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.

This study demonstrates that hydrogen enrichment in lean-burn spark-ignition engines can simultaneously improve three key performance metrics, thermal efficiency, combustion stability, and nitrogen oxide emissions, without requiring modifications to the engine hardware or ignition timing. This finding offers a novel control approach to a well-documented trade-off in existing research, where typically only two of these factors are improved at the expense of the third. Unlike previous studies, the present work achieves simultaneous improvement of all three metrics without hardware modification or ignition timing adjustment, relying solely on the optimization of the air–fuel equivalence ratio 𝜆. Experiments were conducted on a six-cylinder engine for combined heat and power application, fueled with hydrogen–natural gas blends containing up to 30% hydrogen by volume. By optimizing only the air–fuel equivalence ratio, it was possible to extend the lean-burn limit from 𝜆≈1.6 to 𝜆>1.9, reduce nitrogen oxide emissions by up to 70%, enhance thermal efficiency by up to 2.2 percentage points, and significantly improve combustion stability, reducing cycle-by-cycle variationsfrom 2.1% to 0.7%. A defined 𝜆 window was identified in which all three key performance indicators simultaneously meet or exceed the natural gas baseline. Within this window, balanced improvements in nitrogen oxide emissions, efficiency, and stability are achievable, although the individual maxima occur at different operating points. Cylinder pressure analysis confirmed that combustion dynamics can be realigned with original equipment manufacturer characteristics via mixture leaning alone, mitigating hydrogen-induced pressure increases to just 11% above the natural gas baseline. These results position hydrogen as a performance booster for natural gas engines in stationary applications, enabling cleaner, more efficient, and smoother operation without added system complexity. The key result is the identification of a 𝜆 window that enables simultaneous optimization of nitrogen oxide emissions, efficiency, and combustion stability using only mixture control.