Conference Agenda

Nordic education systems have long been associated with equity, social cohesion, and a strong commitment to providing equal learning opportunities for all students (Blossing et al., 2014; Imsen et al., 2017). Educational equity, understood as ensuring fair access to learning regardless of students’ background and providing learners with what they need, remains a core principle of this model (Levinson et al., 2022). Over recent decades, however, demographic change driven by migration, refugee movements, and broader social transformations has reshaped student populations and the everyday conditions of teaching, particularly in urban contexts (Brussino et al., 2021; OECD, 2023). Classrooms are becoming more heterogeneous along multiple dimensions, including students’ migration background, ethnicity, special educational needs, and socio-economic circumstances (Nilsen et al., 2025; Olsen & Hanssen, 2024; Teig et al., 2024). Rising socioeconomic and ethnic inequalities in student outcomes, alongside increasing school segregation by migration background, academic achievement, and socioeconomic status further contribute to this complexity in the Nordic countries (Nilsen et al., 2025; Sandsør et al., 2023; Yang Hansen et al., 2025).
As a result, Nordic classrooms are more diverse than earlier, and there is wide variation in language proficiency, academic readiness, special education needs, and social resources. These developments do not simply challenge established equity goals, but they also reconfigure what it means to teach in Nordic classrooms (Nilsen et al., 2025; Teig et al., 2024). Prior research shows that heterogeneity does not have negative effects by itself. Its consequences depend on mediating factors such as instructional quality, learning culture, and classroom management (Weintraub, 1997). Higher levels of diversity may therefore require adjustments in pedagogical, relational, and organizational practices, to avoid a decline in educational conditions. In turn, these shifts place new demands on teacher education, ongoing professional development, and support structures that can equip teachers to respond effectively to more complex classroom realities.
Against this background, the present study examines whether teachers’ professional outcomes (workload, well-being, job satisfaction with the school environment, need for professional development for teaching for diversity, student behaviour stress) have changed over time (from 2018 to 2024) and whether any such changes are mediated by the changes in classroom composition. To do so, we use data from the last two cycles (year 2018 and year 2024) of the Teaching and Learning International Survey (TALIS) across four Nordic countries. Classroom composition is treated as a potential mediating factor, allowing us to investigate which of its aspects mediate changes over time in teachers’ outcomes. Thus, we answer the following research questions:

RQ1: How have teachers’ workload, well-being, job satisfaction with the school environment, need for professional development for teaching for diversity and student behaviour stress changed from 2018 to 2024 in each of the Nordic countries?

RQ2: Among outcomes that exhibit change over time, to what extent are these changes associated with changes in classroom composition?

In educational settings, a central question is whether the collective class characteristics influence individual student achievement over and above students’ own characteristics (Televantou et al., 2015). These influences are commonly referred to as compositional effects (e.g., Becker et al., 2022). The effects are detected by aggregating student characteristics, such as prior achievement or socioeconomic status, to classroom or school level and estimating whether the aggregated measures influence the individual outcomes after controlling for the student-level variable on which aggregation is based (Lüdtke et al., 2008; Televantou et al., 2015).

Findings on composition effects of class-average prior achievement are, however, inconsistent. While many studies report positive composition effects (e.g., Nilsen et al., 2025; Stäbler et al., 2017), others find none (e.g., Dicke et al., 2026), and effect sizes vary widely and are sensitive to model specification (e.g., Becker et al., 2022; Televantou et al., 2015). One reason for this inconsistency is that composition effects are defined as the difference between within-class and between-class achievement relations, both of which are distorted when prior achievement is measured with error. Standard multilevel models may overestimate composition effects because they ignore both measurement error and sampling bias (Marsh et al., 2009).

Prior research shows that composition effects are sensitive to selection bias and measurement error (Becker et al., 2022; Marsh et al., 2009, 2023). Selection bias arises when models fail to adequately capture important aspects of student composition, causing between-class differences to reflect preexisting individual differences rather than true contextual effects. Becker et al. (2022) demonstrate that controlling for prior achievement substantially reduces this bias and that including covariates, such as socioeconomic background and migration status, further attenuates estimated composition effects. Measurement error introduces a second source of bias. When achievement is measured unreliably, multilevel models tend to underestimate individual-level effect and, as a result, overestimate the group-level effect because the regression coefficient for the aggregated score picks up variance left unexplained due to measurement error. Becker et al. (2022) show that correcting for measurement error reduces spurious inflation of composition effects, whereas correcting for sampling error in class means prevents underestimation due to imperfect aggregation.

Against this background, the present study builds on this line of research by estimating a sequence of three multilevel models that impose progressively stronger corrections for measurement error and aggregation bias. Applying this methodological approach allows us to assess the models across multiple national contexts. In this respect, the study contributes by extending and validating the method beyond a single-country setting. In addition, as socioeconomic and demographic sorting into classrooms can produce biased composition effects, we estimate three additional models where student-level and class-level background controls are introduced incrementally, to distinguish peer achievement effects from contextual SES composition. In the present study, we address the following research question:

What is the magnitude of composition effects, and how does accounting for measurement error alter these estimates?

Out-of-field (OOF) teaching –when teachers are assigned to subjects outside their formal area of training – has become widespread internationally (Price et al., 2019). While often used to address shortages of subject-qualified teachers, OOF teaching raises concerns about instructional quality (Hobbs & Porsch, 2021; Porsch & Whannell, 2019).

Assuming that subject-specific teacher training leads to the development of specific pedagogical and content knowledge (Baumert et al., 2010), differences arise between OOF and in-field teachers. OOF teachers miss certain important learning opportunities; the resulting lack of knowledge of OOF teachers may impact their quality of teaching and instructional practice (Goos & Guerin, 2022) as knowledge (particularly in mathematics) is the foundation of effective teaching and good pedagogy (Goos et al., 2020). Further, in terms of specific knowledge domains in mathematics, research reveals a lower level of algebra knowledge of OOF teachers compared to the algebra knowledge of in-field teachers (Osei & Agyei, 2023).

In line with this assumption, OOF teachers frequently encounter challenges when bridging complex concepts, connecting content to students’ prior knowledge, and addressing diverse learning needs (Du Plessis, 2015). They also tend to focus their instruction on students with average performance, as the knowledge required to assist weaker or higher-performing students is not always adequately developed in such teachers (Du Plessis, 2015). Notably, OOF teachers often rate their own teaching quality as low (Porsch & Wilden, 2022) and heavily rely on textbooks for instruction (Du Plessis, 2013; Napier et al., 2020). In the field of mathematics, OOF teachers possess more direct transmission beliefs and teacher-centered teaching methodologies (Lane & Ní Ríordáin, 2020).

However, evidence regarding the effects of OOF teaching on student achievement remains unclear, partly due to methodological and conceptual heterogeneity across studies (Porsch & Whannell, 2019). Moreover, little is known about whether teaching experience can compensate for limited formal subject preparation, and whether OOF teaching affects not only cognitive outcomes but also motivational outcomes such as students’ interest in mathematics.

In Austria, OOF teaching is common practice in lower secondary education, especially in non-academic school tracks (Sengschmid et al., 2025). Yet empirical evidence on its effects on instructional quality, student performance, and motivation is scarce. Addressing this gap, the present study investigates the role of OOF teaching in lower secondary mathematics education in Austria, drawing on data from the 2023 cycle of the Trends in International Mathematics and Science Study (TIMSS) 2023.

The study examines both direct and indirect effects of OOF teaching on students’ mathematics achievement and interest in mathematics. Instructional quality is conceptualized as a potential mediating mechanism, while teaching experience is considered as a moderator that may reduce or enhance the impact of OOF teaching. Instructional quality is operationalized through three core dimensions: clarity of instruction, cognitive activation, and classroom management.

Given the pronounced structural differences between academic (AHS) and non-academic (MS) lower secondary schools in Austria, and the uneven distribution of OOF teaching across these tracks, analyses are conducted separately by school-type. The central research questions are:

1. What influence does out-of-field teaching in mathematics have on instructional quality and student outcomes in Austria?
2. To what extent are these relationships mediated by instructional quality and moderated by teaching experience?

Methodologically, the study employs multilevel structural equation modeling to account for the nested data structure and to simultaneously model direct and indirect effects. Instructional quality is modeled as classroom-level latent constructs based on student reports. Bayesian Markov Chain Monte Carlo estimation is applied to address missing data and estimation uncertainty. By jointly considering cognitive and motivational outcomes within a differentiated by school-type, this study contributes to a nuanced understanding of the implications of OOF teaching.

Across many education systems, the recruitment of mathematics teachers relies heavily on two easily observed credentials: teachers’ highest education level and subject major (Ingersoll, 2007). These credentials are routinely used as screening criteria in hiring and placement decisions because they are administratively simple and assumed to reflect professional capacity. However, there is no shared or consistent standard for how these two indicators should be interpreted or weighted in practice. Even within the same education system, requirements and preferences can differ across regions and local authorities (Ma et al., 2021). For example, regions may differ in how strictly “major matching” is enforced, whether a Master’s degree is treated as an advantage or a requirement, and whether certain combinations of education level and major are prioritised for particular grades or schools. This creates uncertainty about what these credentials actually capture and whether they are consistently linked to students’ mathematics outcomes.

This paper uses international large-scale assessment data to examine how teachers’ formal qualifications relate to individual students’ mathematics achievement. It brings together two TIMSS cycles that capture different stages of mathematics learning: TIMSS 2023 Grade 8 (lower-secondary) and TIMSS Advanced 2015 (students in the final year of secondary schooling who have taken or are taking advanced mathematics, typically around age 18). Using a common modelling strategy across the two datasets, the study examines whether the same teacher qualifications matter in similar ways across stages and whether the combination of teachers’ education level and subject preparation is especially relevant for advanced mathematics. The main aim of this paper is to provide comparative evidence that can help inform mathematics teacher training and entry requirements, and support stage-specific staffing decisions. It does not make causal claims, but it uses international data to examine whether teacher education level and subject major are consistently associated with students’ mathematics achievement.

Research questions

1. Are students’ mathematics achievement scores associated with their teacher’s highest education level and subject major, after accounting for student background and school context?

2. Is there an interaction between teachers’ highest education level and subject major in relation to students’ mathematics achievement?

3. Are these patterns similar between lower-secondary mathematics (TIMSS 2023 Grade 8) and advanced mathematics at the upper-secondary level (TIMSS Advanced 2015)?