E-Learning App Using Image Processing, Detecting, Ranking and Progress Monitoring for Preschoolers

Abstract

With the world rapidly adapting to online education, preschoolers are left behind to cope with this new approach to learning. In reality, preschoolers require more engagement and interaction; they require hands-on activities/exercises to stimulate their learning progress. As a solution, the author came up with an e-learning application that implements machine learning and image/text recognition for a subtle and fun way of learning. The application has three basic learning activities such as the alphabet, numbers, and color activity. For the alphabet and number activity, the application will simply take the student’s hand-drawn input from the built-in canvas, run it through the image processing/recognition and instantaneously allocate points for the student based on their answer. The color activity is slightly adventurous, where the students will take pictures of a particular color based on the given question, this allows the students to wander around their surroundings making it more interactive and fun. The application additionally includes the student analysis function, which is a detailed student management system with the capability to track student progress and identify their interest to find out their strengths and weaknesses in a particular subject based on the points and activity involvements. The application also has an in-app monitoring and ranking system along with individual student profiles that make student management easier for a larger group of students in a learning center or kindergarten.