We present a remote diagnostic method that uses the IV measurement function of smart inverters and a so called self-referencing procedure to represent the performance of the PV generator vs. operating conditions irradiance and temperature (G and T). We have recorded 200 IV-curves of a PV string within a period of six months using a smart inverter. A deep autoencoder detected disturbed or IV measurements so that these were not included in the evaluation. The effective irradiance Geff at the PV string was determined, which was included in the evaluation instead of the measured irradiance. In addition, the cell temperature of the PV generator Teff was determined using physical models for the evaluation. As a result, we create smooth power-surfaces over Geff and Teff conditions in the range of 100–1100 W/m² and 15–90 °C. For validation, the performance data of the PV string were compared by indoor measurements with a calibrated flasher at standard test conditions. The approach offers a remote and real-time diagnostic by smart inverters. It is well suited for accurate power monitoring of PV generators or degradation or soiling tracking without the need for additional sensor capabilities. 

Keywords: Smart inverter IV tracing, IEC 61853-1, G–T performance matrix, Degradation monitoring, Soiling 

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.

In this work, the factors leading to string outages are examined, and an enhanced method for detecting faults at the substring

level is presented. Utilizing GPT4-o to analyze O&M reports of 5089 photovoltaic plants, we classified outages

according to the affected components and the underlying origin, identifying the most frequent string fault causes. An

approach employing CUSUM Charts is introduced to identify substring outages within PV systems effectively. The methodology

utilizes fundamental field data that is commonly available in practice. A filtering approach, combined with the use

of CUSUM control charts, minimizes false positives, ensuring that only consistent underperformance is flagged as an outage.

The methodology returns far fewer false positives and more stable error intervals for substring outages than a former

monitoring approach. Overall, the study demonstrates a significant improvement in detecting substring outages. The

advanced methodology enables more effective O&M for PV plants, where substring outages are reliably identified after a

short detection time.

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.

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.

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.

On-site current-voltage (IV) measurements will play an essential role in the online monitoring of PV systems. However, challenging measurement conditions like inconsistent irradiance levels on PV arrays (e.g., due to local shading) can distort IV curves, leading to inaccurate characterizations. By accurately detecting deformed IV curves, the reliability of both on-site and remote IV measurements is significantly enhanced. For this purpose, several classifiers were evaluated using 4104 manually labeled IV measurements on a mc-Si-PV array. Machine learning tech-niques perform much better than a traditional rule-based filter, with accuracy above 99 %. A deep Autoencoder was employed to reduce IV measurements into a set of 7 features, which encoded the shape of the curves into a low dimen-sionality. The IV-Autoencoder improved the classification of IV curves, yielding better results than a feature reduction with Principal Component Analysis. The proposed classifiers are able to sort out on-site IV measurements under un-satisfactory environmental conditions, benefiting the online monitoring of PV systems. It may also be used as an indi-cator for faulty PV strings.

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.