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, 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.

Learners in higher education tend to become an increasingly heterogeneous group. Paying proper attention to individual differences is a challenge that may be leveraged by individualized automated recommendations of learning elements. This presupposes some knowledge of the learners’ profiles which can be captured in so-called learner models. Yet, so far, there is no comprehensive overview of existing standards and their contribution related to learner models. This paper presents the results of a systematic literature research devoted to norms and standards in the area of learner models. As it turns out, 16 norms or standards have some relationship to learner models, 3 of them present their versions of a learner model. None of the standards and norms offers a comprehensive learner model, but in their entirety these models provide hints on reasonable contents and structure of learner models.

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.

Many curricula encompass subjects that are deemed less interesting or not important by a large share of students since they cannot perceive their true significance. It is an open question how students can be compelled to get involved with these subjects after all. This paper presents a novel concept how this can be accomplished. In particular, the paper argues that four important requirements must be met, namely that learning can also be accomplished in a less formal environment than regular lectures, learning may happen independent of physical presence at the university and whenever students see themselves fit, learning is based on small units, and students enjoy getting involved in the matter. As a proof-of-concept, this approach has been used in programming education for students of electrical engineering, based on sending short summaries via WhatsApp and adding playful elements. such as quizzes. An evaluation of the proof-of-concept over two terms provides indication of the viabi lity and usefulness of the approach, but also highlights several opportunities for extensions and refinements.