excel format for further review or printing.
#### Steps:
- Use libraries like `ReportLab` for PDF generation or `openpyxl` for creating Excel files.
Example for PDF:
```bash
pip install reportlab
```
```python
Code: Select all
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas
def generate_pdf(result_data):
filename = "results.pdf"
c = canvas.Canvas(filename, pagesize=letter)
c.drawString(100, 750, f"Group Adaptation Score: {result_data['group_score']}")
c.drawString(100, 730, f"Academic Adaptation Score: {result_data['academic_score']}")
c.drawString(100, 710, f"Teacher Adaptation Score: {result_data['teacher_score']}")
c.drawString(100, 690, f"Total Score: {result_data['total_score']}")
c.save()
```
### 7. **Admin Dashboard**
For administrators or teachers, you could implement a dashboard to view all student results and track their adaptation progress. You can integrate it with user authentication and allow administrators to filter or export the data.
#### Steps:
- Create an admin section with login credentials.
- Display all students' results, and provide search and filter options.
Example:
```python
Code: Select all
@app.route('/admin')
def admin_dashboard():
# Admin only access
if not current_user.is_admin:
return redirect(url_for('home'))
# Fetch all student results from database
student_results = fetch_all_results()
return render_template('admin_dashboard.html', results=student_results)
```
### 8. **Advanced Test Analysis**
After collecting enough data, you could perform a deeper statistical analysis to identify trends, patterns, or areas where most students are struggling (e.g., grouping based on performance). You can use libraries like `pandas` for data manipulation and `seaborn` for statistical visualization.
Example:
```bash
pip install pandas seaborn
```
```python
Code: Select all
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
def analyze_results():
# Load data
df = pd.read_csv('results.csv')
# Correlation between adaptation categories
sns.heatmap(df.corr(), annot=True)
plt.show()
```
---