Agency practically imparted: a media-didactical workshop conception

Im folgenden Artikel wird ein mehrstufiges mediendidaktisches Workshop-Konzept vorstellt. Ziel des Angebots ist die praxisnahe Vermittlung von Grundlagen anhand von Fallbeispielen. Der Workshop wurde im Rahmen des seit 2012 durch das BMBF geförderten, sächsischen Verbundprojekts Lehrpraxis im Transfer (LiT) zur Qualifizierung für den Bereich Neue Medien entwickelt. Im Artikel werden die Philosophie und Genese von «Neue Lehre durch Neue Medien? – Sinnvoller Einsatz von Neuen Medien in der Hochschullehre» dargestellt und ein Einblick in Konzeption und Durchführung der Weiterbildung gegeben. Das Konzept richtet sich an Vertreter/innen aller Fachrichtungen und vermittelt mediendidaktische und damit einhergehend hochschuldidaktische Grundlagen. Zur niedrigschwelligen Veranschaulichung der Thematik wird mit Beispielen guter Lehrpraxis gearbeitet. Das Angebot richtet sich an Lehrende, die das Sächsische Hochschuldidaktik Zertifikat erlangen wollen und ist im Zertifikatsprogramm des Hochschuldidaktischen Zentrums Sachsen (HDS) im Bereich «Neue Medien» anrechenbar. Derzeit wird «Neue Lehre durch Neue Medien?» als offene Bildungsressource aufbereitet und zur Nutzung für Weiterbildner/innen zur Verfügung gestellt.The following article introduces a multilevel media-didactical workshop conception, which was developed within the Saxonian network-project for Higher Education Lehrpraxis im Transfer. The aim is the practical teaching of basic didactics through case studies. The workshop was developed within the Saxonian network-project Lehrpraxis im Transfer (LiT) in the field of new media. The article will present the philosophy and generation of «Neue Lehre durch Neue Medien? – Sinnvoller Einsatz von Neuen Medien in der Hochschullehre» and pursue the conception and its implementation. It focuses all teachers in higher education of all subjects and introduces basics in media-didactics and Higher Education by the use of good practice. The workshop addresses teachers in Higher Education, aiming to achieve the certificate of the Hochschuldidaktisches Zentrum Sachsen – Center for Higher Education in Saxony (HDS) within the field of new media. «Neue Lehre durch Neue Medien?» is currently edited for the use as Open Educational Resource

Ethical Questions Raised by AI-Supported Mentoring in Higher Education

Mentoring is a highly personal and individual process, in which mentees take advantage of expertise and experience to expand their knowledge and to achieve individual goals. The emerging use of AI in mentoring processes in higher education not only necessitates the adherence to applicable laws and regulations (e.g., relating to data protection and nondiscrimination) but further requires a thorough understanding of ethical norms, guidelines, and unresolved issues (e.g., integrity of data, safety, and security of systems, and confidentiality, avoiding bias, insuring trust in and transparency of algorithms). Mentoring in Higher Education requires one of the highest degrees of trust, openness, and social–emotional support, as much is at the stake for mentees, especially their academic attainment, career options, and future life choices. However, ethical compromises seem to be common when digital systems are introduced, and the underlying ethical questions in AI-supported mentoring are still insufficiently addressed in research, development, and application. One of the challenges is to strive for privacy and data economy on the one hand, while Big Data is the prerequisite of AI-supported environments on the other hand. How can ethical norms and general guidelines of AIED be respected in complex digital mentoring processes? This article strives to start a discourse on the relevant ethical questions and in this way raise awareness for the ethical development and use of future data-driven, AI-supported mentoring environments in higher education

Effects of fish predation on density and size spectra of prey fish communities in lakes

Planktivorous and benthivorous fish have been documented to influence the density and size structure of their prey communities in lakes. We hypothesized that piscivorous fish modify their prey fish communities in the same way and sought to find evidence for such predation effects from a comparison across 356 lakes located in nine European ecoregions. We categorized individual fish as being either piscivore, non-piscivore or prey of piscivores, depending on species and individual size. We calculated piscivore, non-piscivore and piscivore prey densities, respectively, and fit linear abundance size spectra (SS) on lake-specific piscivore, non-piscivore and piscivore prey size distributions. Multiple linear regressions were calculated to quantify the effect of piscivore density and SS slopes on non-piscivore and piscivore prey densities and SS slopes, by accounting for potentially confounding factors arising from lake morphometry, productivity and local air temperature. Piscivore density correlated positively with piscivore prey density, but was uncorrelated to density of non-piscivores. Across a subset of 76 lakes for which SS slopes of piscivores were statistically significant, SS slopes of piscivores were uncorrelated with SS slopes of either non-piscivores or piscivore prey. However, densities of piscivores, non-piscivores or piscivore prey were a significant negative predictor of SS slopes of the respective groups. Our analyses suggest that direct predation effects by piscivorous fish on density and size structure of prey fish communities are weak in European lakes, likely caused by low predator-prey size ratios and the resulting size refuges for prey fish. In contrast, competition may substantially contribute to between-lake variability in fish density and size

Ethical Questions Raised by AI-Supported Mentoring in Higher Education

Mentoring is a highly personal and individual process, in which mentees take advantage of expertise and experience to expand their knowledge and to achieve individual goals. The emerging use of AI in mentoring processes in higher education not only necessitates the adherence to applicable laws and regulations (e.g., relating to data protection and nondiscrimination) but further requires a thorough understanding of ethical norms, guidelines, and unresolved issues (e.g., integrity of data, safety, and security of systems, and confidentiality, avoiding bias, insuring trust in and transparency of algorithms). Mentoring in Higher Education requires one of the highest degrees of trust, openness, and social–emotional support, as much is at the stake for mentees, especially their academic attainment, career options, and future life choices. However, ethical compromises seem to be common when digital systems are introduced, and the underlying ethical questions in AI-supported mentoring are still insufficiently addressed in research, development, and application. One of the challenges is to strive for privacy and data economy on the one hand, while Big Data is the prerequisite of AI-supported environments on the other hand. How can ethical norms and general guidelines of AIED be respected in complex digital mentoring processes? This article strives to start a discourse on the relevant ethical questions and in this way raise awareness for the ethical development and use of future data-driven, AI-supported mentoring environments in higher education

Ethical Questions Raised by AI-Supported Mentoring in Higher Education

Mentoring is a highly personal and individual process, in which mentees take advantage of expertise and experience to expand their knowledge and to achieve individual goals. The emerging use of AI in mentoring processes in higher education not only necessitates the adherence to applicable laws and regulations (e.g., relating to data protection and nondiscrimination) but further requires a thorough understanding of ethical norms, guidelines, and unresolved issues (e.g., integrity of data, safety, and security of systems, and confidentiality, avoiding bias, insuring trust in and transparency of algorithms). Mentoring in Higher Education requires one of the highest degrees of trust, openness, and social–emotional support, as much is at the stake for mentees, especially their academic attainment, career options, and future life choices. However, ethical compromises seem to be common when digital systems are introduced, and the underlying ethical questions in AI-supported mentoring are still insufficiently addressed in research, development, and application. One of the challenges is to strive for privacy and data economy on the one hand, while Big Data is the prerequisite of AI-supported environments on the other hand. How can ethical norms and general guidelines of AIED be respected in complex digital mentoring processes? This article strives to start a discourse on the relevant ethical questions and in this way raise awareness for the ethical development and use of future data-driven, AI-supported mentoring environments in higher education