Development of a Randomized Quizzing System for Adaptive Learning

Abstract

This thesis presents the development and evaluation of a web-based randomized quiz system for pseudo-adaptive learning. The system demonstrates the limitations of traditional quiz platforms by adding features such as adaptive quiz generation, gamification with XP, levels, and badges, detailed performance analytics, and secure user management for both students and administrators. Developed using Python’s Flask framework and SQLite, the project uses an agile, user focused approach and prioritizes usability and engagement. Feedback increases motivation and improves learning outcomes. The thesis also discusses possible future improvements, such as more advanced adaptive algorithms, new question types, mobile app versions, and integration with learning management systems. Overall, this work shows that a simple gamified pseudo-adaptive quiz platform can be effective and scalable for self-paced learning.