Learners can then have adaptive access to interactive course materials and relevant services on various devices (e.g. mobile devices, computers and even Smart TVs) via a seemless frontend.
AI is one of the key drivers of adaptive learning. Analyzing usage data leads to a better understanding of the learning process and thus, optimized content, teaching and learning thereafter. For instance, in digital learning environments, real-time analyses of students’ interactions with learning objects provides vital information about content validity, teaching methods and personal learning habits. All user interactions (for example, with learning objects) are persisted as ADL xAPI or IMS CALIPER statements in a learning record store. To address the evolution of sustainable software architectures, FOKUS researchers developed the Common Learning Middleware (CLM), a field-proven enabler for interoperable microservice infrastructures. This service mediator is strongly recommended in order to manage AI services, data storage, content databases and adaptive user interfaces.