Adaptive Learning Environment Reference Architecture for an Optimised Learning Process

In higher education, learners become increasingly heterogeneous as they differ in, e.g., knowledge levels, competences, learning styles and media preferences. It is difficult for instructors to cater for these differences individually in face-to-face courses due to the effort for individualized supervision which does not scale for larger groups. One solution is to supplement face-to-face contact with tailored learning materials for use outside of regular physical classes. However, learners often do not know which material is most appropriate for them, which is why individualised recommendations are necessary. This paper outlines the reference architecture of an adaptive digital learning environment and how it interfaces to other independent information systems, e.g., to retrieve student data which is available anyway. The digital learning platform offers personalised recommendations for learning elements, such as learning videos or quizzes, based on the behaviour and individual characteristics of the learner. It combines AI technologies with a didactic foundation based on self-directed learning. The reference architecture fulfils previously gathered requirements and integrates the platform well in an existing software landscape due to its flexible design.