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). The methodology employed a qualitative approach, utilizing the Open University Learning Analytics Dataset (OULAD) with pre-processing to account for Ghanaian learner demographics. The RL framework leveraged on Markov Decision Process (MDP) with Q-learning and Deep Q-Networks (DQN) in order to model learner states, actions, and rewards (Baker & Inventado, 2014). Features were engineered to reflect local contexts, such as connectivity fluctuations and mobile learning prevalence. The RL model successfully converged on personalized learning paths for three learner profiles: Visual Learners, Textual Learners, and Assessment-Oriented Learners. The Q-learning algorithm optimized content delivery (video, text, quizzes) across five learning levels, achieving higher cumulative rewards for tailored content strategies, with Visual Learners preferring videos, Textual Learners favouring text, and Assessment-Oriented Learners excelling with quizzes.