Extended Reality (XR) technologies are beginning to enter the mainstream and have the potential to change the way humans interact with computers on a global scale. As with all powerful tools, XR not only has the potential for enormous good, but also brings in a new set of challenges for policy to guide its innovation and development. This paper draws on a wide range of expertise from academia, research and development, and industry to collectively provide guiding principles for XR policy. The authors began discussions and developed a framework at a workshop at ACM SIGCHI 2024 and the ideas presented here are the result of debate, discussion, refinement, and offer next steps for the development of pervasive XR. We present three main principles for XR policy: Trust, Agency, and Inclusivity, along with a cross-cutting theme of Future-Proofing. Each principle is broken down and we offer example implementations. This paper aims to build upon previous work and efforts for fair and equitable XR for all, and further dialogue towards tangible changes in policy to help guide responsible innovation in virtual worlds.

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

This vision paper outlines a future learning ecosystem in which AI agents act as tutorial companions, analyse learning progressions and make adaptive recommendations. The use of retrieval augmented generation (RAG) systems will make learning content accessible in a contextualised manner and generate individual feedback. Initial prototypes from the corporate world demonstrate the technical feasibility of this. We therefore assume that if AI-supported agents are successfully trialled, everyday teaching and learning at universities can be fundamentally changed in the direction of self-directed learning.

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.