Modelling learning experiences in adaptive multi-agent learning environments

In next generation technology-enhanced learning environments, intelligent educational systems can benefit from tapping into multi-agent, adaptive, gamified learning experiences, which transform the traditional instructional paradigm from classroom-based learning to personalised learning in any setting, whether collective or individual. Such settings enable learning targeted to each individual's learning styles and needs, through the use of autonomous technological agents as actuators of the learning process. Learning components which will respond to the needs of such an educational framework should provide capabilities for adaptive, affective and interactive learning, automatic feedback and automatic assessment of the learners' behavioural state. A novel methodology is proposed to model such components, which focuses on the representation and management of learning objects (LOs) for any educational domain, any type of learner and learning style and any learning methodology, while fostering non-linearity in the educational process. This methodology is supported by a strategy for modelling and adapting re-usable learning objectives, coupled with an ontology that enables scalable and personalized decision-making over learning activities on autonomous devices, enabling dynamic modularisation of learning material during the learning process.