Machine Learning

FeynMind: Your Learning Buddy

FeynMind is a mobile application designed to enhance science education for senior high school students in Indonesia. Inspired by the Feynman Technique, the app enables users to engage in simulated teaching sessions, promoting active learning through simplified explanations of complex topics. The application uses speech recognition to capture user input, provides feedback based on their responses, and presents relevant references from a curated knowledge base. By integrating machine learning and cloud computing, FeynMind delivers an engaging and interactive platform for improving cognitive skills, academic confidence, and science comprehension.

Table of Contents

• FeynMind: Your Learning Buddy
• Key Features
• Model Architecture
• Usage
• Results
• Contributing
• Acknowledgements

Key Features

• Speech Recognition: Uses Android’s SpeechRecognizer to capture and process user input.
• Interactive Feedback: Provides constructive feedback and follow-up questions based on the user’s explanations.
• Knowledge Base Integration: Displays relevant references to guide learning.
• PDF Upload: Allows users to upload textbooks and teaching materials.
• User Authentication: Secure login and registration system.

Dataset

Scientific Textbooks Dataset: High school-level science materials for creating a knowledge base and question generation.

Model Architecture

Machine Learning

• Speech Recognition: Pre-trained speech-to-text models fine-tuned with high school science terminology.
• Feedback Mechanism: Natural language processing (NLP) techniques to classify and analyze user responses.
• Question Generation: Sequence-to-sequence (Seq2Seq) models to generate relevant questions based on user input.

Cloud Services

• Serverless Functions: Handle voice processing and data delivery.
• API Integration: Facilitates communication between the mobile app and backend services.
• Logging and Monitoring: Ensures performance and cost management.

Usage

1. Prepare the dataset:

   • Place the dataset in the appropriate directory.

2. Train the model:

   • Open and run the provided Jupyter Notebook.
   • The notebook will guide you through data augmentation, model training, and evaluation.

3. Evaluate the model:

   • The notebook includes steps to evaluate the model using the test set and visualize the results.

Results

• Improved Engagement: Students reported higher levels of interest and understanding in science topics.
• Enhanced Performance: Preliminary testing showed a 15% improvement in comprehension scores.
• Positive Feedback: Users appreciated the intuitive interface and interactive teaching features.

Contributing

We welcome contributions to the Acnetify project. To contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes and commit them:

   git commit -m "Add new feature"

4. Push to the branch:

   git push origin feature-branch

5. Open a pull request.

Acknowledgements

• Bangkit 2024 Program: For providing mentorship and resources.
• Feynman Technique: For inspiring the teaching methodology.
• Open Source Libraries: Used in speech recognition, NLP, and Android development.

FeynMind is a collaborative effort to revolutionize science education and empower students with effective learning tools. We’re excited to see how this app will impact students’ academic journeys!