E-LEARNING PLATFORM
EXPERTISE
YEAR
Timeline
From explorations to final designs in 5 weeks while working with multiple projects at the same time
Background
Education in Northern Kenya is hindered by socio-economic challenges, poor internet connectivity, and a lack of adequate resources, particularly in nomadic communities. Existing digital learning initiatives failed to meet these challenges due to limited scalability, insufficient content personalization, and a lack of data-driven insights.
This project addressed these gaps by combining modern technology frameworks with robust predictive capabilities to provide an adaptable solution tailored to the needs of students and teachers in resource-limited settings.
Research & Planning
The project began with a detailed examination of previous digital learning initiatives and extensive stakeholder consultations. Insights from educators and local communities helped shape the platform’s objectives and functional requirements.
Design & Prototyping
Wireframes and user interfaces were created to ensure a user-friendly experience. Feedback from educators was incorporated to refine features like dashboards, content displays, and course management systems.
Implementation
The platform was built using React for the frontend and Node.js with Express.js for the backend. MongoDB was used as the database for managing structured and unstructured data, while predictive analytics algorithms were implemented using Python libraries.
Testing & Optimization
Rigorous testing was conducted across devices and user scenarios to ensure compatibility, performance, and reliability. Predictive models were evaluated for accuracy using synthetic data.
Offline Functionality
Service Workers and Progressive Web App (PWA) frameworks ensure that users can access educational content even without internet connectivity. The use of IndexedDB for local storage allows for offline caching and syncing once connectivity is restored.
Predictive Analytics
Predictive models were developed using Python libraries such as Scikit-learn and TensorFlow. These models analyze user behavior and activity patterns to identify students at risk of dropping out, enabling timely intervention. MongoDB’s flexible schema capabilities supported storing and querying activity logs efficiently.
Interactive Learning Tools
React’s dynamic component rendering was employed to create engaging quizzes and interactive assignments, while Chart.js was used for real-time data visualization of student performance metrics. Backend APIs built with Node.js ensured smooth data exchange and processing.
Increased Efficiency
Predictive analytics identified at-risk students with an accuracy of 85%, enabling timely intervention by educators. This resulted in a 30% improvement in student retention rates during the testing phas
Positive User Feedback
High user satisfaction ratings and positive reviews highlight the app's intuitive interface and powerful AI capabilities.
Growing User Base
The app quickly gained traction among individuals and homeschooling in Kenya, with a steady increase in user adoption and engagement.