In modern education, individualized learning environments play a vital role by allowing learners to tailor their learning paths based on personal needs, interests, and abilities. Achieving effective individualization relies on dynamic adaptation of the learning path, typically facilitated by recommender systems. These systems offer personalized suggestions, commonly employing content-based or collaborative filtering approaches. However, traditional recommender systems often lack consideration of the semantics of learning elements. To address this limitation, ontology-based recommender systems integrate semantic modeling, establishing additional connections within a domain to enhance precision and context in recommendations. Notably, these systems mitigate the cold start problem and are particularly advantageous in learning environments with limited data. While videos are prevalent in learning platforms, their unstructured nature poses challenges for processing. This paper introduces an innovative approach, leveraging Large Language Models, specifically GPT, to extract metadata from learning videos. The proposed method intelligently augments videos and links them to a domain ontology, enabling the integration of videos into ontology-based recommender systems. The application of this approach is demonstrated through a case study in software engineering education, showcasing its potential to enhance individualized learning experiences in specific domains. The presented method offers an automated alternative to manual video processing, aligning with the evolving landscape of education technology.

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.

Screen content images typically contain a mix of natural and synthetic image parts. Synthetic sections usually are comprised of uniformly colored areas and repeating colors and patterns. In the VVC standard, these properties are exploited using Intra Block Copy and Palette Mode. In this paper, we show that pixel-wise lossless coding can outperform lossy VVC coding in such areas. We propose an enhanced VVC coding approach for screen content images using the principle of soft context formation. First, the image is separated into two layers in a block-wise manner using a learning-based method with four block features. Synthetic image parts are coded losslessly using soft context formation, the rest with VVC. We modify the available soft context formation coder to incorporate information gained by the decoded VVC layer for improved coding efficiency. Using this approach, we achieve Bjontegaard-Delta-rate gains of 4.98% on the evaluated data sets compared to VVC.

Soft context formation is a lossless image coding method for screen content. It encodes images pixel by pixel via arithmetic coding by collecting statistics for probability distribution estimation. Its main pipeline includes three stages, namely a context model based stage, a color palette stage and a residual coding stage. Each stage is only employed if the previous stage is impossible since necessary statistics, e.g. colors or contexts, have not been learned yet. We propose the following enhancements: First, information from previous stages is used to remove redundant palette entries and prediction errors in subsequent stages. Additionally, implicitly known stage decision signals are no longer explicitly transmitted. These enhancements lead to an average bit rate decrease of 1.16% on the evaluated data. Compared to FLIF and HEVC, the proposed method needs roughly 0.28 and 0.17 bits per pixel less on average for 24-bit screen content images, respectively.

Die präzise Erkennung von Clear-Sky-Momenten ist für die Überwachung und Effizienzana-lyse von Photovoltaikanlagen von zentraler Bedeutung, da zu diesen Zeitpunkten definierte und model-lierbare Einstrahlungsverhältnisse herrschen. Es wird ein hybrides Modell zur verbesserten Erkennung von Clear-Sky-Momenten auf Basis von Einstrahlungsdaten vorgestellt. Hierfür wurden zunächst ma-nuell, dann mithilfe eines CNNs Merkmale aus den Einstrahlungsdaten gebildet. Eine Falls tudie mit Referenzdaten belegt, dass durch die Kombination dieser wissens-und datengetriebenen Methoden Clear-Sky-Momente zuverlässiger identifiziert werden können. Dadurch können Analysemethoden schneller und zuverlässiger Aussagen über die untersuchten PV-Anlagen treffen.

In order to cope with current disruptive technical and societal transformations, e.g. through digitalization or AI, new competences, commonly called future skills, are indispensable for everyday as well as for professional life. Many organizations, large and small, work on defining a set of future skills. This might imply that future skills are generic and identical across all professions. In contrast, it is a consensus in pedagogical research that generic competences are specifically shaped by the professional environment. Clearly, these two positions contradict each other. But what does this contradiction mean for future skills?
This research rests on the assumption that these context-sensitive generic competences including future skills are developed differently for each occupational field. In order to be able to offer target- and competence-oriented teaching, target competences must be known in the first place, taking into account the specific professional characteristics, currently and in the future.
This paper provides evidence that the initial assumption of context-specific competences is true by collecting and comparing qualitative research data. To do so, qualitative data was collected for different occupations, in particular software engineering and health care.
Our research shows that in fact each profession expresses competences specifically, and this applies to technical as well as non-technical competences. The details of relevant competences need to be identified and characterized as a prerequisite for being able to devise and offer competence-oriented learning approaches.

Digitalisation affects all areas of our private and professional lives, posing new requirements to cope with new technologies and the possibilities they offer. New competences are needed formastering these challenges. Consequently, digital transition affects learning substantially. One of the competences that gain increasing importance through digital transition is media literacy. This paper tries to answer the question of how we can devise learning settings that foster media literacy in higher education. It does so by analyzing media literacy itself on the one hand, and learning settings in higher education which addressmedia literacy on the other hand. Key findings are that media literacy is not precisely characterized yet and that learning settings often do not addressmedia literacy as a main competence goal. To that end, recommendations for developing suitable competence-oriented learning settings in media literacy education at universities are given.

Education in healthcare must enable professionals to work in the health sector for as long and as fulfilled as possible. Yet, new requirements are constantly
arising in the health sector, which continuously change the required competences, e.g. due to new technological possibilities and increasing
interdisciplinarity. Several challenges arise: education in healthcare needs awareness of required competences and their rapid change. At the same time,
addressing them in education presupposes an in-depth understanding of what they actually are.
To tackle these issues, a teaching concept was developed that builds on selfreflection of to-be professionals in healthcare. This concept includes characterizing typical professional situations and deducing required (future) competences for mastering these situations.
Beyond rising awareness to future skills, applying this teaching concept also yields data that support a better understanding of required competences and their importance across professions. A case study resulted in initial competence profiles for several professions in healthcare.

In order to cope with current disruptive technical and societal transfor-mations, e.g. through digitalization or AI, new competences, commonly called future skills, are indispensable for everyday as well as for professional life. Many organizations, large and small, work on defining a set of future skills. This might imply that future skills are generic and identical across all professions. In contrast, it is a consensus in pedagogical research that generic competences are spe-cifically shaped by the professional environment. Clearly, these two positions contradict each other. But what does this contradiction mean for future skills?
This research rests on the assumption that these context-sensitive generic competences including future skills are developed differently for each occupational field. In order to be able to offer target- and competence-oriented teaching, target competences must be known in the first place, taking into account the specific professional characteristics, currently and in the future.
This paper provides evidence that the initial assumption of context-specific competences is true by collecting and comparing qualitative research data. To do so, qualitative data was collected for different occupations, in particular software engineering and health care.
Our research shows that in fact each profession expresses competences specifically, and this applies to technical as well as non-technical competences.
The details of relevant competences need to be identified and characterized as a prerequisite for being able to devise and offer competence-oriented learning approaches.