JoWUA

Abstract

The growing use of mobile learning in wireless and ubiquitous computing environments has posed various difficulties in managing the cognitive load of learners in a dynamic environment. Traditional adaptive learning approaches may not be equipped with the capacity to monitor continuously the learner's cognitive state and adapt intelligently to the environmental and behavioral changes. This problem could lead to a lower level of engagement of learners, increased cognitive load, and inefficiency in the learning process. In order to solve such problems, this research paper presents the Neuromorphic Computing-Enabled Context-Aware Adaptive Mobile Learning Framework for Real-Time Cognitive Load Management. This approach consistently monitors contextual factors such as the user's engagement pattern, environmental conditions, and device-level operations to predict the cognitive load and deliver the instruction accordingly. Neuromorphic intelligence was applied for cognitive processes that will be done with low delay and energy consumption in the wireless mobile context. The new learning model was tested with the help of various metrics, namely adaptation accuracy, precision, recall, F1-score, energy efficiency, and response latency. The experimental outcome revealed that the new framework obtained an adaptation accuracy rate of 96.8%, precision of 96.1%, recall of 95.7%, and F1-score of 95.9%, which is superior to traditional mobile learning and deep adaptive learning models. Furthermore, the proposed framework offered a reduction in response delay to 104 ms and an energy efficiency rate of 0.95. Results indicate that the integration of neuromorphic computing technology with context-aware adaptive learning systems can greatly improve learner personalization, cognitive load management, and system reactivity. The suggested framework is expected to contribute towards the design of an intelligent, scalable, and energy-efficient adaptive learning environment for future wireless-based learning platforms.