This work explores the mathematical modelling capabilities of various iterations of ChatGPT, focusing on their performance across tasks of differing complexity and openness. The study examines the abilities of GPT-3.5, GPT-4.0, and a more instructed version, GPT-MM, in multiple scenarios. It is observed that all versions demonstrate basic mathematical problem-solving skills. However, their effectiveness varies with increasing task complexity. While GPT-4.0 and GPT-MM show marginal improvements in providing detailed solutions, significant challenges persist, especially in moderate to complex modelling contexts where comprehending the nuances of tasks becomes challenging. Additionally, the study suggests that the openness of modelling tasks has a limited impact on performance, highlighting that mathematical and contextual complexities play more critical roles. The implications of these observations are discussed in terms of potential enhancements to teaching methodologies and the integration of AI tools like GPT in educational settings. This reiterates the importance of further research to fully understand the capabilities and limitations of AI tools and ensure their effective use in education.

Research on instructional quality has been of great interest for several decades, leading to an immense and diverse body of literature. However, due to different definitions and operationalisations, the picture of what characteristics are important for instructional quality is not entirely clear. Therefore, in this paper, a scoping review was performed to provide an overview of existing evidence of both generic and subject-didactic characteristics with regard to student performance. More precisely, this paper aims to (a) identify both generic and subject-didactic characteristics affecting student performance in mathematics in secondary school, (b) cluster these characteristics into categories to show areas for quality teaching, and (c) analyse and assess the effects of these characteristics on student performance to rate the scientific evidence in the context of the articles considered. The results reveal that teaching characteristics, and not just the instruments for recording the quality of teaching as described in previous research, can be placed on a continuum ranging from generic to subject-didactic. Moreover, on account of the inconsistent definition of subject-didactic characteristics, the category of ‘subject-didactic specifics’ needs further development to establish it as a separate category in empirical research. Finally, this study represents a further step toward understanding the effects of teaching characteristics on student performance by providing an overview of teaching characteristics and their effects and evidence.

School quality has become a guiding concept that increasingly shapes educational planning and school development. For many decades, it has been...

The discussion document Teachers of mathematics working and learning in collaborative groups (IPC, 2019) identifies “Roles, identities and interactions of various participants in mathematics teacher education” as one of four major themes needing further elaboration. In regard to this Theme C, the document indicates, among others, that collaborative groups can include different ‘actors’ in various combinations. These actors can have a variety of roles, which can shift over time. In collaborative interactions, the learning of all participants is also important. The document indicates six ‘actors’ in an exemplary way.

Zusammenfassung: Der Beitrag behandelt die Frage, ob sich österreichische Lehrkräfte hinsichtlich der Themen ihrer gewählten Fort- und Weiterbildungsveranstaltungen zu distinkten Gruppen zusammenfassen lassen und inwiefern sich diese in professionsrelevanten und berufsbiografischen Merkmalen unterscheiden. Dafür werden Daten aus TALIS 2024 analysiert ( N = 4335). Die Befunde zeigen, dass sich vier statistisch und inhaltlich unterscheidbare latente Cluster (LCA) bilden lassen, die sich in den Bereichen (1) unterrichtsfachliche, fachdidaktische und curriculumrelevante Themen, (2) Digitalisierung und Einsatz von KI, (3) Individualisierung, Förderung und Assessment sowie (4) Schulverwaltung und Administration systematisch unterscheiden. Die Cluster differenzieren sich zudem hinsichtlich der Schulform, der Überzeugung zur Wirkung von Fortbildungen, in Aspekten der Digitalisierung sowie in der professionellen Motivation und Überzeugung.