Conference Agenda

In many education systems, schools are judged primarily by final exam results, with high scores often taken as indicators of quality. Yet this view is limited: some students enter with a strong educational background, while others begin at a lower level. Without distinguishing prior knowledge from progress made during schooling, outcomes may be misinterpreted and the actual contribution of schools overlooked.

The theoretical framework draws on value‑added models of school effectiveness (Hanushek & Rivkin, 2010; Koedel & Betts, 2011), which emphasize the importance of separating students’ initial achievement from the contribution of schools to subsequent progress. This approach is widely used in European educational research to promote fairer evaluation of school performance. Rather than focusing only on raw results, it highlights growth indicators, as reflected in policies in the UK, the Netherlands, Belgium, and Ireland (Timmermans & Thomas, 2015; Leckie & Goldstein, 2019; Brown, McNamara & O’Hara, 2016).

Research on language acquisition in school contexts (Baker, 2011) further underscores the role of instructional environments and pedagogical practices in shaping outcomes. This is especially relevant in multilingual education systems, where language development depends not only on individual ability but also on teaching quality and the broader learning context.

The aim of this study is to evaluate the contribution of Nazarbayev Intellectual schools (NIS) to students’ development in Kazakh and Russian by comparing entry scores obtained in the Grade 7 selection test with exit scores achieved in the Grade 12 external summative assessment. NIS is the flagship of Kazakhstani education. Admission is highly competitive, with on average 10 applicants per place, and only the strongest students succeed (Nazarbayev Intellectual schools, 2024). The schools admit talented, motivated students with the goal of fostering their growth into critical thinkers and independent learners. NIS campuses are located across Kazakhstan (Toybazarova & Nazarova, 2018).

This study addresses two research questions:

1. To what extent do entry scores reflect students’ initial Kazakh and Russian language proficiency at admission to NIS?
2. To what extent do exit scores reflect the schools’ contribution to language development?

The first question examines students’ starting point, highlighting how selection test results capture prior knowledge rather than the effect of schooling. The second investigates how Grade 12 external assessments, aligned with international benchmarks, reflect cumulative progress and the added value of schools by distinguishing between initial ability and growth achieved through instruction.

Together, these questions allow for a more accurate evaluation of school effectiveness in language development. By comparing entry and exit scores, the study shows why it is important to look beyond raw results. It also connects to wider European discussions about fair assessment, multilingual education, and how schools can be judged not only by final exams but by the progress they help students make.

A large body of international research has consistently documented positive associations between family socioeconomic background and academic achievement. Meta-analytic evidence generally indicates small to moderate effect sizes, suggesting that SES is a meaningful, though not deterministic, contributor to students’ learning outcomes (Harwell et al., 2017; Liu et al., 2022; Korous et al., 2022). At the same time, substantial variation exists in the reported magnitude of SES effects. While some large-scale syntheses report correlations in the moderate range (r ≈ .22–.28; Liu et al., 2022), others identify considerably weaker associations, particularly when controlling for various individual and school-level factors (Kim et al., 2019; Song & Tan, 2025). These findings suggest that SES effects are shaped not only by family-level resources but also by broader institutional, cultural, and policy environments.
Beyond differences in performance levels, prior research has also examined how socioeconomic inequalities relate to students’ developmental trajectories. Longitudinal and cross-cohort analyses indicate that although students from higher SES backgrounds consistently outperform their lower SES peers, socioeconomic background often shows limited influence on the rate of development over time. Instead, early differences tend to emerge at the beginning of schooling and remain relatively stable across subsequent grades, resulting in largely parallel developmental trajectories (Liu et al., 2022). This pattern suggests that schooling may neither substantially compensate for nor increase initial SES-related gaps in many educational systems.
Cross-national evidence highlights particularly strong SES effects in several European countries, including Hungary. According to recent PISA reports, Hungary belongs to the group of education systems in which students’ socioeconomic background explains a markedly larger share of variance in academic performance. In mathematics, for example, SES accounts for approximately 25% of performance variance in Hungary, compared to an OECD average of around 15% (OECD, 2023). These findings indicate pronounced educational inequalities and underline the importance of examining SES-related processes within the Hungarian context in greater depth.
Socioeconomic inequalities in educational outcomes are especially consequential when they concern fundamental learning-related abilities. Cognitive skills have been shown to play a central role in shaping individual life chances, income distribution, and long-term economic growth (Hanushek & Woessmann, 2008). Among these skills, inductive reasoning occupies a key position. Inductive reasoning supports learning across domains by enabling individuals to detect regularities, form generalizations, and transfer knowledge to new situations (Klauer & Phye, 2008). It is closely related to fluid intelligence and has been identified as a strong predictor of academic achievement (Perret, 2015; Van Vo & Csapó, 2022).
A large-scale study in Hungary has demonstrated substantial SES-related differences in students’ inductive reasoning between Grades 1 and 8, with children from more advantaged family backgrounds consistently achieving higher levels of performance and these differences remaining largely stable across grade levels (Molnár, 2025). However, this evidence is based on cross-sectional designs and relies on a narrow SES indicator, namely parental education alone (Molnár, 2025). Consequently, little is known about whether socioeconomic background influences not only the level but also the rate of development of inductive reasoning when examined within a longitudinal framework and using a more comprehensive conceptualization of SES.
Addressing this gap, the present study examines the longitudinal development of students’ inductive reasoning from Grades 3 to 7 and investigates how differences in socioeconomic background are associated with initial levels and growth trajectories across primary and lower secondary education.

In the TIMSS (the Trends in International Mathematics and Science Study) 2023 computer-based assessment, student process data was provided along with other achievement and context questionnaires data. Process data (e.g., response time) provide empirical evidence about the cognitive states and behaviors that occur while working on a test item and that influence how the measured construct(s) translate into item performance (Goldhammer & Zehner, 2017). By revealing underlying response mechanisms beyond the test score, these processes provide evidence supporting the validity of intended interpretations of test scores. This source of validity evidence is emphasized in the Standards for Educational and Psychological Testing (American Educational Research Association, American Psychological Association, and National Council on Measurement in Education, 2014). Examining response processes in TIMSS, as a large-scale assessment, provides valuable insights into the cognitive processes underlying item responses and contributes to a better understanding of the meaning and interpretation of test scores.

There are various approaches to examining response processes, but mixture item response theory (IRT) models have been widely used due to their strengths. Mixture IRT models combine features of traditional IRT models with latent class analysis (LCA) and assume the presence of hidden subpopulations in the data (Sen & Cohen, 2019), which makes them especially useful for examining students’ distinct response strategies. Huang, Luo, and Jeon (2025) reviewed previous models within the mixture IRT framework for studying item-response strategies and proposed a new response time-based mixture IRT model. They grouped existing approaches into two categories: examinee-level mixture models (e.g., Jeon & De Boeck, 2019) and examinee-by-item-level mixture models (e.g., Nagy & Robitzsch, 2021). While examinee-level models assume that test-takers employ the same response strategy throughout the assessment, examinee-by-item-level models allow test-takers to switch between response strategies (Huang et al., 2025). The examinee-by-item-level mixture model proposed by Huang et al. (2025) assumes the presence of multiple latent classes, including one regular ability-based class (modeled using a two-parameter logistic [2PL] IRT model) and one or more non-ability-based secondary classes (e.g., rapid guessing, knowledge retrieval), and incorporates response times to predict examinee-by-item class membership. This modeling approach allows examinees to switch between response strategies across items, which is likely in practice, as they may use different strategies depending on item properties (e.g., difficulty, complexity) or other factors (e.g., fatigue, lack of motivation).

In this study, we aim to explore which response strategies eighth-grade students use when responding to TIMSS 2023 mathematics items in Türkiye and Finland, two countries with similar average mathematics achievement. We apply a response time-based mixture IRT model proposed by Huang et al. (2025) to both easy and difficult item groups to examine potential differences in response processes across item difficulty levels.

This paper presents a large-scale institutional research project examining the drivers of student literacy growth in Kazakhstan. The study focuses on students in Grades 3, 4, and 7 across 13 schools within the BI Education network and aims to identify student-, teacher-, and family-level factors associated with literacy development over an academic year. While literacy outcomes are a central concern in international education systems, there remains a need for context-sensitive, data-informed approaches that move beyond cross-sectional achievement measures.

The study is guided by a value-added framework and contemporary models of school effectiveness, which emphasise growth-oriented assessment, instructional quality, and the role of home learning environments. Rather than relying solely on end-point achievement scores, the project examines literacy development longitudinally by administering equated initial and final literacy assessments. This approach allows for the estimation of student growth “beyond what might be expected,” accounting for prior achievement and contextual factors.

The research addresses three core questions:
(1) What student-level, teacher-level, and parental factors predict growth in literacy achievement across Grades 3, 4, and 7?
(2) To what extent do pedagogical practices and classroom-level variables explain variation in literacy growth after controlling for student background characteristics?
(3) How can value-added analysis be used to identify classrooms demonstrating high, moderate, and low contributions to student literacy growth?

The conceptual framework integrates multilevel models of learning with value-added analysis to capture the nested structure of educational data (students within classes, classes within schools). Student literacy growth is conceptualised as an outcome influenced by instructional practices, school organisation, and family-level support. The study also incorporates follow-up qualitative querying of selected classrooms to contextualise quantitative findings and explore pedagogical practices associated with higher-than-expected student growth.

The international relevance of this research lies in its methodological contribution and its focus on literacy development within a rapidly reforming education system. By combining psychometrically robust literacy assessments, multilevel modelling, and value-added analysis, the study offers an evidence-informed framework applicable to other education systems seeking to strengthen literacy outcomes through growth-oriented evaluation rather than high-stakes accountability alone. Findings are expected to inform both policy and practice by highlighting effective pedagogical and organisational conditions for literacy development.