Künstliche Intelligenz (KI) kann im Prozess der Leistungsbewertung assistieren und diesen transformieren. Besonders lohnend scheint eine KI-Assistenz bei der Bewertung von komplexem, geschriebenem Text. Jedoch ist der Einsatz von KI im Bewertungsprozess «hochriskant» (EU 2024) und bedarf umfangreicher Analysen. Die vorliegende Studie untersucht, inwiefern ChatGPT-4o die Auswertung und Interpretation von Lerntagebucheinträgen objektiv vornehmen kann. Dafür werden 757 Lerntagebucheinträge aus der geförderten Weiterbildung in Deutschland von Mensch und Maschine bewertet. Sowohl Mensch als auch Maschine erhalten hierzu Kriterien, nach denen die Bewertung vorzunehmen ist; ChatGPT-4o wird diesbezüglich mit einem Prompt unterstützt. Die Übereinstimmung der Bewertungen wird anhand der Masse Sensitivität und Spezifität gemessen. Die Ergebnisse zeigen, dass die Bewertungsvorschläge von ChatGPT-4o eine moderate bis hohe Übereinstimmung mit den menschlichen Bewertungen aufweisen; gleichzeitig neigt ChatGPT-4o jedoch zu einer optimistischen Bewertung der Lerntagebucheinträge. Die Ergebnisse weisen darauf hin, dass eine hybride Intelligenz, also eine Kombination der Stärken von Mensch und Maschine, gewinnbringend für Bewertungsprozesse sein kann. Künftig denkbar sind halbautomatisierte Bewertungsprozesse von Lerntagebucheinträgen, in denen die KI die Bewertung der Lerntagebucheinträge übernimmt und Lehrkräfte bei kritischen Fällen regulierend eingreifen. So könnte die Korrektureffizienz ohne bedeutende Qualitätsverluste gesteigert werden.

Learning diaries are reflective tools, often used as formative assessments in adult education with the aim to promote cognitive and metacognitive learning strategies. As grading of and feedback on learning diaries is effortful for teachers, artificial intelligence (AI) may assist teachers in evaluating learning diaries. A prerequisite is that AI's ratings show high accordance with the teachers' ratings. AI accuracy, measured via absolute accuracy and bias, is the focus of the current study with N = 540 learning diary entries focusing on learning strategies, seven teachers, and ChatGPT-4o. Findings revealed that AI evaluations align closely with teacher assessments, indicated by high overall accuracy and low bias. Interestingly, the accuracy varied based on the types of learning strategies assessed in the diaries. Additionally, individual teacher assessments influenced the alignment between human and AI evaluations, suggesting that teachers applied their profession-specific expertise to the assessment process while AI produced somewhat generic evaluations. Overall, the study results indicate that AI can enhance the efficiency of formative assessments while providing timely feedback to learners.

For successful learning, monitoring one's learning behavior and outcomes is essential. While both the use of monitoring strategies and monitoring judgment accuracy are regarded as indicators for metacognitive monitoring, their joint contribution to metacognitive monitoring and their interrelationship is unclear. The current study investigated how monitoring strategies and monitoring judgments (spontaneous as well as prompted judgments) change micro-genetically and influence each other reciprocally during an exam preparation phase. In this diary study, N = 273 higher education students completed knowledge tests and surveys on five consecutive days. Results indicated interindividual differences in dependence of prior performance and gender for the accuracy of prompted judgments but not for monitoring strategies or spontaneous judgments. Random intercept cross-lagged panel models revealed that higher quality monitoring strategies led to the spontaneous formation of monitoring judgments, while the quality of monitoring strategies and prompted judgment accuracy did not influence each other significantly.

Context plays a critical role in talent development, yet most national analyses continue to rely on individual-centered talent concepts. This paper highlights the limitations of traditional models for assessing how countries support talent and proposes a resource-oriented, systemic alternative. Building on the Educational and Learning Capital Approach (ELCA), this study argues that national talent development depends on the availability, accessibility, and orchestration of both endogenous and exogenous learning resources across systemic levels. By analyzing the clumping patterns of excellence in STEM, the arts, sports, and innovation, this paper illustrates the unequal global distribution of talent-supportive environments. Seven key principles for effective resource orchestration are outlined, offering a framework for evaluating and strengthening national talent ecosystems. The paper concludes that systematic assessment and strategic enhancement of national resource landscapes are critical for sustainable talent development and for ensuring that human potential can flourish more equitably across countries.

AI-based technologies are becoming increasingly significant while transforming human-machine interactions. Yet, many important questions remain unanswered. One example is the research question of the present contribution, regarding the effects of artificial intelligence (AI) on users’ perceptions of dependency on chatbots and smart home systems. The objective is to analyze to what extent users perceive dependencies on these technologies and which factors influence this perception. Based on a survey of 325 users, it was found that dependency perception is currently low but increases with more frequent usage. The results show a substantial association between perception of dependence and frequency of use, with a stronger effect among users of chatbots compared to users of smart home systems. Furthermore, a slight negative effect was observed for attitudes toward AI on dependency perception for both chatbots and smart home systems. To test the hypotheses, linear regression analyses were employed, revealing substantial associations between usage frequency and dependency perception. Despite limitations such as gender imbalance in the sample—even though gender had a negligible effect on PoD –, this study provides valuable insights into the societal impacts of AI and lays the groundwork for future research in this area. Future studies should include larger and more representative samples and develop validated measurement instruments to enhance the generalizability of findings. The results emphasize the necessity of critically evaluating the deployment and integration of AI technologies to identify potential dependencies at an early stage.

Since October 2017, 3D audio recordings have been made every month at the Vogelinsel nature reserve on Lake Altmühlsee, Germany. The recordings start one hour before and end one hour after sunset. The microphone, position and recording device are identical for all recordings. Each recording is analysed by hand using the same analysis method, whereby the content of the recordings is annotated. The weather data for the recording days is collected. The recording and weather conditions at the recording location are documented photographically. This data set is highly unique due to the 3D recording technique and the long duration of the acoustic observation of nature at one spot. In addition to collecting further data, the project aims to compare the recordings with known algorithmic bioacoustic analysis methods in order to better understand the changes in nature at the recording site (possibly caused by climate/biodiversity change), to develop further algorithmic methods of soundscape analysis and to use the recordings and additional data for nature education, medical-therapeutic and wellness contexts. The article describes the data collection in detail, outlines the comparative results achieved so far, presents our additional analysis methods and gives an outlook on the future of the project.

The possibilities and limitations of realtime-auralizations of an acoustic laboratory with the “WWISE Spatial Audio” are discussed by 3D-modelling and auralizing a test rig designed for sound reduction index measurements in VR. The simulated results were compared to real measurements with the acoustic parameters: Reverberation Time [T30] and Sound Reduction Index [SRI], each with three binauralization methods used and further optimizations done with Dolby Atmos for headphones. The results showed a deviation and partly a correlation within the “Just Noticeable Differences [JND]” and a precise reproduction of the sound reduction index within WWISE was possible for test components with the lowest real SRI-values. The frequency dependent SRI-values were subtracted from the audio file using bandpass filters - before using “WWISE” for sound playback in VR. The results of this work can be a seen as a technical analysis and fundament for future research regarding realtime-auralization capabilities of audio middlewares.

The positive effects that bioacoustic environments in nature can have on people raise the question of the extent to which bioacoustic recordings can have positive effects as well when played back via VR in the context of a hospital. This paper presents a study in which the effect of a 3D soundscape recording including a 360° still image on geriatric patients in a clinic was investigated. 28 test subjects each spent 15 minutes in a 360° VR room, surrounded by six projection screens and 14 loudspeakers. The study was conducted in a geriatric rehabilitation ward. The recording location was chosen in a nearby recreation and nature reserve. The subjects' state of mind was measured at four points in time using a standardised psychological measurement procedure. During the 15-minute stay in the virtual nature, biosignals of various vital parameters were recorded. In addition, the room was opened to patients and staff at defined times and their use was recorded. The staff were asked about their experiences. The results show that playback increased the well-being of most participants. The therapeutic possibilities of using 3D bioacoustic recordings are discussed and future research and application scenarios for the system are presented.

The interactive soundscape education project, ‘Klangwelt-Entdecker’, aims to raise awareness of the soundscape in a nature reserve. By scanning QR codes with their smartphones at six listening stations on a circular route along the bird island on Lake Altmühlsee, Germany, visitors can experience the diversity of the soundscape of the site throughout the year and obtain information about the bioacoustics of the local nature and birdlife via the project website. The initiative places a special focus on the effects of climate change on the soundscape and aims to convey a greater understanding of the acoustic facets of nature. Sounds are used from a database that has been fed with monthly 3D sound recordings of the bird island for more than 7 years. The impact of the project with regard to the soundscape educational objectives will be analysed by means of a study among participants of the circular walk. The results of the study will be presented and discussed in the context of existing similar projects. Based on this, well-founded demands are made on nature education and, in particular, bioacoustics and soundscape education.

The development of digital musical instruments is often pursued from a substance-ontological functional and physical perspective. However, many musicians talk less about music from a functional and physical perspective but more about the experienced phenomena they encounter when making music. In this article, instrumental playing and musical interaction will be considered from two perspectives: the broadly used substance-ontological view and the process-ontological approach as drafted by Alfred North Whitehead. The instrumental focus here is on digital and traditional bowed stringed instruments and varying descriptions of the interaction on the instrument. Related works and these descriptions are looked at from the two perspectives mentioned. It is shown why it often comes to a bifurcation of nature into primary and secondary qualities of musical interaction during the interface design process. Furthermore, it is shown that musicians' requirements for digital musical instruments are sometimes unfavourably excluded. These problems will be exemplified and suggestions for a process-ontological perspective on the design of musical interaction will be presented. Also an example of a highly market relevant design principle of a musical sound synthesis system fitting to the process-ontological understanding of musical interaction is presented and discussed.

Evidence syntheses, such as systematic reviews, aim to summarise the current state of research in a field, often using the publication language of a study as a criterion for inclusion or exclusion. However, this has serious implications for capturing evidence from a wider range of geographical areas, and the potential for linguistic bias. In order to explore this issue, a trilingual tertiary mapping review of 446 evidence syntheses within the field of educational technology (EdTech) and published in English, Spanish and German was undertaken, analysing the frequency of multi- and monolingual evidence syntheses, reasons for language choice by research teams, and the composition of research teams in multi- and monolingual evidence syntheses. Items were included if they were a form of evidence synthesis with an explicit method section, indexed within ERIC, Scopus, Web of Science, Dialnet, FIS-Bildung, or Google Scholar, education-related, and published between 1983 and May 2022. The results showed that only eight languages were considered in published syntheses, only five languages were used to construct search strings, most evidence syntheses included research published in English without explaining why, and multilingual research team composition did not predict multilingual evidence syntheses. The findings suggest the need to address publication languages not only as a formal criterion but as an integral aspect of methodological approach, influencing the content and scope of syntheses in educational research.

Synchronous online learning is situated in synchronous webinars, virtual classrooms, and videoconferences, in which students and teachers communicate live over the Internet across distant geographical locations. Shared virtual platforms with voice-over-IP technology and webcams are used to interact ubiquitously and synchronously in real-time. During the COVID-19 pandemic, the use of synchronous online learning environments in primary, secondary, higher, and continuous education has grown massively. In this chapter, we aim to identify design recommendations that help increase the effectiveness of synchronous online learning. First, we provide an overview of recent meta-analyses on the effectiveness of synchronous online learning compared to synchronous face-to-face and asynchronous online learning. Second, we use this empirical evidence to develop design principles that can guide teachers, trainers, and educators in designing effective synchronous online learning environments. Third, we discuss a research agenda for future studies on synchronous online learning, webinars, and videoconferences in education, and we reflect on some practical affordances, including the use of webcams and breakout rooms.

This paper investigates anthropomorphism of a robot arm during a cooperative human-robot pick-and-place task, while varying cognitive load of test persons. Test persons are required to repeatedly provide a Lego brick for the robot by alternatingly putting it onto one of two trays. The robot then picks it up and puts it in front of the test person again. Cognitive load is varied by whether or not an initially given 8-digit number has to be remembered by the test person. Dimensions of anthropomorphism are acquired using the HRIES questionnaire and cognitive load is acquired using two state-of-the-art questionnaires. Results show a significant correlation of perceived sociability and animacy on mental demand and cognitive load, but only in the high load condition. It is suggested, that cognitive load should be considered during cooperative task design because resulting variations of anthropomorphism may impact cooperative task performance.

Abstract

This paper presents results from a study on the impact of negative attitudes towards robots on pedestrians’ needs for technological communication capabilities of autonomous vehicles and preferred communication modalities. Further, the amount of prior information on autonomous vehicles given to test persons is varied. The study is realized in terms of an imagination scenario. Results show a significant dependency of the demand for communication of autonomous vehicles with pedestrians on the extent of negative attitudes towards robots as well as a general demand for such communication capabilities. Interestingly, these findings are independent of the amount of prior information. Differences of preferred communication modalities with respect to negative attitudes or prior information are not found. The results of this study emphasize the importance of vehicle-pedestrian communication, particularly, using multi-modal interfaces in future autonomous driving technology.

Zusammenfassung

Dieser Beitrag präsentiert die Ergebnisse einer Studie über die Auswirkungen negativer Einstellungen gegenüber Robotern auf die Bedürfnisse von Fußgängern nach technologischen Kommunikationsmöglichkeiten autonomer Fahrzeuge und bevorzugte Kommunikationsmodalitäten. Darüber hinaus wird die Menge an Vorinformationen zu autonomen Fahrzeugen, die den Testpersonen gegeben werden, variiert. Die Studie wird anhand eines imaginären Szenarios durchgeführt. Die Ergebnisse zeigen eine signifikante Abhängigkeit des Bedarfs an Kommunikation autonomer Fahrzeuge mit Fußgängern vom Ausmaß negativer Einstellungen gegenüber Robotern sowie eines Bedarfs an solchen Kommunikationsfähigkeiten allgemein. Interessanterweise sind diese Erkenntnisse unabhängig von der Menge an Vorinformationen. Unterschiede der bevorzugten Kommunikationsmodalitäten in Bezug auf negative Einstellungen oder Vorinformationen wurden nicht festgestellt. Die Ergebnisse dieser Studie unterstreichen die Bedeutung der Fahrzeug-Fußgänger-Kommunikation, insbesondere unter Verwendung multimodaler Schnittstellen in zukünftigen autonomen Fahrtechnologien.

As a contribution to addressing the challenges posed by climate change, acoustic ecology, and in particular the study of changes in soundscapes, provides an opportunity to better understand and subsequently manage climate change. This paper focuses on the development of new spectrogram-based analysis methods to expand the possibilities of comparative analysis of soundscapes. The database to be analysed with the new methods is provided by the WikliNathi project, a long-term monitoring study to investigate the sonic changes of populations of living organisms as well as geophone and anthropophone sounds in a nature reserve. The developed methods of analysis will be presented in detail. Selected audio recordings are analysed for changes in the sounds of great tits, grasshoppers and frogs from 2018 to 2023 using spectral flatness, temporal flatness and onset detection in aligned time-frequency regions. The evaluation of the results shows that the proposed new methods can be used to obtain more detailed and complementary results in future studies as part of this and other bioacoustic projects.