A simulation-based learning (SBL) approach that enables pre-service teachers (PSTs) to develop practice-based teaching skills within simulated classroom environments can be effective as a complementary element in increasing their teaching skills in teaching practicum. This research aims to test the effectiveness of an SBL approach in teaching practicum on PSTs’ Technological Pedagogical Content Knowledge (TPACK), classroom management, and lesson planning skills. A quasi-experimental design with a pre-post test control group was used. A total of 129 PSTs were divided into two groups. An SBL approach was applied to the experimental group, while the control group received training within the current teaching practicum course. The results show that, compared to the control group, the experimental group’s use of an SBL approach in teaching practicum significantly improved their TPACK, classroom management, and lesson planning skills. Overall, the findings of this study reveal the effectiveness of SBL-based teaching practicum and contribute to bridging theory and practice in teacher education by supporting the development of future teachers’ teaching skills.
Generative artificial intelligence (AI) systems can now reliably solve many standard tasks used in introductory physics courses, producing correct equations, graphs, and explanations. While this capability is often framed as an opportunity for efficiency or personalization, it also poses a subtle ethical and educational risk: students may increasingly submit correct results without engaging in the epistemic practices that define learning physics. This challenge has recently been described as the "boiling frog problem" because we may not fully recognize how rapidly AI capabilities are advancing and fail to respond with commensurate urgency. In this article, we argue that the central challenge of AI in physics education is not cheating or tool selection, but instructional design. Drawing on research on self-regulated learning, cognitive load, multiple representations, and hybrid intelligence, we propose a practical framework for cognitively activated learning activities that structures student activities before, during, and after AI use. Using an example from an introductory kinematics laboratory, we show how AI can be integrated in ways that preserve prediction, interpretation, and evaluation as core learning activities. Rather than treating AI as an answer-generating tool, the framework positions AI as an epistemic partner whose contributions are deliberately bounded and reflected upon.
Educators and students face significant challenges in addressing global issues in education. Although concepts such as Education for Sustainable Development or Global Citizenship Education have gained prominence, their strong emphasis on methodological considerations has left the conceptual foundations of global issues underdeveloped. This paper advances a consolidated conceptual framework that foregrounds the pragmatic, ethical, and moral dimensions of complexity inherent in global issues. By integrating Habermas’ discourse ethics with elements of ethical complexity theory developed by Paul Cilliers and Harry Kunneman, we elaborate these dimensions. Building on this perspective, we argue for the establishment of a robust conceptual foundation that enables a more systematic organization of teaching and learning in this field.
Self-regulation competence is known to be a critical driver of academic achievement, mental health, and lifelong success. However, research on education and educational practice in K-12 remains fragmented in that groups of researchers focus on conceptualizations from different traditions, including self-regulated learning (SRL), executive functions (EFs) and personality psychology (e.g., conscientiousness), and there is limited cross-talk between these groups. In this article, we propose an integrated multicomponent model that unifies these perspectives and conceptualizes self-regulation competence as a developmental “superpower” that is both predictive of a broad range of outcomes and amenable to educational support. We examine three core propositions: (1) Self-regulation competence—broadly defined—predicts important cognitive, social, and emotional outcomes in school and across the life span; (2) SRL, EFs, and conscientiousness show considerable conceptual and empirical overlap, supporting their integration into a comprehensive framework; and (3) self-regulation competence can be effectively fostered through targeted and embedded educational interventions. To fully develop and exploit the superpower qualities of self-regulation competence, promoting self-regulation competence should become a guiding principle of education systems. High-quality teaching—characterized by effective classroom management, cognitive activation, and student support—and whole-school approaches are needed to orchestrate the promotion of self-regulation competence in real-world educational contexts.
Complex mathematical practices, such as proving and calculating in geometry, draw on a range of students’ individual resources and may pose substantial challenges for students. The development of these resources and the skills required to enact these practices are affected by the cultural context and the role of proof in the mathematics curriculum. Drawing on cultural and curricular differences regarding the role of proof between Taiwan, as an East Asian country, and Germany, as a Western country, we compare students’ cognitive and affective resources for geometry calculation and proof, as well as their effects on performance between the two contexts in an experimental study with 519 secondary school students. The results showed only a few significant differences between the two countries, which can be explained by their cultural characteristics. For example, country-related differences in geometry proof and calculation performance were particularly pronounced for students with above-average geometry topic knowledge. The different role of proof in the curricula in the two countries resonates with significant differences in performance between proof and calculation performance in Germany, but not in Taiwan.
