Optimising Content Recommendations in Context‐Aware Mobile Learning Platform Through Machine Learning

In today’s mobile communication society, the integration of mobile technology in education has revolutionised learning through m‐Learning. This approach leverages wireless mobile technology and computing, enabling learners to access educational resources anytime and anywhere, thereby enhancing flexibility and freedom. Mobile devices, such as smartphones, tablets and PDAs, facilitate m‐Learning by providing mobility and interactive learning environments. M‐Learning is characterised by its personalised, collaborative, and ubiquitous nature, offering learners context‐aware experiences tailored to their immediate surroundings and needs. Context awareness plays a pivotal role in m‐Learning, distinguishing it from traditional education by dynamically adapting content delivery based on factors beyond just location, including time, network conditions and user preferences. This adaptability poses significant challenges in designing effective teaching strategies that meet diverse learner requirements. To address these challenges, this study explores the application of machine learning techniques—specifically “Artificial Neural Networks (ANN), K‐Nearest Neighbours (KNN) and Adaptive Neuro‐Fuzzy Inference Systems (ANFIS)”—in developing a Context‐Aware m‐Learning platform. Performance comparisons based on RMSE, MAE and accuracy metrics reveal ANFIS as the optimal method for enhancing the proposed m‐Learning system, aligning with the contextual demands and parameters defined for effective mobile education. ANFIS’s ability to minimise absolute prediction errors more effectively than the other methods. In terms of accuracy, ANFIS again leads the performance metrics, achieving an accuracy of 82.46% with 10 neurons. In comparison, ANN and KNN achieved accuracies of 81.17% and 80.74%, respectively. These accuracy values indicate that ANFIS not only reduces prediction errors but also consistently delivers higher predictive accuracy. This research contributes to advancing the field by providing insights into leveraging machine learning for adaptive and context‐aware educational technologies, thereby optimising learning experiences in today’s mobile‐centric educational landscape.