Elearning Platform final project based .

Elearning Platform

1. Presentation outlines Abstract Introduction Motivation Problem Statement Objective Hardware/Software Requirements References

2. Abstract The "Personalized Language Learning Platform: Spoken Grammar & Feedback" project will develop a web tool for practicing spoken English. It offers real-time grammar correction and personalized feedback on fluency and pronunciation. Phase 1: Implements speech-to-text conversion and basic grammar checks with a simple interface. Phase 2: Adds advanced language processing and user profiling for more targeted feedback. The platform will include gamification, bite-sized lessons, interactive exercises, adaptive learning, and progress tracking to enhance the learning experience.

3. Introduction The "Personalized Language Learning Platform: Spoken Grammar & Feedback" project aims to simplify learning spoken English through an online tool. It will use APIs like Google Cloud Speech-to-Text and NLTK to convert speech to text, correct grammar mistakes, and provide instant, personalized feedback on fluency, pronunciation, and word choice. Stage 1 : Focuses on basic grammar correction and real-time feedback, with errors highlighted in the transcribed text. Stage 2 : Adds advanced features for detailed, progress-based feedback using sophisticated language processing techniques. The platform will also incorporate gamification, bite-sized lessons, interactive exercises, adaptive learning, spaced repetition, and audio-visual content. It will track user progress and include community features to enhance engagement and effectiveness in learning spoken English.

4. Motivation Learners face difficulties in language acquisition due to time constraints, lack of motivation, and poor tools. A solution is needed that provides flexible practice, engaging features, and real-time feedback to improve language learning outcomes.

5. Problem statement Despite the widespread desire among individuals to learn new languages, various factors such as time constraints, motivational challenges and lack of access to quality learning resources have led to significant impediments in the learning process. As a result, individuals give up learning which poses a critical need for effective solutions that can address these issues & help these learners achieve their goals.

6. Objective Gamification: Use game-like elements to boost motivation and achievement. Bite-sized Lessons: Offer manageable chunks of learning to reduce overwhelm. Interactive Exercises: Provide diverse activities to match different learning styles. Adaptive Learning: Personalize content based on individual strengths and weaknesses. Audio and Visual Content: Utilize native recordings and visuals for better comprehension. Progress Tracking: Allow users to track progress, set goals, and celebrate achievements. Community Features: Enable learners to connect, practice with native speakers, and build a supportive network. Grammar Correction : Offer real-time grammar correction with text and spoken suggestions for improving spoken English.

7. Hardware/Software Requirements Software Requirements :- Development Tools : IDE/Text Editor (VS Code) Backend : Node.js or Python, Web Framework (Express.js, Flask/Django), Database (MySQL/ M ongoDB) Speech-to-Text API : Google Cloud, Assembly AI NLP Libraries : NLTK Frontend : HTML, CSS, JavaScript, (or React.js, Angular) Text-to-Speech API : Google TTS Audio Processing : Web Audio API Hardware Requirements:- Development Machine: Intel i5/Ryzen 5, 8 GB RAM, 256 GB SSD Testing Devices: Desktop/Laptop, iOS/Android smartphones Network: Stable internet connection Audio Equipment: Quality microphone and headphones Server (for Production): AWS, Google Cloud

8. References Erradi , A., Nahia , S., Almerekhi , H., & Al- kailani , L. (2012). “ LingoSnacks : m-Learning Platform for Language Learning”. Department of Computer Science and Engineering, Qatar University, Doha, Qatar. Raj, N. S., & Renumol , V. G. (2021). “A systematic literature review on adaptive content recommenders in personalized learning environments” from 2015 to 2020. Sai, M. S. S., Nagalakshmi , L., Poojitha , M., Keerthana, L., & Kavya, L. V. (2022). “Customized learning strategies for students”. Ambele , R. M., Kaijage , S. F., Dida, M. A., Trojer , L., & Kyando , N. M. (2022). “A review of the development trend of personalized learning technologies and its applications” .