Cognitive-Inspired Autonomic Learning Models for Self-Regulating Digital Education

Abstract

Online teaching models are becoming more and more unable to support learner heterogeneity, changing mental states, lack of engagement and scalability. Traditional formalised or fixed personalization methods do not reflect the dynamic and context specific characteristics of human learning and cognitive self-regulation. Based on the neural and cognitive control processes as observed in human learning processes, this paper suggests a cognitive-inspired autonomic learning to be implemented on self-controlling digital learning systems. The proposed design incorporates a closed-loop autonomic structure that consists of perception, cognitive state representation, decision-making and adaptive control layers. The cognitive workload, level of attention, and mastery of knowledge are latent cognitive states that their learner interaction signs are continuously tracked and mapped into them. According to such inferred states, the system makes use of self-development of instructional strategies to continue the conditions of cognitively optimum learning whilst reducing the amount of overload and inattention. The simulations based on this model are also tested by the means of experimental procedures with the help of simulation and different learner profiles with different cognitive and behavioural traits. The performance is measured by efficiency in learning, stability in engagement, and robustness of the system and measured against the case of the non-adapting personalised basis and performance-driven adaptation bases. Findings also show the effect of stable benefits in learning outcomes and stability regardless of the situation, which reflects the advantages of the cognitive-led autonomic control. This paper establishes cognitive-inspired autonomic learning as a paradigm of the next generation intelligent digital education system that can span across the cognitive theory and adaptive system design as well as provide a scalable and self-regulating learning environment.