Machine Learning for Personalized Education: Optimizing Learning Outcomes among Diverse Online Learner Populations in Ghana Using Reinforcement Learning

The rapid growth of online learning platforms in Ghana offers increased educational access but faces serious challenges in addressing the very diverse needs of the learners (Agbe et al, 2022). Traditional models often does not help with individual learning styles, digital literacy levels, and infrastructure constraints leads to suboptimal outcomes. Machine learning (ML), particularly reinforcement learning (RL), presents a promising approach to personalize education by tailoring content to individual learner profiles. The aim of this study is to develop an RL-based framework to optimize learning outcomes for diverse online learner populations in Ghana. This seeks to address various challenges such as varying digital literacy, limited technology access as well as cultural differences by dynamically adapting learning paths using ML techniques (Asabere & Mends-Brew, 2021).