Adaptive Learning Systems Using Machine Learning and Web Technologies

Abstract

Adaptive learning systems transform education by offering tailored experiences, analysing learner behaviours in real time and dynamically customizing content delivery. This paper proposes a machine learning-driven adaptive framework, harnessing contemporary web technologies—including React for responsive UIs, Node.js for robust server-side logic, and TensorFlow.js for lightweight, client-side inference. At its core, the system gathers rich interaction data, encompassing task durations, pathway choices, mistake sequences, and involvement indicators, to construct detailed learner models. Employing sophisticated algorithms such as recurrent neural networks (RNNs) for time-series forecasting and gradient boosting ensembles for accuracy prediction, it identifies potential weaknesses and projects progress paths. This intelligence drives precise recommendations, adapting resources like interactive drills, enriched media, or progressive challenges to match each user’s skill profile. The framework’s design ensures heightened engagement via personalized nudges, elevated outcomes through focused interventions, and optimized efficiency over conventional e-learning setups. Ultimately, it promotes equitable, autonomous learning ecosystems, accommodating varied paces and styles to democratize high-quality instruction on a broad scale.