Leveraging Q-Learning Models within a Web-Based OBE Platform for Enhanced Student Learning Outcome Verification

Abstract

Outcome-Based Education (OBE) is increasingly recognized as a powerful tool for aligning teaching with intended learning outcomes. In this work, a web-based platform is presented that integrates OBE principles with Q-Learning model which is a reinforcement learning technique to verify and enhance student learning. The motivation for this system came from observing the challenge that educators face in adapting instruction to meet diverse learner needs in real-time. This platform enables educators to define the learning objectives and monitor the student performance against them. What makes this approach distinct is the adaptive learning engine powered by Q-Learning model. This analyses student data and dynamically adjusts the content delivery for personalized learning. The proposed work translates the OBE principles into a web-based platform which not only assesses the learning outcomes but also actively contributes to improving them. Early experiments conducted in controlled classroom settings showed measurable gains in student comprehension and retention. The accessibility and adaptability of the proposed web-based platform make it a promising tool for institutions aiming to scale personalized learning without compromising on quality. The web-based OBE platform offers a hands-on-approach to implement OBE digitally, which gives educators a real time insight and ability with adjusting their teaching methodology with respect to the student needs.