Abstract

The simplicity of the Internet-based learning platforms has led to a high demand for personalized and scalable digital education facilities. Nevertheless, most current e-learning solutions continue to be based on the delivery of static information, which leads to low levels of interaction of the learner and poor learning results. In order to overcome this shortcoming, the paper presents a context-sensitive adaptive learning model as a smart Internet service, which incorporates the behavior of learners, access context, and service-level feedback to facilitate the ongoing personalization of the services on both web and mobile platforms. The proposed solution puts forward a service-based architecture that is backed by an adaptive learning algorithm as well as a mathematical model of personalization decision making. The model is tested with the help of the simulation-based experimental design that compares the suggested adaptive learning service with the fixed Internet-based learning system. The quantitative findings show that the adaptive service enhances the average learning performance of 68.4 to 82.7 with a 14.3% point difference in performance, and the completion rate of 72.1 to 88.9. Incidence of learner engagement is also enhanced significantly, as the average score of the engagement is increased by more than 40 % points notwithstanding a difference between 0.57 and 0.81. The session-wise analysis also indicates that performance has increased as the first session created 64% and by tenth session it has increased to 88% as compared to the performance of the static system of 72%. This evidence confirms the idea that adaptive learning can be treated as an intelligent Internet service to increase the quality of educational learning, stability of engagement, and quality of the service. As a result, the proposed model is applicable to scalable digital education settings.