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.

The abbreviation rANS stands for a relatively new method of arithmetic coding based on asymmetric numeral systems (ANS) which combines the advantages of arithmetic coding in terms of performance and the advantages of Huffman coding in terms of speed.
Compared to conventional arithmetic coding methods, the mathematical apparatus is slightly different which has the consequence that the decoding order is reversed to the encoding order, i.e. the processing follows the last-in-first-out principle.
This makes it somewhat difficult to design the coding process to adapt to changing symbol statistics, and therefore rANS coding has so far only been applied in settings with fixed statistics.
In particular, the frequent rescaling of statistics required to reduce the influence of old symbols becomes a problem when the order of processing is different on the encoder and decoder sides.

This paper proposes a new method that allows adaptive coding within the framework of rANS coding and additionally offers the possibility of rescaling the symbols frequencies. Investigations show that this method enables the same compression performance for rANS as for conventional arithmetic coding.

Sensory perturbations are a valuable tool to assess sensory integration mechanisms underlying balance. Implemented as systems-identification approaches, they can be used to quantitatively assess balance deficits and separate underlying causes. However, the experiments require controlled perturbations and sophisticated modeling and optimization techniques. Here we propose and validate a virtual reality implementation of moving visual scene experiments together with model-based interpretations of the results. The approach simplifies the experimental implementation and offers a platform to implement standardized analysis routines. Sway of 14 healthy young subjects wearing a virtual reality head-mounted display was measured. Subjects viewed a virtual room or a screen inside the room, which were both moved during a series of sinusoidal or pseudo-random room or screen tilt sequences recorded on two days. In a between-subject comparison of 10 6 min long pseudo-random sequences, each applied at 5 amplitudes, our results showed no difference to a real-world moving screen experiment from the literature. We used the independent-channel model to interpret our data, which provides a direct estimate of the visual contribution to balance, together with parameters characterizing the dynamics of the feedback system. Reliability estimates of single subject parameters from six repetitions of a 6 20-s pseudo-random sequence showed poor test–retest agreement. Estimated parameters show excellent reliability when averaging across three repetitions within each day and comparing across days (Intra-class correlation; ICC 0.7–0.9 for visual weight, time delay and feedback gain). Sway responses strongly depended on the visual scene, where the high-contrast, abstract screen evoked larger sway as compared to the photo-realistic room. In conclusion, our proposed virtual reality approach allows researchers to reliably assess balance control dynamics including the visual contribution to balance with minimal implementation effort.

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.

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 for mastering 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 address media literacy on the other hand. Key findings are that media literacy is not precisely
characterized yet and that learning settings often do not address media 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.

This paper will focus on future skills and outline the needs of universities to adapt their learning concepts to new requirements. It discusses challenges for universities during this adaption process, lessons learned concerning the integration of future skills in study programs, and proposes some solutions to overcome these deficiencies.