Der Beitrag stellt den Entwicklungsprozess des im Projekt LeadCom entstandenen Selbstlernkurses Videokonferenznutzung zur kollegialen Kommunikation und Kooperation dar und zeigt auf, warum diese Fortbildung zum Thema Videokonferenzen in außerunterrichtlichen Situationen für eine zeitgemäße Schulentwicklung relevant ist. Einerseits wurde der Kurs mit dem Ziel konstruiert, durch eine kompetente Nutzung von virtuellen Meetings im Schulkontext, eine moderne und effektive Zusammenarbeit im Lehrerkollegium zu fördern und so den sich verändernden Ansprüchen einer zunehmend digitalisierten Welt gerecht zu werden. Andererseits soll die Nutzung des Selbstlernkurses auch dazu beitragen, dass Lehrkräfte und Schulleitungen sich digital kompetent und selbstwirksam im Umgang mit digitalen Kommunikationsmedien erleben. Als Transferziel sollten die Einstellungen und Erwartungen von Lehrkräften gegenüber Informations- und Kommunikationstechnologien (ICT) und die Nutzung von ICT für schulinterne Kooperation die Nutzung von ICT im Unterricht sowie die digitale Kompetenzentwicklung von Schülerinnen und Schülern positiv beeinflussen (u.a. Drossel et al., 2016; Fraillon et al., 2020). Die Entwicklung des Selbstlernkurses fußte dabei unter anderem auf dem weiterentwickelten Technologieakzeptanzmodell von Venkatesh et al. (2003, 2012), der Unified Theory of Acceptance and Use of Technology (UTAUT). In einer eng mit dem Entwicklungsprozess der Fortbildungseinheit durchgeführten quantitativen Studie wurden anhand des UTAUT-Modells Einstellungen und Erwartungen von Lehrkräften in Bezug auf die Nutzung von Videokonferenzen in außerunterrichtlichen Kommunikations- und Kooperationssituationen untersucht. Der Beitrag wird aufzeigen, wie sowohl die theoretischen Überlegungen aus dem UTAUT-Modell als auch in Teilen die Ergebnisse der Studie in die Konstruktion des Selbstlernkurses eingeflossen sind.

Systemic and learning resource-oriented talent development emphasizes interplays of resources such as the law of the minimum, which states that the least resource limits talent development. In the present study, 1693 Sudanese students completed the Questionnaire for Educational and Learning Capital. First, the factorial validity was confirmed. Subsequently, four hypotheses regarding the law of the minimum were tested and partially confirmed: (1) The minimum of exogenous learning resources (in the environment of the students) explained substantial proportions of the variance of endogenous learning resources (within the students). (2) The minimum exogenous resource explains a similar amount of variance within endogenous learning resources than a mean of several minimum scores. (3) Additional compensation possibilities did explain significantly more variance of the endogenous resources than just the law of the minimum. (4) In line with the law of the minimum paradox, a cluster analysis identified a group of students with higher correlated learning resources (less well adapted system) than a second group of students. The results are discussed to encourage further research into systemic interplays of learning resources and ideas toward a systemic gifted and talented education.

Entrepreneurial ecosystems thrive on the contributions of diverse actors, among whom entrepreneurs play a pivotal role. Interestingly, previous research on entrepreneurial ecosystems has remained silent on how to encourage entrepreneurs to “give back” to their ecosystem. This study probes the phenomenon of downward causation, aiming to unravel the motivations and mechanisms driving entrepreneurs to actively contribute to the evolution and stability of their ecosystems. Drawing on qualitative analyses of 32 in-depth interviews with startup entrepreneurs in Germany, our findings unveil the characteristics of contribution behavior and highlight the heterogeneous nature of entrepreneurs’ motivations. Reciprocal motivation emerges as a central driver, influenced by a complex interplay of interactions. Experiences with ecosystem actors shape diverse interpersonal relationships, impacting entrepreneurs’ gratitude and fostering a sense of affiliation. Individual characteristics, such as entrepreneurial passion and altruism, along with the situational context, can reinforce or constrain this motivation. These insights converge into a dynamic model, enhancing theoretical foundations of downward causation within entrepreneurial ecosystems.

Generative KI-Tools wie ChatGPT eröffnen neue Möglichkeiten für das Lernen – von schnellen Erklärungen bis hin zu personalisierter Unterstützung. Für erfolgreiche Lernprozesse sollten Lernende aber nicht alle Denkprozesse auslagern (cognitive offloading), den eigenen Lernfortschritt im Blick behalten (metacognitive laziness) und die KI als aktiven Lernpartner nutzen – nicht nur als Suchmaschine (co-creation). Im Vortrag wird vorgestellt, wie diese KI-Interaktionen im Projekt SekoKI beforscht werden.

Selective Laser Sintering of Polymers is a widely used Additive Manufacturing technology that involves a laser to selectively sinter layers of a powder bed, with Polyamide 12 being a common material choice. Despite its favourable processability and component performance, the printing process leaves a significant amount of unsintered powder that undergoes heat treatment due to temperature gradients during printing, leading to material degradation over time. A deep comprehension of the aging behaviour in the powder for rightly planning the successive building process is thus necessary to define the proper recycling methods. This paper presents a comprehensive study of the thermal and structural characteristics of Polyamide 12 after five successive reusing cycles, as well as the dimensional accuracy and the mechanical strength of the corresponding printed parts. The study includes tests on the powder that underwent successive printing, and the parts manufactured using this powder. The results were compared to those obtained from virgin powder. These results were used to justify the differences in mechanical, macro-geometrical, and micro-geometrical performance between virgin and multiple reused powder parts. The results indicate that the powder degradation causes a significant reduction of the mechanical strength, and the texture quality of parts made from reused powder, while the dimensional accuracy remains very high.

Individuell und komplex: Lernressourcenregulation im Lernprozess - Eine temporale Netzwerkanalyse am Einzelfall

Theoretischer Hintergrund: Lernprozesse finden nicht isoliert statt, sondern innerhalb eines vernetzten Systems aus Ressourcen, deren Zusammenspiel ein Lernender zu steuern versucht. Bisherige Theorien und empirische Befunde verweisen zwar auf die Bedeutung der Ressourcenverfügbarkeit im lernenden System, die Ressourcenregulation wird jedoch zu wenig beachtet. Es ist ein sehr individuelles Ressourcennetzwerk zu erwarten, das über den Lernprozess hinweg und situationsabhängig angepasst werden muss.

Fragestellung: Die Studie untersucht an einem Einzelfall die komplexe, individuelle Ressourcenregulation im zeitlichen Verlauf, um bei diesem Einzelfall stabile Muster im Zusammenwirken der Ressourcen zu identifizieren und Vorhersagen für die Ressourcenkonstellation zur nächste Lernepisode zu machen.

Methode: Das Vorgehen ist analog zur Entwicklung personalisierter Psychotherapiepläne auf Basis von Symptom-Zeitreihenerhebungen (Epskamp et al., 2018). In einem Zeitreihen-Lernexperiment über 50 Tage wurden täglich die Ressourcen, operationalisiert über die Lern- und Bildungskapitale (Ziegler & Stoeger, 2011), für das Erlernen von Vokabeln bei einer Studentin (N = 1) erfasst. Zusätzlich wurde die Leistung, Lerndauer und das Stresslevel täglich erhoben. Die multivariate Zeitreihe wurde netzwerkanalytisch mit einem graphical vector autoregressive model ausgewertet, das ein Netzwerk für die gleichzeitig auftretenden Effekte und eines für die zeitliche Dynamik von Tag zu Tag schätzt (Epskamp, 2017; Epskamp et al., 2018).

Ergebnisse: Die Netzwerkanalysen zeigten auf, welche Ressourcen besonders relevant für die Regulation des Lernprozesses und der Leistung, Lerndauer und Stresserleben der Studentin waren. Es fielen Ressourcen auf, die (1) besonders stark mit anderen interagierten, (2) die eine mediierende Rolle zwischen anderen Ressourcen einnahmen, und (3) die homöstatisch das System stabilisierten.

Diskussion: Die Ergebnisse einer Einzelfallanalyse lassen keine Generalisierung zu, erlauben jedoch erste theoretische Rückschlüsse. Da die Befunde teils theoretischen Annahmen widersprechen (z.B. dem law of the minimum, der besonderen Bedeutung von Protokapitalen), ist eher von individuellen Regulationsgefügen auszugehen als von allgemeingültigen, über Personen hinweg bestehende Mustern. Darüber hinaus liegt der Mehrwert der Netzwerkbasierten Einzelfallanalyse in einem detaillierten Einblick in ein individuelles Ressourcengefüge, der fundierte und effektive Fördermöglichkeiten eröffnet.

Implikationen für Theorie und Praxis: Die Methode der Einzelfallzeitreihe erlaubt es in der Praxis eine auf den Fall zugeschnittene Förderung zu entwickeln, wobei die Netzwerkmethode nicht nur die Schwierigkeiten visualisiert, sondern auch die effektivsten Ansatzstellen für Interventionen identifiziert. Weitere Untersuchungen zur Individualität der Ressourcenregulation sind angezeigt, um weiterführende theoretische Ableitungen vorzunehmen.

Literatur

Epskamp, S. (2017). graphicalVAR: Graphical VAR for experience sampling data (R package version 0.3.4). http://CRAN.R-project.org/package=graphicalVAR

Epskamp, S., van Borkulo, C. D., van der Veen, D. C., Servaas, M. N., Isvoranu, A. M., Riese, H., & Cramer, A. O. J. (2018). Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections. Clin Psychol Sci, 6(3), 416-427. https://doi.org/10.1177/2167702617744325

Ziegler, A., & Stoeger, H. (2011). Expertisierung als Adaptions- und Regulationsprozess: Die Rolle von Bildungs- und Lernkapital. In M. Dresel & L. Lämmle (Eds.), Motivation, Selbstregulation und Leistungsexzellenz (pp. 131-152). LIT.

The European Union and many governments of its (former) membership states demand more expertise on China, including Chinese language skills. Fostering Chinese language education requires a thorough understanding of the target group. This article aims to lay the foundation for a systematic and comprehensive analysis of the profile of at least one subgroup of Chinese learners, namely adult learners at universities in German-speaking countries. It presents the results of a preliminary meta-study comprising data from 217 Chinese learners. Among the information that can be extracted from various studies are personal data (age, gender, field of study, type of educational institution, first and, where applicable, second language) and data on individual learning biographies (number and specification of languages learned to date, use of learning strategies, and degree of language awareness). Results show that these learners have, on average, a comparable number of previously learned foreign languages. There are more multilingual speakers than, for example, among learners of Spanish. Experienced learners have certain preconditions and needs according to current L3 acquisition theories, which should be paid special attention to when teaching Chinese as a foreign language. Some didactic suggestions are put forward on how to integrate the results into the teaching of Chinese

Learning in the digital world requires not only technological skills for using digital tools but also ethical skills to critically reflect on (in)adequate digital media use and potential negative consequences. These skills are particularly crucial in professions dealing with public welfare and societal issues. Inter alia, those are teachers who educate the youth, legal professionals who judge (il)legal behavior regarding media, or computer scientists who bear responsibility when developing algorithms. Accordingly, higher education students studying teacher education, law studies, or computer science studies should develop ethical skills for the digital world. This study examined how higher education students perceive problematic media behaviors and which digital competences they regard relevant for ethical issues. In addition, the study investigated whether students of teacher education, law studies, and computer science studies differ in their perceptions. To this end, an online survey with N = 461 participating students was conducted. Study results indicated that higher education students perceived problematic media behaviors as such with posting inappropriate content identified as the most problematic. Furthermore, students considered several digital competences as relevant for ethical issues with protecting and acting safely as most relevant. In-depth analyses uncovered subject-specific differences with computer science students being most ethically savvy, albeit differences were only of small effect size. The study provides valuable insights into the intersection of digital competences, ethical considerations, and academic disciplines. In the future, longitudinal and training studies will help to understand how differences emerge and whether students of different study subjects benefit from digital ethics training.

Does the AI agree? Inter-rater agreement in learning diary evaluation

Theory

Learning diaries as formative assessments are promising to support the learning process and stimulate reflection. In an open learning diary, learners apply learning strategies to reflect on their learning and deepen their knowledge. To guide learners, learning diaries can be structured according to different learning strategies. However, grading and feedback on learning diaries is effortful for teachers. Artificial intelligence (AI) may assist teachers in the evaluation process. A prerequisite is that AI and teachers show high inter-rater agreement.

Research Questions

The current study aimed to analyze the agreement between teachers and ChatGPT-4o by examining four separately assessed learning strategy categories of a learning diary in adult education (organization, in-depth elaboration, transfer-supporting elaboration, and metacognition). The two research questions were:

• Q1: How accurate is the overall agreement between teachers and ChatGPT-4o, and are there differences across different learning strategy categories?

• Q2: Does the inter-rater agreement differ between teachers across the four learning strategy categories?

Method

Seven different adult education teachers and ChatGPT-4o evaluated a total of 540 learning diary entries. Each teacher assessed approximately 65 entries. Teachers were trained in criteria-based evaluation per learning strategy category. An engineered prompt supported the ChatGPT-4o model.

Teacher ratings served as the reference for the inter-rater agreement. Absolute accuracy and under-/overestimation (bias) were calculated for each learning strategy category as accuracy measures. Furthermore, overall accuracy values were calculated across the four categories for absolute accuracy and bias.

A doubly multivariate repeated measures ANOVA was conducted with the four learning strategy categories as repeated measures and absolute accuracy and bias as measures to test for accuracy differences between the learning strategy categories (Q1). Additionally, teacher was used as a between-subjects factor. Thus, the interaction of the learning strategy category and rating teacher regarding accuracy could be tested statistically (Q2).

Laser technology presents a compelling alternative to conventional methods for removing coatings from plastic and metallic parts, offering advantages over chemical solvents and media blasting for component reuse and recycling. This study investigates laser paint removal from thermoplastic materials, particularly those with complex 3D geometries. Utilizing a 1064 nm pulsed fiber laser, experiments were conducted to analyze paint ablation from plastic substrates and characterize the resulting surfaces. Experimental results on plastic substrates demonstrate the feasibility of complete paint removal while preserving the integrity of the thermoplastic. The laser's energy density, scanning speed, and spot size are identified as key parameters influencing removal efficiency and substrate integrity. The study presents the necessary steps and process conditions and analyzes the resulting quality of the processed surfaces. The study concludes that laser paint removal facilitates the reuse or high-quality recycling of plastic parts and can be an environmentally friendly, flexible, and highly efficient method for paint removal.

Reverse engineering encompasses an array of technologies and methods which produce results in terms of design, simulation and manufacturing. Options are pushed a little further by a new set of digital tools and scientific methods. Finite element methods (FEM) can validate models and propose structurally optimised geometries or attain prolonged life in service. 3D additive manufacturing processes can be simulated making it possible to pin point problematic areas. With respect to product design and digital manufacturing, reverse engineering uses a number of techniques, tools, methods and means of optimization which ease the process of re-manufacturing or use of an enhanced reinterpreted product. Of course, there are legal and ethical concerns that must be met within legal boundaries. Reverse engineering can be corroborated with other techniques of data interpretation and manipulation in such a way that results are both time-and-cost effective. Easy integration with Industry 5.0 concept is an ongoing process that proposes sustainable optimization of energy consumption, materials processing, and product lifecycles by prioritizing humans.

This study describes the development of an operation-independent simulation model for electrified die-casting foundries which use a smart grid system to cover their energy requirements. The model uses real weather and electricity price exchange data for the simulation period. It can be used to determine and compare electricity costs for production at specific times of day and year, as well as the economic efficiency of different photovoltaic (PV) system and electricity storage variants. It also enables the proportion of different energy sources for each configuration to be analysed. This can be carried out using the model for locations throughout Germany. Additionally, this paper presents exemplary simulation studies that demonstrate the model’s wide range of applications. The results provide an initial overview of the potential savings and optimisation. In the future, the model will provide a basis for determining optimum plant layouts and production times using simulation-based optimisation.

A systematic sequence of materials was investigated to develop phytic-acid (Phyt)–based flame retarded poly(lactide acid) (PLA), while factoring in molecular weight (MW), crystallinity and mechanical properties. Synergistic approaches were developed based on combinations with lignin and expandable graphite (EG), as well as by applying different Phyt salts of melamine (Mel), piperazine (Pip), and arginine (Arg). Compounds were twin screw extruded, injection molded, hot pressed and investigated with thermal analysis, size exclusion chromatography, infrared spectroscopy, tensile testing, limited oxygen index (LOI), UL 94, cone calorimeter, and scanning electron microscope. 16.7 wt.% flame retardant (FR) slightly enhances crystallization while MW remains unchanged in PLA Phyt Arg and PLA Phyt Mel. LOI was improved to 43.7 vol.% for PLA Phyt Arg, UL 94 V0 achieved for PLA Phyt Pip. Cone calorimeter results show total heat evolved reduced by 14 %, maximum average rate of heat emission 43 % lower, and peak heat release rate reduced by 50 % for PLA Phyt Mel. Phyt Mel combined with EG increased the char yield of PLA to 20 wt.% and 15.5 wt.% at 600 and 900 °C, respectively. Phyt is exploited to enhance char yield, stabilize the intumescent char, and lower the apparent effective heat of combustion. The combination of Phyt Mel and EG was proposed as an efficient FR for PLA via an evidence based developing route.

Creative insight is critical to adaptive leadership and innovation in increasingly complex environments. This article introduces Inspirational Intuition as a conceptual framework that integrates intuitive perception and knowledge imagination. Drawing on creativity theory, unconscious cognition, and leadership research, the article outlines four foundational dimensions: Self-Confident Acting, Creative Silence, Emotional Awareness, and Visionary Openness. These form the basis of the IKII-10, a reflective inventory designed for conceptual engagement with intuitive Human Resource Development (HRD) competencies. Rather than an empirical tool, the IKII-10 serves as a scaffold for further theory building, supporting a more holistic understanding of learning, leadership, and organisational development in the face of uncertainty.

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.

The European Union and many governments of its (former) membership states demand more expertise on China, including Chinese language skills. Fostering Chinese language education requires a thorough understanding of the target group. This article aims to lay the foundation for a systematic and comprehensive analysis of the profile of at least one subgroup of Chinese learners, namely adult learners at universities in German-speaking countries. It presents the results of a preliminary meta-study comprising data from 217 Chinese learners. Among the information that can be extracted from various studies are personal data (age, gender, field of study, type of educational institution, first and, where applicable, second language) and data on individual learning biographies (number and specification of languages learned to date, use of learning strategies, and degree of language awareness). Results show that these learners have, on average, a comparable number of previously learned foreign languages. There are more multilingual speakers than, for example, among learners of Spanish. Experienced learners have certain preconditions and needs according to current L3 acquisition theories, which should be paid special attention to when teaching Chinese as a foreign language. Some didactic suggestions are put forward on how to integrate the results into the teaching of Chinese. 

The exponential rise in global plastic consumption, coupled with its heavy dependence on fossil fuels, has resulted in severe environmental and ecological consequences—ranging from greenhouse gas emissions to widespread marine pollution. In response, bioplastics derived from renewable sources have emerged as a promising solution, particularly those synthesized from algae. This study explores the potential of algae-based bioplastics—produced from macroalgae (e.g., Ulva lactuca) and microalgae (e.g., Chlorella, Spirulina) - as a sustainable and circular alternative to conventional plastics. Owing to their rapid growth, high biomass yield, and non-reliance on arable land, algae offer distinct ecological advantages over traditional biomass sources like corn or starch. The research examines the types of algal bioplastics, key production processes, and the integration of genetic engineering techniques to enhance yield and biodegradability. It also addresses scalability, economic feasibility, and degradation by-product limitations. Furthermore, the study evaluates policy and regulatory frameworks that support the development and adoption of bio-based materials. The findings suggest that algae-derived bioplastics reduce reliance on fossil resources and align with circular economy principles by offering biodegradable, renewable, and low-impact alternatives. As global environmental concerns intensify, algae-based bioplastics may play a vital role in reshaping the future of sustainable materials and reducing plastic pollution at scale.