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.

Abstract Learning is at the heart of every progress the human species makes. It is most effective when it considers who we are as individuals, what learning approach we prefer and what we already know to begin with. In the digital age, we strive to capture such information in the form of a digital representation—the so-called learner model—, to tailor learning-related systems to this information and build upon it to create more personalised learning experiences. Over recent years, the proliferation of diverse models across various educational applications and disciplines has made it challenging to access targeted research. In this survey, we aim to address this gap, reviewing the latest advances in learner modelling and conducting a comprehensive analysis of the existing approaches, focusing on developments from 2014 to 2023. With the help of a systematic literature review (SLR), we want to provide designers and developers of learner models with a structured overview and simplified entrance into the topic and the field of learner models. We investigate the question: What do learner models look like and how are they filled, kept up to date and used? To this end, we analyse and classify existing approaches. Our findings provide a comprehensive and structured overview of the field of learner modelling, allowing researchers to navigate and understand the diverse approaches more easily and providing developers of learner models or adaptive systems with a practical tool to access relevant information according to their needs.

Students in higher education tend to become increasingly heterogeneous groups of learners. This is due to different levels of prior knowledge or competences, diverse learning styles, differing affinity to (digital) media, and other factors. Learner-centred education needs to cope with that heterogeneity in order to make specific learning offers to the individual learner. This is difficult in physical classes where the coaching effort cannot be increased without limitation. This paper presents an individualized digital learning environment, iLE, that is intended to be used as an additional learning aid that supplements physical classes. iLE provides recommendations of learning material such as learning videos targeted to the specific needs of individual learners. The paper presents the technical approach behind iLE, in particular a combination of data- and knowledge-driven artificial intelligence techniques, as well as the didactical underpinning of iLE.