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.

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.