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.

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.

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.

Learning videos enjoy great popularity in a digitalized world, especially since their use is usually possible regardless of time and location. Learners use this advantage mainly in self-study. Supervision, as for example in classroom teaching, is rather difficult and learners are usually left to their own devices when learning obstacles arise. However, not treating learning obstacles can have serious consequences, ranging from a gradual loss of the learners’ motivation to the termination of the learning project. Consequently, learning obstacles must be identified and treated in order to support an efficient learning process. Fortunately, a digital learning environment opens up many opportunities to support learners automatically. This paper explains an approach to identify potential learning obstacles in video learning based on indirect feedback. The first part of the approach to removing learning obstacles in learning videos is based on an analysis of learners' click interaction within a video to identify potential problem areas. Building on this, the second part provides first thematically relevant ad hoc video recommendations for the potentially identified learning obstacle. In order to verify whether there is actually a learning obstacle, the third part explicitly induces learners to give indirect or direct feedback on whether the recommendations have helped them and, consequently, whether they have removed an actual learning obstacle.

Teleoperation is a fundamental feature for the development of autonomous vehicles. Not only offers it the possibility of an efficient fallback solution for emerging situations that the autonomous vehicle cannot resolve itself but also it potentially offers new business models for transportation service providers. Fundamental to these considerations, however, is the performance of the communication network and the resulting parameters such as latency and bandwidth. These quantities have a critical impact on the ability of a human to perceive the environment, assess the situation and remotely control the vehicle safely and reliably. In this paper, a framework for simulating and evaluating the impact of network parameters on the performance of a remote human driver is presented. Using an example scenario of a shuttle bus route, the effects of latency for the teleoperation use case is evaluated. The local network and the impact on the human driver are modeled in SUMO (Simulation of Urban MObility) and OMNEST. The latency network simulation is validated against real measurement data. The car-following model Extended Intelligent Driver Model (EIDM) and its human driver model are extended so that latency effects can be quantified. The proposed approach has multiple advantages, on the one hand, it allows to evaluate the network suitability for the teleoperation use case. On the other hand, it limit the number of variables to be evaluated in e.g. user studies.

This article deals with the digitalization of management accounting / controlling in insurance companies, which goes hand in hand with the digital transformation of the insurance industry by Big Data, Artificial Intelligence (AI) and Blockchain. The insurance business requires an industry-specific design of the controlling instruments, but not of the controlling concept itself. Managing insurance as a service in a value- and risk-oriented way requires cost transparency, e.g. through contribution margin calculations. Risks, on the other hand, can only be understood from a balance sheet perspective, e.g. through internal models. These interdisciplinary fields of application of controlling are undergoing digitalization. In addition, there are new market developments such as telematics tariffs, in which the digitalization of controlling is essential in order to address the limits of insurability. The fields of application result in new competence profiles of controllers in distinction to actuaries and data scientists.

Interior insulation plays a key role in reducing the energy consumption of historic buildings. However, it might cause moisture accumulation
and must be thoroughly analyzed. The use of recycled materials allows for further reduction of environmental impact. This paper presents
the study of a new insulating plaster containing aerogel and recycled glass used as capillary active interior insulation system. Firstly, the
hygrothermal properties of the material are measured in laboratory to obtain a complete characterization. Laboratory tests results are post-
processed to obtain the data required as input by the simulation software. Finally, hygrothermal simulations are carried out to investigate
the material’s behavior in realistic application scenarios and to study how different input parameters affect the results.