In today's digital age, many educational tools are static, one-size-fits-all solutions that fail to engage students dynamically. As education increasingly moves online, students often find it difficult to locate tailored content that meets their individual learning needs. This is especially true for younger learners who might need quick, digestible lessons on core subjects like math, English, science, or history. Our project seeks to solve this issue by creating an intuitive, personalized educational app that mimics the style of platforms like TikTok or YouTube Shorts but focuses on delivering targeted educational content.
The Problem: Students often struggle to find learning resources that cater to their specific learning levels and needs. While platforms like YouTube offer educational content, they lack the personalization and curation needed to keep students engaged and progressing in their studies. The algorithms in entertainment apps are well-suited for keeping users hooked on content, but they are rarely designed for educational purposes.
Our Solution: Our app combines the best of both worlds—an interactive, algorithm-based content feed like TikTok, but with a focus on delivering quality educational videos. The app will recommend videos to students based on their past preferences and feedback, allowing them to engage with subjects in a way that keeps them interested and motivated to learn.
Video Categorization: We start by preloading a list of videos from YouTube or other educational sources, categorized by subjects like math, history, science, and English. Using transcripts and metadata, each video is analyzed and tagged for easier retrieval based on subject and difficulty level.
Content Delivery: The app will deliver a stream of these videos to the user's device in a format that can be easily consumed, such as a mini-player. This creates a seamless, user-friendly interface for navigating educational content, keeping it interactive and short-form.
Feedback-Driven Personalization: The app's recommendation engine is driven by user feedback. If a student thumbs-up a math video, the system will recommend another math video from the pool. If they give a thumbs-down, it will offer content from a different subject. This allows for a dynamic and personalized learning experience that adapts as the student interacts with the app.
Why It Matters: Our app not only makes learning more accessible but also ensures that students are receiving content tailored to their learning progress. By combining the engagement techniques of popular social media platforms with targeted educational content, we aim to help students of all learning levels improve and retain information in a fun, personalized way.
Our project streamlines the discovery of educational videos and takes a data-driven approach to content recommendation, solving the problem of static, uninspiring educational apps.
Yekalaivan: Full-Stack Developer & Project Manager
Sree: Back-End Developer & Research Analyst
Galina: Back-End & AI Developer
Tanishka: Full-Stack Developer & UX/UI Designer
- Thanks to Forest Hacks Hackathon for the inspiration and platform to build this project.
- Special thanks to the creators of the libraries and tools we used.