1. Objective: Develop a learning assistant that recommends resources and tracks student progress.
2. Focus: Tailor recommendations based on user input using simple algorithms.
3. Tools: Python, scikit-learn, educational content APIs.
1. Install Python: Ensure Python is installed on your system.
2. Set Up Virtual Environment: Create a virtual environment to manage dependencies.
3. Install Required Libraries: Install scikit-learn and other necessary libraries.
1. Identify Sources: Find educational content APIs that provide learning resources (e.g., Khan Academy API, EdX API).
2. User Data: Collect or simulate student data such as preferences, past performance, and learning goals.
1. Clean the Data: Preprocess the user data to remove any inconsistencies or errors.
2. Feature Engineering: Create features from the user data that can be used by recommendation algorithms (e.g., subject preferences, performance metrics).
1. Content-based Filtering: Recommend resources similar to those the student has liked or used before.
2. Collaborative Filtering: Use user interaction data to recommend resources based on similar users' preferences.
1. Data Storage: Use a database (e.g., SQLite) to store user interactions and progress.
2. Update Progress: Function to update the student's progress in the database.
Develop a Simple Interface: Use Flask to create a web interface for interaction.
1. Simulate User Interactions: Test the system by simulating user interactions and tracking their progress.
2. Collect Feedback: Gather feedback to improve the recommendation accuracy and user experience.
1. Test with Common Phrases: Validate the tool by translating common phrases and cultural texts.
2. Collect Feedback: Gather feedback to improve the tool's accuracy and usability.
1. Host the Tool: Deploy the learning assistant on a cloud platform (e.g., AWS, Heroku).
2. Monitor and Update: Continuously monitor the tool's performance and update the algorithms as needed.
1. Host the App: Deploy the app on a cloud platform (e.g., AWS, Heroku).
2. Monitor and Update: Continuously monitor the app's performance and update the model and information database as needed.
