Digitale Lernumwelten, studentische Diversität und Learning Outcomes. Empirische Befunde und Implikationen für die digitale Hochschulbildung

Digitalen Lernumwelten, die Künstliche Intelligenz (KI) einsetzen, wird zugeschrieben, dass sie der Diversität Studierender, ihren unterschiedlichen Lebenslagen und differierenden Lernvoraussetzungen sowie -erfahrungen besser gerecht werden, indem sie flexibles, zeit- und ortsunabhängiges sowie personalisiertes Lernen ermöglichen und somit zum individuellen Lern- und Studienerfolg beitragen können. Dieser Beitrag gibt einen Überblick, in welcher Weise die Vielfalt der Studierenden bisher in die empirische Forschung zur digitalen Hochschulbildung (unter Einbeziehung von KI) Eingang gefunden hat. Während diversitätsorientierte Studien zur ICT-Literacy sowie zu Mediennutzung und -präferenzen diverser Studierendengruppen international vorhanden sind, werden die Effekte digitaler Lernumwelten bzw. Lernformate oder die Auswirkungen von KI auf die Learning Outcomes einer divers zusammengesetzten Studierendenschaft (z.B. Lernerfolge, Mobilität) auch international selten thematisiert. Der vorliegende Beitrag stellt daher exemplarisch aktuelle Befunde zum Einfluss digitaler Lernumwelten auf die Mobilität diverser Studierender vor und zeigt, welche Ansatzpunkte und Herausforderungen für künftige Forschung zur digitalen Hochschulbildung bestehen. (DIPF/Orig.)Digital learning environments using artificial intelligence (AI) are viewed as better able to accommodate the diversity of students, their different life situations, different learning conditions and experiences, as they allow individuals to learn more flexible and more personalised, independent of time and place, and thus contribute to their success in learning and studying. This paper provides an overview of the ways in which student diversity has been included in empirical research on digital higher education (involving AI) to date. While diversity-oriented studies on ICT literacy and on the media use or media preferences of various student groups are available internationally, effects of digital learning environments, different digital learning formats or even AI on the learning outcomes of a diverse student body (e.g. learning success, mobility) are rarely addressed. Therefore, we present current findings on the influence of digital learning environments on the mobility of diverse students as an example and point out the starting points, but also challenges that exist for future research in this area. (DIPF/Orig.

Time Series Sampling and Data Assimilation in a Simple Marine Ecosystem Model

Simulated distributions of nutrients, phytoplankton and zooplankton were obtained from a simple marine ecosystem model that included nutrient inputs from episodic events. These distributions were then used in numerical identical twin experiments to test the ability of an adjoint data assimilation method to recover rate parameters, such as population growth and death rates, component initial conditions, and the amplitude of episodic events. Data were assimilated into the marine ecosystem model at monthly, bi-weekly and weekly intervals over a period of about 2 months. The ability to recover rate parameters and component initial conditions was determined primarily by the frequency and type of data that were assimilated. Assimilation of data at monthly intervals proved to be adequate for recovery of most of the rate parameters and some of the initial conditions. Bi-weekly data yielded better recoveries; however, increasing the data availability to weekly intervals did not significantly improve the results relative to the bi-weekly cases. The ability to recover biological rates with only monthly data suggests that these are fundamental aspects of marine ecosystems and can be resolved with only a few measurements. The availability of zooplankton information, even at a reduced frequency relative to phytoplankton or nutrient information, improved the ability to recover rate parameters with data more widely spaced in time. Recovery of component initial conditions was related to the timescales of the biological processes; faster processes required more frequent data. The recovery of the amplitude of the episodic events was related to the timing of the sampling relative to the event, rather than to the frequency at which data were available. The number of iterations needed for convergence when using data assimilation with the marine ecosystem model was dependent not only on the frequency and type of the input data series, but also on the structure of the marine ecosystem model. These results have implications for designing sampling strategies for measurement programs, such as the U.S. Joint Global Ocean Flux Study Hawaii Ocean Time-series and Bermuda Atlantic Time-series sites, so that these multidisciplinary data sets can be used with data-assimilative marine ecosystem models

A Data Assimilation Technique Applied to a Predator-Prey Model

A new approach for data assimilation, which is based on the adjoint method, but allows the computer code for the adjoint to be constructed directly from the model computer code, is described. This technique is straightforward and reduces the chance of introducing errors in the construction of the adjoint code. Implementation of the technique is illustrated by applying it to a simple predator-prey model in a model fitting mode. A series of identical twin numerical experiments are used to show that this data assimilation approach can successfully recover model parameters as well as initial conditions. However, the ease with which these values are recovered is dependent on the form of the model equations as well as on the type and amount of data that are available. Additional numerical experiments show that sufficient coefficient and parameter recoveries are possible even when the assimilated data contain significant random noise. Thus, for biological systems that can be described by ecosystem models, the adjoint method represents a powerful approach for estimating values for little-known biological parameters, such as initial conditions, growth rates, and mortality rates

Resilienz stärken: Interventionsmöglichkeiten für Hochschulen zur Förderung der akademischen Resilienz ihrer Studierenden; Ein Leitfaden

Gerade in Zeiten von (persönlichen) Krisen benötigen Studierende eine ausreichende psychische Widerstandsfähigkeit, um mit Belastungen im Hochschulkontext umgehen zu können. Dies hat uns spätestens die COVID-19-Pandemie deutlich vor Augen geführt. Daher stellt sich mehr denn je die Frage, was Hochschulen tun können, um ihre Studierenden dabei zu unterstützen, mit Belastungserfahrungen konstruktiv umzugehen und ihr Studium erfolgreich zu Ende zu bringen. Der vorliegende Leitfaden fasst zentrale Erkenntnisse hierzu zusammen und richtet sich an alle Akteure aus der Hochschulpraxis, welche um die Sicherung des Studienerfolgs und der Studierendengesundheit bemüht sind