An Evolution of Mathematics Curriculum

This chapter highlights some considerations for mathematics curriculum reform, based on the series of curriculum analysis reports from OECD Future of Education and Skills 2030 (E2030) project. The reports discuss significant issues related to holistic curriculum development generally, rather than on reform in specific subject areas, and provide examples of innovation from around the world. The reports include:

Findings from this series of curriculum analysis reports are interpreted in this report though the lens of mathematics as a discipline, wherein the specific implications for mathematics curriculum reform are discussed.

Mathematical competencies comprise the knowledge and skills required to develop and apply mathematics, as well as the values and attitudes (dispositions) necessary to apply such knowledge and skills in appropriate contexts. The E2030 Mathematics framework reaffirms the importance of several areas of disciplinary knowledge that are integral to the OECD’s Programme for International Student Assessment (PISA) mathematics assessment framework 2022 (OECD, 2023[6])

Traditional topics such as number, geometry and algebra are easily recognised within these important areas. Indeed, as discussed in previous chapters of this report, the findings from the Mathematics Curriculum Document Analysis (MCDA) and Curriculum Content Mapping (CCM) studies found that core foundational competencies, such as numeracy and data literacy, together with mathematical knowledge on essential content areas like algebra, geometry and number systems, continue to serve as the foundation of mathematics education worldwide. Moreover, the studies revealed that most countries/jurisdictions share a similar structure in terms of topic sequencing and instructional tasks (Schmidt et al., 2022[7]; OECD, 2020[1]).

At the same time, new demands are emerging for mathematics curricula in response to global and societal challenges, such as demographic shifts like population ageing, health management, and global concerns such as climate change. Growing economic inequality, the evolving demands of the modern workplace for higher-order thinking and collaboration, and rapid advancements in digital technologies are also shaping the need for more interdisciplinary approaches in education. While foundational mathematical knowledge remains crucial, these changes highlight the necessity of integrating mathematics with other disciplines to equip students with the skills required to tackle complex, real-world problems, as is stressed in both learning and assessment frameworks (see Table 1.1 in Chapter 1).

This section will illustrate some of the concrete examples of how mathematics curriculum accommodate 21st-century demands, reflecting the diverse approaches to preparing students for citizenship and for work.

As discussed in Chapter 1, the OECD Learning Compass for Mathematics sets out a broad vision for learning mathematics, for students to be able to navigate through unfamiliar contexts and shape a better future (OECD, 2023[8]). While the compass is not designed to be a curriculum document itself, it acknowledges that influences on student learning go beyond the education system and offers considerations to policymakers involved in curriculum design.

Key concepts and constructs central to the compass, among others, include:

• Student agency, co-agency and collective agency. Agentic students are driven by a sense of purpose, intrinsic motivation and responsibility to influence people, events and circumstances around them. Co-agency and collective agency recognise that students participate in social settings and interact with others, which also guides and influences student personal growth (OECD, 2019[9]). The implication for the enacted mathematic curriculum is that students’ agency, co-agency and collective agency are developed through opportunities to exercise control over their own learning and to participate in communities of practice through collaborative sense-making, in which meaningful situations are investigated by students.

• Critical thinking is crucial for solving complex problems where students need to evaluate patterns, question assumptions and devise strategies for solutions. In this sense, critical thinking enables students to engage deeply with mathematical content. Learning mathematics also requires critical thinking as students must understand abstract concepts and apply them to new situations, fostering their ability to think logically and analytically.

• Curiosity and creativity are both a means and an outcome of mathematics education. Solving non-routine mathematical problems often requires thinking creatively, particularly when standard procedures fail to provide solutions. In turn, mathematics also encourages creativity by presenting students with open-ended problems, which require innovative approaches to develop multiple solutions or explore different strategies.

• Research and inquiry skills also contribute significantly to mathematics education. Students learn to formulate hypotheses, explore mathematical questions and investigate patterns or real-world applications. This promotes an inquisitive mindset, fostering independence as students engage in deeper inquiry-based learning.

• Persistence and resilience are equally important. Problem solving in mathematics is often iterative and challenging, requiring students to persist through failure and uncertainty. These skills prepare students to tackle real-world problems and instil a sense of perseverance, which extends beyond the classroom into everyday life.

These competencies are not only essential for mastering mathematical concepts, but also for studies outside of math classrooms. Furthermore, these competencies are also important beyond school – for citizenship and for work.

To promote such competencies, partnerships – including those between schools and higher education – are crucial to supporting schools in curriculum design and implementation; see Box 3.1 for an example.

Box 3.2 serves as another good example that fosters critical 21^st-century competencies in mathematics education by embedding curiosity, creativity and problem solving into mathematical tasks, allowing students to grow in both their mathematical understanding and broader competency development.

Box 3.3 also serves as a good example of how student agency can become a core driver of advanced mathematics learning in school.

In today’s rapidly evolving technological landscape, mathematics education is expanding to include new topics that address the demands of the digital age. In addition to traditional areas such as numbers, algebra and geometry, contemporary topics like mathematical reasoning (with an understanding of how mathematicians think), statistics and data science are gaining prominence. These areas are critical for equipping students with the skills needed to navigate a world increasingly driven by technology, automation and data.

Many examples of epistemic knowledge are also included in the PISA framework (OECD, 2023[6]), with an aim of students understanding the ways in which knowledge is generated within the disciplines of mathematics and statistics.

• mathematical (including algebraic and geometric) and statistical reasoning;

• number systems and their algebraic properties;

• mathematics as a system based on abstractions and symbolic representation;

• the structure of mathematics and its regularities;

• functional relationships between quantities;

• mathematics modelling as a lens to the real world;

• variance as the heart of statistics;

• history of mathematics as a human activity.

These examples are not mutually exclusive. For example, representation is integral to mathematical and statistical reasoning, and to the structure of mathematics as a discipline. The main implication for curriculum writers is to ensure that there are opportunities for students to practice like mathematicians and to reflect on when a disciplinary methodology is in play. Inclusion of processes, competencies, disciplinary capabilities or proficiencies (whichever term is used) should be a key feature of 21^st-century mathematics curricula.

The integration of data science into mathematics curricula reflects the growing importance of data in nearly every sector, from business to healthcare. Students are now being taught to collect, process and analyse large datasets to make informed decisions. Statistics, a fundamental component of data science, plays a crucial role in helping students understand variability, probability and statistical inference – skills necessary to draw accurate conclusions from data. Computational thinking – the ability to break down complex problems, recognise patterns and develop algorithmic solutions – is now recognised as a critical skill across disciplines. In mathematics education, it encourages students to think logically and analytically, building a strong foundation for problem solving.

The use of computer-based modelling and simulations in mathematics classrooms is revolutionising how students engage with abstract concepts and apply them to real-world situations. Modelling is now a key component in fields such as engineering, finance, environmental science and healthcare. By integrating simulations into the curriculum, students can explore complex systems – like climate models, population growth, or disease spread – through mathematical algorithms.

Interdisciplinary learning, particularly through STEM (Science, Technology, Engineering and Mathematics) and STEAM (including the Arts), is recognised as essential for helping students connect knowledge across fields and view the world through varied disciplinary lenses. Teaching mathematics in the context of STEM/STEAM can help prepare students to tackle modern workplace challenges that demand collaborative, multidisciplinary thinking (OECD, 2023[8]).

By combining the abstract nature of mathematics with tangible applications in engineering, technology and the arts, students see how mathematical concepts apply to real-world tasks – such as designing bridges, coding or analysing data – bridging theory and practice. This interdisciplinary approach cultivates cognitive flexibility and adaptability, crucial for careers requiring diverse skills (Honey, Pearson and Schweingruber, 2014[10]). Engaging in projects like sustainable urban development allows students to apply math in calculating population density or resource needs, fostering motivation by illustrating math’s practical value (Honey, Pearson and Schweingruber, 2014[10]; Modeste et al., 2023[11]). STEAM further enhances learning by incorporating creativity, as seen, for example, in geometry projects that merge mathematical rigour with aesthetic design, allowing students to view mathematics as both a precise tool and a means of creative expression, deepening appreciation and understanding of the subject (Perignat and Katz-Buonincontro, 2019[12]).

While integrative STEM/STEAM holds great promise for fostering interdisciplinary connections, its effective implementation within mathematics education requires thoughtful curriculum design, teacher training and tailored assessment strategies to overcome several challenges. Teachers need deep content knowledge across disciplines and the ability to create projects that are relevant and maintain mathematical rigour. Assessment can be particularly complex, as interdisciplinary projects often lack standardised frameworks, necessitating custom rubrics that assess both subject knowledge and broader skills, which is a time-intensive task. Additionally, disparities in resources and technology access mean that students in under-resourced schools may miss out on quality interdisciplinary learning, underscoring the need for investment in training, materials and infrastructure to ensure equitable access to STEM/STEAM education.

Digital curricula and digital textbooks together have the potential to transform mathematics education by incorporating interactive tools, simulations and adaptive learning features that can make complex concepts more accessible, engaging and suited to individual learning needs. Digital resources such as GeoGebra, Desmos and TinkerPlots enable students to visualise and experiment with mathematical models at their own pace. For instance, in geometry, students can manipulate shapes in real time to explore concepts like area and transformations, while in statistics, simulations help them understand probability distributions through hands-on, repeatable experiments. These tools can help make abstract concepts tangible and relevant, bridging theory and practice while preparing students for fields like engineering and data science.

Digital textbooks complement digital curricula by embedding multimedia resources, interactive exercises and adaptive assessments directly within the material. Unlike static print resources, digital textbooks allow students to engage with concepts through videos, animations and instant feedback. This adaptability supports self-paced learning, enabling students to progress at the speed that suits them best, which is particularly beneficial in diverse classrooms. For instance, a digital algebra textbook might include an interactive tutorial on solving equations, where students can see the effects of variable changes immediately and take as much time as needed to master the topic.

Furthermore, digital textbooks can enhance personalised learning by identifying areas where individual students may need additional support and offering targeted resources to reinforce understanding. In a geometry unit, for instance, students who need more practice with volume calculations can receive supplementary exercises within the textbook itself. This personalisation ensures that each student has access to tailored learning resources, making the experience more relevant and effective.

A key benefit of digital curricula and textbooks is their potential to provide personalised feedback to students. Many platforms offer real-time feedback on exercises and assessments, helping students immediately understand their mistakes and correct them. For example, if a student struggles with certain algebraic steps, the system can highlight areas of difficulty and offer targeted hints or alternative explanations. This immediate, tailored feedback helps students address gaps in understanding right away, building confidence and improving learning outcomes.

Digital curricula and textbooks support not only self-directed independent learning (Box 3.4) but also collaborative and interactive learning, with tools for discussion, shared projects and peer engagement. For example, students can work together on trigonometry problems, using embedded Desmos tools to adjust angles and observe changes collaboratively, while teachers monitor and provide feedback, fostering an interactive classroom dynamic. The combined approach of digital curricula and textbooks provides a holistic, flexible and responsive learning experience, allowing students to build foundational math skills, digital literacy and collaboration abilities.

The rapid development of AI in educational contexts has sparked significant interest due to its potential to reduce teacher workload and enhance both student learning and performance. However, the full extent of the benefits and limitations of integrating these tools into educational settings remains largely unexplored. For instance, despite advancements in AI, tools like ChatGPT have demonstrated performance limitations in standardised mathematical assessments. An OECD study revealed that in November 2022, GPT-3.5 successfully answered 35% of a set of PISA mathematics tasks, significantly underperforming compared to humans, who successfully answered 51% of the tasks on average. By March 2023, GPT-4's performance had improved to 40% of the tasks successfully completed (OECD, 2023[13]). Although this marks a notable improvement, it also highlights persistent limitations and risks.

Technology has long been integral to teaching and learning in schools, including mathematics education, encompassing not only digital curricula but also essential tools and infrastructure, such as graphing calculators and digital whiteboards. While digital tools – and increasingly, AI – are pushing the boundaries of what is possible in education, much remains to be discovered about their efficacy in genuinely enhancing student learning. For example, the capacity of AI to deliver truly personalised learning experiences is still under investigation, with ongoing questions about whether AI can understand students as comprehensively as teachers do, beyond merely analysing performance on pre-programmed tests.

Regarding the use of AI in assessment, particularly the application of Large Language Models (LLMs) for grading student work, the field is still in its early stages despite years of development in automated scoring systems. The current use of LLMs in educational assessments is pioneering yet experimental, indicating that while the technology holds promise, substantial development and evaluation are needed to determine its viability and effectiveness (Hao et al., 2024[14]).

While the potential of digital technologies, particularly AI, to transform mathematics education is undeniable, their effective implementation carries inherent risks and limitations. To maximise the benefits and mitigate the risks associated with their use in classrooms, it is essential that the integration of these technologies into curricula and teaching practices be complemented by comprehensive teacher training and development. This important discussion about digital technology's capabilities and its practical implications in educational settings highlights the need for continued and more thorough examination in a dedicated analysis elsewhere.

Curriculum overload occurs when the quantity of content required to be taught exceeds the available instructional time or capacity (OECD, 2020[2]; Erstad and Voogt, 2018[15]). Curriculum overload commonly occurs when policymakers attempt to meet the competing demands of different stakeholder groups or societal changes by introducing new topics into the curriculum without removing or adjusting existing ones. This expansion can take the form of adding new subjects or embedding additional topics within existing subjects. Both approaches significantly risk overwhelming an already crowded curriculum, potentially hindering effective teaching and learning. Today, this challenge is further amplified as policymakers strive to integrate 21st-century competencies – such as critical thinking, creativity and digital literacy – into curricula, intensifying the pressure on instructional time and content management.

Despite the growing number of topics to be covered in mathematics education, the amount of teaching time available has changed very little across OECD countries in recent years. According to Education at a Glance data, mathematics remains a core subject in countries’/jurisdictions’ curricula alongside reading, writing and literature, natural sciences, and second language subjects. Incorporating modern competencies alongside traditional mathematical content can also lead to a curriculum imbalance. For instance, while fields like algebra and geometry may dominate the curriculum, newer areas like data analysis and mathematical modelling, which are crucial for real-world applications, may be underrepresented. This imbalance may prevent students from gaining a holistic mathematical education that prepares them for interdisciplinary challenges. The dilemma thus persists as students still need to develop a solid understanding of traditional mathematics concepts to be able to apply them in different transversal contexts. Figure 3.1 and Figure 3.2 show the change in average instruction time in mathematics between 2011 and 2023 in primary and lower secondary education. Figure 3.1 reveals, with the exception of a few countries/jurisdictions – such as Czechia, the French Community of Belgium, Türkiye, Ireland and Germany – most countries/jurisdictions saw either no change or a slight decrease in the average compulsory instruction time dedicated to mathematics in primary education over the past 12 years. Similarly, Figure 3.2 shows that only a handful of countries, including Denmark and Italy, saw an increase in mathematics instruction time at the lower secondary level, while it remained stable in most other countries/jurisdictions with available data. These instruction hours translate to around 16% of total instructional time in primary education and 13% in lower secondary education dedicated to mathematics in 2023 on average across OECD countries/jurisdictions (OECD, 2023[16]).

Responding to demands for curriculum expansion (incorporation of new content and competencies into curriculum) by increasing instruction time may not only be unrealistic given the relative stability of instruction time in most countries, it may also be counter-productive. International data also show that excessive time spent on learning activities (in and outside of school) is not necessarily related to better students’ outcomes (Figure 3.3). In fact, students in several high-performing systems spend less time on learning activities than their peers in lower-performing countries. The quality of their learning time is clearly more important than the number of hours they spend on it (OECD, 2023[18]).

Curriculum expansion can result in content overload as mathematics curricula typically cover a wide range of essential topics like numbers, algebra and geometry. When additional subjects or topics are added, the breadth of the curriculum increases, potentially at the cost of depth of learning. Teachers may be forced to skim through important mathematical concepts to keep pace with the breadth of material, making it difficult for students to develop a deep understanding of foundational knowledge.

Incorporating modern competencies alongside traditional mathematical content can also lead to a curriculum imbalance. For instance, while fields like algebra and geometry may dominate the curriculum, newer areas like data analysis and mathematical modelling, which are crucial for real-world applications, may be underrepresented. This imbalance may prevent students from gaining a holistic mathematical education that prepares them for interdisciplinary challenges. The dilemma thus persists as students still need to develop a solid understanding of traditional mathematics concepts to be able to apply them in different transversal contexts.

Other sources of curriculum overload may come from the sheer size of curriculum documents and from teachers’ interpretations about what they believe needs to be covered in a school year. For example, content overload is often linked to the extensive size of curriculum-setting statutory documents, which can include a vast amount of subject content and objectives. Several studies have noted that the physical size of curriculum documents can contribute to perceived overload (FitzPatrick and O’ Shea, 2013[19]; Voogt, Nieveen and Klopping, 2017[20]; Hong and Youngs, 2019[21]). The more pages and words these documents contain, the longer it takes for teachers to comprehend the curriculum requirements. The extensive documentation can be a strong indicator of general overcrowding, suggesting that the more detailed and voluminous the curriculum documents, the more challenging it becomes for educators to quickly grasp and implement the required teaching objectives effectively, and not be overwhelmed by their volume.

Perceived curriculum overload, on the other hand, refers to the sense among teachers and students that the curriculum demands more than what can be reasonably managed with the given time and resources (OECD, 2020[2]; Kuiper, Nieveen and Berkvens, 2013[22]). Unlike actual overload, which arises from an objectively excessive curriculum, perceived overload often stems from subjective experiences shaped by the number of topics to be covered, the frequency and type of assessments and learning materials, and school expectations (OECD, 2020[2]). In mathematics, the intense focus on high-stake assessments can drive teachers to cover more material than required, prioritising test preparation over deeper, more meaningful learning. This “teaching to the test” culture, combined with a reliance on extensive textbooks and frequent assessments, creates an overwhelming experience for students and challenges that are difficult for policymakers to address as these pressures are often deeply embedded within local educational practices (OECD, 2020[2]; Jennings and Bearak, 2014[23]). Research has suggested that some design principles can be used by policymakers and curriculum designers in order to minimise the risks of curriculum overload. They are described as follows.

Three curriculum design principles can be applied as a strategy to avoid mathematics curriculum overload: focus, rigour and coherence. These principles provide a framework for balancing the integration of new demands, such as the incorporation of 21st-century competencies, with traditional content, ensuring that curricula remain both manageable and impactful. Each of the three principles has its own challenges for implementation and should be used jointly to avoid unintended consequences of using them in isolation (OECD, 2020[2]).

To mitigate the effects of curriculum overload, focus becomes a crucial design principle. Instead of expanding the curriculum by adding numerous new topics, emphasis should be placed on fewer, high-leverage ideas. For example, data literacy, an increasingly essential skill, can be embedded within core mathematical topics such as descriptive statistics and probability distributions. By concentrating on a smaller number of key mathematical concepts, students can explore these topics in greater depth, resulting in better mastery and application of knowledge. In-depth focus on a smaller number of key ideas is positively associated with student performance in the Trends in International Mathematics and Science Study (TIMSS) at 9 and 13 years of age (Schmidt et al., 2001[24]). More emphasis on teaching for conceptual understanding also positively associates with high achievement (OECD, 2020[2]; OECD, 2013[25]; Echazarra et al., 2016[26]). An example of how curriculum overload can be mitigated by focusing on key or “big” ideas in mathematics can be seen in Box 3.5.

Big ideas are key concepts that are essential for foundational knowledge, that are transferable to other topics in mathematics or other learning areas, that endure over time and appear in basic as well as in advanced topics (OECD, 2020[2]).

Focusing on big ideas as an approach to mitigate curriculum overload can be challenging, as it may meet resistance from stakeholders who wish to defend their specific subjects or interests. Reducing content can also be perceived as lowering educational standards, risking backlash that could result in future curriculum expansions or increased instructional time (OECD, 2020[2]). Decisions to streamline an overcrowded curriculum can benefit from the involvement of stakeholders to avoid such pitfalls.

Another example of how curriculum overload can be addressed by focusing on key or “big” ideas in mathematics can be seen in Box 3.6.

It is important to aim for a sensible balance between breadth and depth of content in curricula. Breadth means the number of subjects included in the curriculum and the number of topics to be taught within subjects. Depth means the degree to which students have opportunities to explore and understand what they are learning, to solve problems and to connect ideas. Rigour aims to ensure the latter; it is about maintaining challenging content that promotes deep thinking and reflecting (Schmidt, Wang and McKnight, 2005[27]). This implies that curriculum goals must include application of knowledge and skills, along with application of conceptual knowledge, to realistic situations and transfer to unfamiliar contexts. While instructional focus is facilitated by reduction of content to a small number of key ideas, rigour requires deliberate prescription in curriculum documents and resources that support the curriculum.

In a mathematics curriculum that integrates 21st-century competencies, deeper understanding can be encouraged by linking conceptual knowledge to real-world applications. For example, computational thinking and data science can be embedded into core topics. Computational thinking can be introduced through algorithmic problem solving in algebra, where students write simple algorithms to solve equations or model real-world scenarios using programming. Moreover, interdisciplinary connections to STEM fields are becoming increasingly important. Integrating these skills not only strengthens the mathematics curriculum but also equips students with the necessary problem-solving skills for today's digital world. This approach ensures that students engage in cognitively demanding tasks while mastering key concepts like algebra and geometry. In PISA, teaching strategies identified with conceptual understanding are associated with higher student performance (OECD, 2013[25]). They are captured by the index of cognitive-activation instruction, composed of students’ answers to the following prompts:

• The teacher asks questions that make us reflect on the problem.

• The teacher gives problems that require us to think for an extended time.

• The teacher asks us to decide on our own procedures for solving complex problems.

• The teacher presents problems for which there is no immediately obvious method of solution.

• The teacher presents problems in different contexts so that students know whether they have understood the concepts.

• The teacher helps us to learn from mistakes we have made.

• The teacher asks us to explain how we have solved a problem.

• The teacher presents problems that require students to apply what they have learned to new contexts.

• The teacher gives problems that can be solved in several different ways.

To further strengthen the curriculum, it is crucial to emphasise the balance between procedural fluency – a skill often prioritised in vocational and basic education programmes – and deeper conceptual understanding. This focus ensures that students not only learn how to perform mathematical operations but also understand the underlying concepts, which is essential for developing higher-order competencies that are often overlooked in less-advanced educational settings (OECD, 2024[28]).

Like any other design choice, the implementation of a rigorous curriculum poses several challenges, such as balancing rigour with accessibility. If a curriculum is too rigorous, it risks alienating students, particularly those from disadvantaged backgrounds who may not have access to the same resources or support as their peers. A curriculum that is overly challenging can lead to frustration, disengagement and even increased dropout rates among students who struggle to keep up with the pace and demands of the content. Achieving the right balance between focus and rigour is therefore essential (OECD, 2020[2]).

A well-designed curriculum must also exhibit coherence, which involves organising learning in a logical progression that connects topics and competencies. Coherence in mathematics curriculum can be understood in terms of vertical and horizontal connections. Vertical coherence refers to the logical progression of mathematical ideas through different grade levels. This can support students to build upon previous knowledge and progress from earlier to later grades. Horizontal coherence refers to making connections between different mathematical topics within the same grade (to support students in seeing how various mathematical concepts are interrelated) (Peters, 2024[29]; Schmidt, Wang and McKnight, 2005[27]).

Coherence is also reflected in the way curricula integrate topics, as well as in the way that progression is organised. Reduction to a small number of key ideas facilitates important connections. For example, calculation with decimals might be taught though measurement situations. Properties of two- and three-dimensional shapes and measurement of attributes such as perimeter, volume and surface area might be learned together. Geometric pattern, especially growing and repeating sequences, might be used to develop relations at all levels.

When adding new content or removing old content from the curriculum, it is crucial to maintain the coherence of the curriculum, ensuring that key concepts are built upon across grades and subjects without unnecessary overlap or gaps in learning. Eliminating redundancies (unnecessary repetition) is crucial, and requires good judgement, since mastering of certain topics and ideas in mathematics does require some practice and repetition in order to automate sequences of steps, thus supporting greater levels of proficiency (Jablonka and Bergsten, 2021[30]). This often means that subject experts work together to manage cross-disciplinary co-ordination and to ensure that the integrity and logic of individual disciplines are preserved.

The spiral curriculum, introduced by Bruner (1960[31]), plays a key role in achieving coherence by reintroducing key topics over time with increasing complexity. This approach is particularly effective in mathematics, where foundational concepts such as number sense, algebra and geometry need to be revisited and expanded upon as students advance in their learning trajectory. The spiral design ensures that students do not merely encounter topics once but build a deeper understanding of them as their knowledge and cognitive abilities develop. In mathematics education, coherence is crucial for enabling students to connect new learning with what they have previously mastered. The spiral approach reduces the need for extensive review by trusting that prior learning has been effectively absorbed, allowing for more instructional time to be dedicated to exploring new applications or deeper facets of a concept.

Moreover, research on learning trajectories, such as that by Clements and Sarama (2020[32]), provides a strong foundation for improving coherence in curricula; trajectories map out typical pathways that students follow in developing mathematical thinking, offering guidance on when and how to introduce particular concepts. Coherence is enhanced when curriculum designers align content with these trajectories, ensuring that the progression of topics is developmentally appropriate. However, a challenge arises in the varied nature of research on learning progressions. Studies may differ in terms of age groups, methodologies and content focus, making it difficult to create a unified approach. Nonetheless, ongoing research promises to integrate these findings into a more cohesive framework for curriculum design.

By applying all three principles: focus, rigour and coherence together, policymakers can design mathematics curricula that balance depth with breadth, ensuring that students develop essential 21^st-century competencies along with key mathematical knowledge without risking curriculum overload.

Equity in education seeks to ensure that every student has access to the resources, opportunities and support needed to reach their full potential. Unlike equality, which involves the provision of the same level of resources to all students, equity tailors these resources to meet individual needs, recognising that diverse learners may require varied levels of support to succeed (OECD, 2021[3]). Meanwhile, inclusion in curriculum development aims to provide every learner with a high-quality curriculum that allows them to achieve their full potential, embracing their diverse characteristics, needs, abilities and expectations. This approach focuses on removing structural and cultural barriers, including bias and discrimination (OECD, 2021[3]).

Despite ongoing efforts, achieving equity in mathematics education remains a work in progress across OECD countries. Certain student sub-groups confront unique barriers, contributing to persistent achievement gaps. PISA results over the years on learning and equity have consistently highlighted that female students, socio-economically disadvantaged students, students with an immigrant background, and students with special education needs frequently underperform in mathematics compared to their peers. Moreover, the design of educational pathways can further exacerbate these equity gaps. Students who struggle in school, particularly in mathematics, are frequently guided toward vocational education and training (VET) programmes or lower-level academic classes, which typically set limited expectations for developing higher-order competencies such as mathematical reasoning and problem solving. In contrast, educational systems that integrate these competencies across all levels, as observed in countries like Ireland and Poland, suggest potential strategies for reducing these gaps (OECD, 2024[28]).

The gender gap in mathematics performance, as reported by both PISA and TIMSS, highlights ongoing disparities, though the extent varies by region and educational level. According to PISA 2022, boys scored an average of nine points higher than girls in mathematics across OECD countries, a gap that has remained largely unchanged since 2018 due to declining performance for both genders (OECD, 2023[33]).

Similarly, TIMSS 2019 data from 4^th-grade assessments found that boys tended to outperform girls in close to half of the participating countries, with girls scoring higher in only a few countries, and about half of countries showing no gender difference. By 8^th grade, gender equity was even more prominent, with most countries showing little to no difference in average achievement between boys and girls, although a few leaned toward either boys or girls performing better. Over time, TIMSS data show that gender gaps tend to remain stable within countries, although some nations like Chinese Taipei, England, and Hong Kong (China) have successfully closed gaps that previously favoured boys, while others, including Germany and Singapore, saw new or widening gaps favouring boys from 2015 to 2019 (Mullis et al., 2020[34]).

The socio-economic gap in mathematics performance is a significant issue across educational systems. PISA 2022 data reveal that nearly half (47%) of socio-economically disadvantaged students scored below proficiency Level 2 in mathematics, compared to only 14% of their advantaged peers – a notable 33 percentage-point difference on average across OECD countries (see Figure 3.4). In some cases, this gap is even more pronounced; for example, it exceeds 50 percentage points in Romania and the Slovak Republic. PISA’s socio-economic gradient metric further illustrates this disparity, with a steeper gradient indicating greater inequity in educational outcomes (Willms, 2006[35]). TIMSS indirectly measures socio-economic status through indicators like the availability of educational resources at home, the number of books, internet access and parents' education levels. Findings consistently show that students from homes with more educational resources tend to achieve higher in mathematics and science at both the 4^th and 8^th-grade levels (Mullis et al., 2020[34]).

PISA data also highlight a consistent "immigration gap" in mathematics performance, where non-immigrant students score, on average, higher than immigrant students. This gap is notably influenced by socio-economic and linguistic barriers faced by immigrant students. Before accounting for these factors, non-immigrant students across OECD countries scored 29 points higher than their immigrant peers, but the gap reduced to 15 points after adjusting for socio-economic status and further to 5 points when considering language spoken at home (OECD, 2023[33]).

Students with special educational needs (SEN) face significant challenges in their mathematics learning. While international comparative data on mathematics achievement for SEN students is not readily available – largely due to varying definitions and classifications across countries – national studies suggest that these students consistently perform below their peers in mathematics (Gottardis, Nunes and Lunt, 2011[36]) (Gottardis, Nunes and Lunt, 2011[36]; Mazzocco et al., 2013[37]). This gap is largely due to the unique challenges these students face, which require specialised support and resources. The OECD categorises SEN into three broad groups: learning disabilities, physical impairments, and mental health conditions (OECD, 2023[38]) – all of which can have an impact on students’ ability to learn mathematics.

Learning disabilities are neurological conditions that affect skills such as language processing, mathematical calculations, and attention. Common learning disabilities include dyslexia, dyscalculia, dysgraphia, and Auditory Processing Disorder, which can significantly impact mathematical understanding. For example, students with dyscalculia may struggle with basic numerical concepts, making it difficult to progress in mathematics without tailored support (Chen and Li, 2014[39]; Mazzocco et al., 2013[37]).

Physical disabilities can affect students' ability to access information and participate in classroom activities. For instance, students with visual impairments may struggle with interpreting visual aids, a common feature in mathematics instruction, while those with hearing impairments may face challenges in following verbal instructions (Gottardis, Nunes and Lunt, 2011[36]; Spinczyk et al., 2019[40]).

Mental health issues, such as anxiety disorders, ADHD and Autism Spectrum Disorder, also impact students' learning experiences. These conditions can affect focus, impulse control and social interactions, all of which are essential for engaging with complex mathematical tasks (Bullen et al., 2020[41]; Oswald et al., 2015[42]). Furthermore, the school environment itself can contribute to mental health challenges, with factors like bullying and social isolation exacerbating conditions like anxiety and depression, which in turn hinder academic progress (Samara et al., 2021[43]; Yu and Zhao, 2021[44]).

Addressing equity in mathematics education requires rethinking traditional teaching methods. Research highlights the need to create an inclusive environment that recognises and supports the diverse needs of learners (Gervasoni and Lindenskov, 2010[45]; Lambert, 2021[46]). To effectively address these gaps, teachers would need comprehensive training on the factors that exacerbate performance disparities. This includes understanding how reminding students of their group identity can lead to stereotype threat, a phenomenon that has been shown to negatively impact student performance (Beilock, 2008[47]; Beilock, Rydell and McConnell, 2007[48]). Other critical factors include the strategic choice of materials that avoid reinforcing stereotypes, managing time pressure to reduce anxiety, providing supports for executive function and self-regulation, and ensuring even availability of accommodations like text-to-speech. Such measures are essential for fostering a supportive and equitable learning environment.

Building on the need for comprehensive teacher training and awareness, one approach that has been found effective in promoting equity and inclusion is the implementation of Universal Design for Learning (UDL), which provides a framework for designing flexible learning environments that cater to all students. Universal Design for Learning (UDL) is a framework that aims to make education accessible for all students by designing curricula and learning environments to meet diverse needs (Meyer and Rose, 2000[49]). UDL emphasises removing learning barriers through three main principles: engagement (the “why” of learning), representation (the “what” of learning), and action and expression (the “how” of learning). These principles can guide educators to create more inclusive learning experiences by offering various ways to engage with content, represent information and demonstrate understanding. While UDL has been successfully implemented to support students with special needs, its adaptable nature makes it beneficial for all learners, fostering an inclusive educational environment (OECD, 2021[3]).

In mathematics, UDL can help bridge equity gaps by accommodating learner diversity. For example, engagement strategies might involve adapting to students’ interests and providing real-world problem-solving contexts to enhance motivation. Representation can incorporate visual aids, multi-lingual resources, or adaptive digital tools to support comprehension (Lambert, 2021[46]; Abrahamson et al., 2018[50]). Lastly, varied methods of expression, such as interactive tasks or collaborative projects, allow students to demonstrate their understanding in ways that best suit their abilities (Lambert et al., 2021[51]). By designing with UDL, mathematics education can move away from a one-size-fits-all approach, instead supporting all learners – especially those from marginalised backgrounds or with learning disabilities – to access and excel in meaningful mathematical experiences.

To address the equity gaps present in mathematics education, many OECD member and partner countries, schools and teachers are adopting specific curriculum innovations designed to make mathematics curricula more inclusive and relevant for diverse learners. The E2030 report on adapting curriculum to bridge equity gaps (OECD, 2021[3]) identifies four major types of curriculum innovations that, when carefully designed and implemented, can help transform mathematics education to better meet the needs of all students in the 21st century:

• digital curriculum;

• personalised curriculum;

• cross-curricular content and competency-based curriculum;

• flexible curriculum.

In mathematics education, a digital curriculum holds the potential to address equity gaps by making learning more accessible for students with diverse needs, including those with special educational needs. By incorporating assistive technologies – such as screen readers, motion and voice recognition apps, Braille devices, augmented reality, AI and wearable tech – a digital curriculum can offer tailored support for students who face unique learning challenges, as well as enhance learning for all students.

Furthermore, digital tools can personalise learning pathways through interactive tutoring systems that deliver real-time, continuous feedback, allowing students to progress at their own pace. For teachers, learning analytics and Big Data provide valuable insights for early identification of learning difficulties, enabling more responsive goal setting and targeted support for individual learners. Digital curricula can also engage students at risk of disengagement or dropout by integrating gamified learning experiences and virtual reality.

Digital textbooks can also add another layer of support, especially for low-performing students, by enabling them to move back and forth across different grade-level content. This flexibility allows students to revisit foundational concepts they may have struggled with, providing a scaffolded learning experience that strengthens understanding and confidence.

Strategic implementation of digital technology in curriculum design thus enables more equitable learning opportunities beyond those possible in traditional, face-to-face mathematics environments. Many countries are already leveraging digital platforms to host and access documents and resources (such as Chile's Aula 360 platform, focused on mathematics, see Box 3.7), using tools like e-texts, videos and interactive learning objects. In the PQC survey of 31 OECD countries, 71% reported adopting or developing digital tools, including virtual learning environments (OECD, 2021[3]). Furthermore, the potential of artificial intelligence and augmented reality is beginning to be explored to personalise learning experiences based on data gathered from students' interactions with digital resources.

A personalised curriculum, also known as an individualised or tailored curriculum, aligns learning opportunities with the unique needs, skills, interests, learning preferences and cultural backgrounds of students (Pane, 2017[52]). By adapting content to each student's starting point and goals, personalised curricula can effectively address equity gaps, providing a tailored pathway that meets diverse learners where they are and supports their progress. Personalised approaches to mathematics have been shown to help students’ performance in mathematics (Prain et al., 2013[53]).

For students with SEN, personalisation ensures that educational strategies are aligned with individual learning profiles. This might involve setting specific learning goals and using resources suited to the students’ needs, such as tactile learning aids for students with sensory impairments or cognitive support for students with learning disabilities like dyscalculia. For students from different linguistic and cultural backgrounds, a personalised curriculum respects and incorporates students' identities by offering mathematics content that is culturally relevant and linguistically accessible. This approach can involve translating materials or integrating cultural examples within mathematics problems, helping students see themselves within the curriculum, inspiring increased engagement.

Furthermore, personalised curricula address socio-economic gaps by recognising the varied experiences of students from different backgrounds (Prain et al., 2013[53]). Teachers can adapt tasks to reflect real-world contexts that are familiar to students or use adaptive technology to provide timely feedback based on individual progress. For students from low-income backgrounds, early and targeted interventions in foundational mathematical skills can make a significant difference in closing gaps over time, enabling students to build confidence and resilience in mathematics.

Adopting a cross-curricular and competency-based approach to mathematics education can also play a pivotal role in addressing equity gaps in mathematics. This approach allows mathematics to be integrated with other disciplines, making learning more meaningful and accessible to a diverse range of students by demonstrating the practical and interdisciplinary applications of mathematical concepts.

For students from socio-economically disadvantaged backgrounds or minority groups, cross-curricular content can provide authentic learning experiences, connecting mathematics with real-world contexts that resonate with their lived experiences. This approach can also enhance motivation and engagement by presenting mathematics not as an isolated subject but as a tool for solving practical problems across disciplines like science, literacy and social studies. For instance, inquiry-based learning, which is commonly used in science, can be adapted in mathematics to involve students in exploring mathematical concepts through real-life problem solving and argumentation. As an example, students could use statistical methods to analyse air quality data collected from different city areas, allowing them to apply mathematical concepts directly to meaningful community issues and enhancing their skills in argumentation and knowledge construction. Research indicates that such an integrative curricula, can have positive impacts on learning outcomes for low-income and minority students (Hand et al., 2018[54]; Tong et al., 2014[55]; Thadani et al., 2010[56]).

Competency-based curricula can further address equity gaps by focusing on skills that can be applied across various contexts, including critical thinking and problem solving. For example, a mathematics program designed for students with SEN might employ a competency-based approach by integrating explicit instruction with hands-on activities, such as using tactile learning tools to solve algebraic equations. This method allows students to physically engage with the concepts, facilitating a deeper understanding and retention of mathematical principles. Such structured, step-by-step approaches have proven beneficial for all learners, including those with learning disabilities, by providing concrete learning strategies tailored to their unique needs (Therrien et al., 2017[57]).

For cross-curricular approaches to succeed in closing equity gaps, adequate teacher preparation and support are essential. Integrative curricula often require teachers to adopt new pedagogical practices and adapt to interdisciplinary content, which may challenge traditional teaching routines. Professional development opportunities and access to curriculum resources are crucial to ensure that teachers can confidently implement these approaches, ultimately contributing to a more equitable and inclusive mathematics education.

Flexible curricula are another policy approach that has the potential to address equity gaps in mathematics education by adapting to the diverse needs of students, particularly those from disadvantaged or underrepresented backgrounds. While research on flexible curricula is limited and results vary, successful implementations tend to incorporate adaptive instruction and targeted activities that support individual learning trajectories (OECD, 2021[4]). Flexibility, when thoughtfully designed and implemented, allows local education providers to adjust content, pedagogy and assessments to better serve students, enhancing inclusivity and accessibility.

A flexible curriculum enables customisation in multiple areas, such as the time and place of learning, which can greatly benefit students with different socio-economic needs. For instance, flexibility in learning schedules and locations, such as through blended or digital learning models, supports students who might otherwise struggle with traditional school hours due to external responsibilities or accessibility issues (Jonker, März and Voogt, 2020[58]). Such options can be particularly valuable for young carers or those with family responsibilities, creating opportunities to continue education while balancing other life demands.

In addition to flexible learning environments, flexibility in assessment can play a crucial role in achieving equity. By incorporating various assessment formats, educators can more accurately capture students' understanding, especially those with learning disabilities or who struggle in standardised testing environments. Formative assessments, for instance, reduce stress and provide students with ongoing feedback, creating a supportive atmosphere that encourages growth over high-stakes performance (Hayward and Spencer, 2010[59]). For students with special needs, this flexibility allows for alternative forms of demonstrating learning better aligning with their abilities and strengths, embodying principles of UDL.

Flexible curricula can also support diverse mathematical learning pathways and levels, as seen in countries like New Zealand, Ireland and Singapore, where students can choose options suited to their strengths, needs, and future ambitions. For example, Singapore’s tiered mathematics levels (H1, H2, and H3) offer clear pathways that help students tailor their education to their goals, potentially reducing disengagement. In contrast, England’s post-16 educational framework lacks similarly structured options, representing a potential fragility in addressing diverse student needs compared to other countries (OECD, 2024[28]).

Lastly, flexible curricula that incorporate real-world applications and service learning can engage students from all backgrounds, making mathematics more relevant and accessible. These practical learning opportunities, such as apprenticeships or community-based projects, provide authentic learning experiences that connect mathematical concepts to students' social and cultural contexts, enhancing both engagement and comprehension (OECD, 2021[4]).

Attitudes towards mathematics play a critical role in shaping students’ learning experiences and their overall performance in the subject. Positive attitudes such as self-confidence, enjoyment and persistence are often linked to better academic outcomes, whereas negative attitudes like anxiety, fear or a lack of interest can severely hinder students' ability to engage with mathematical content (Mazana, Montero and Casmir, 2018[60]; Wen and Dubé, 2022[61]; Berger, Mackenzie and Holmes, 2020[62]). This is especially important as attitudes not only influence how students approach mathematics but also affect their long-term engagement with the subject, determining whether they continue to study mathematics at higher levels or avoid it altogether. A recent UNESCO report states that mathematics education is crucial not only for developing reflective and critical citizens who can handle the mathematical demands of everyday life but also for preparing a sufficient number of mathematicians and scientists capable of meeting the challenges of the contemporary world (UNESCO, 2022[63]). Encouraging students’ interest in mathematics so they become lifelong practising mathematicians is therefore important to both their personal well-being and to society as a whole.

Students' attitudes towards mathematics are fundamental to their personal development, influencing not only their academic achievement but also their sense of self-confidence, resilience and willingness to engage with challenging material. A positive relationship with mathematics can support students' growth mindsets and their capacity for problem solving, which are beneficial both within and beyond the classroom. This section explores specific factors that shape individual attitudes, such as mathematics anxiety and fear of failure, as well as the significance of fostering teacher competencies to support a positive learning environment.

Students’ attitudes towards mathematics (ATM) develop in response to their interactions with others and their personal experiences of learning and doing mathematics. Negative experiences can lead to long-term negative reactions towards mathematics. Mathematics anxiety and fear of failure are well-documented in research for their negative impact on learning and on self-confidence.

Mathematics anxiety is a widespread issue that significantly impacts students' academic achievement and long-term engagement with the subject. It is characterised by feelings of stress, tension and apprehension when faced with mathematical tasks, often leading to avoidant behaviour and poor performance (Ashcraft and Kirk, 2001[64]; Richardson and Suinn, 1972[65]). This anxiety can create a self-perpetuating cycle in which students with lower confidence in their mathematical abilities underperform, reinforcing their negative attitudes and increasing their anxiety. Research suggests that mathematics anxiety affects not only day-to-day performance but also long-term decisions regarding further studies or careers requiring mathematical skills (Brown, Brown and Bibby, 2008[66]).

PISA 2022 data reinforce the well-established negative correlation between mathematics performance and anxiety. In every education system that participated in PISA 2022, students with higher levels of mathematics anxiety consistently performed worse than their peers with lower anxiety levels. This relationship holds true regardless of socio-economic status or school characteristics, demonstrating the widespread impact of anxiety on mathematics outcomes (OECD, 2023[33]). On average across OECD countries, a one-point increase in the index of mathematics anxiety corresponds to an 18-point decrease in mathematics performance. Internationally, mathematics anxiety accounts for approximately 25% of the variation in student achievement across countries. This is particularly notable among the 17 countries with the highest levels of mathematics anxiety, all of which performed below the OECD average in mathematics, with 13 of them scoring below 400 points on the PISA scale (see Figure 3.5).

However, Figure 3.5 also shows that top-performing countries exhibit wide variation in levels of mathematics anxiety. For example, while East Asian countries like Japan and Chinese Taipei excel in mathematics, they report higher-than-average levels of anxiety. On the other hand, countries like Denmark, Finland and the Netherlands demonstrate both high performance and lower levels of anxiety.

The negative impact is especially pronounced in examination settings where time pressure exacerbates anxiety, contributing to what Ashcraft & Moore (2009[67]) call an “affective drop,” where the individual’s true mathematical ability is masked by their anxiety. Moreover, mathematics anxiety can result in broader negative effects on students’ emotional and psychological well-being. Chronic anxiety around mathematics may contribute to generalised academic stress, affecting overall attitude toward schooling. For many students, their relationship with mathematics becomes one of frustration and avoidance, which can significantly limit their career aspirations in STEM fields and other disciplines requiring quantitative skills (Dowker, Sarkar and Looi, 2016[68]). The role of education systems in addressing mathematics anxiety is crucial. Developing teacher competencies in identifying and managing this anxiety, promoting positive mathematical attitudes, and fostering a supportive learning environment is essential for improving outcomes. One key finding from the PISA 2022 data is the potential role of positive attitudes, such as a growth mindset, in mitigating mathematics anxiety. A belief that abilities can be developed and improved over time, rather than being fixed, has been linked to lower anxiety levels and better performance, suggesting that fostering these attitudes could be a powerful tool in reducing the negative impact of mathematics anxiety (OECD, 2023[33]). Teacher training programmes that focus on emotional and cognitive strategies to reduce anxiety can empower educators to help students break free from the cycle of poor performance and fear of mathematics.

Fear of failure is defined as the tendency to avoid mistakes, as they may be perceived as shameful or indicative of a lack of innate ability, potentially jeopardising one's future prospects (Atkinson, 1957[69]; Conroy, Willow and Metzler, 2002[70]). This fear stems from the pressure to meet academic expectations, avoid mistakes, and succeed in high-stakes assessments. In mathematics, where precision and correctness are highly emphasised, fear of failure can become particularly pronounced, leading students to avoid challenges and risk-taking in problem solving, which are essential for deep learning.

Additionally, fear of failure is associated with broader psychological effects, such as lower social and emotional well-being (Elliot and Sheldon, 1997[71]) and higher rates of stress, anxiety, burnout and depression (Gustafsson, Sagar and Stenling, 2016[72]; Sagar, Lavallee and Spray, 2007[73]). Research also indicates that fear of failure disproportionately affects girls, who tend to experience more negative outcomes such as reduced confidence and increased anxiety when faced with failure (Alkhazaleh and Mahasneh, 2016[74]; McGregor and Elliot, 2005[75]; Wach et al., 2015[76]; Borgonovi and Han, 2020[77]). PISA 2018 findings also indicate that fear of failure is a much better predictor of academic performance amongst girls than amongst boys (OECD, 2020[78]). Girls who expressed a greater fear of failure scored significantly higher in mathematics and science in PISA 2018 compared to girls with less fear of failure, with differences of five and eight points, respectively. In contrast, boys who expressed a greater fear of failure showed only marginal improvements in their scores (OECD, 2020[78]). The gender gap in fear of failure was particularly noticeable among top-performing students, with girls exhibiting a fear of failure 0.5 units higher than boys at this level, compared to a gap of 0.3 units among low achievers. On the other hand, the PISA results also revealed that fear of failure is negatively associated with life satisfaction (OECD, 2020[78]).

Many factors impact attitudes towards mathematics (ATM), some external to schooling and many within educational experiences. The ATM of family members, peers and wider society, particularly about the usefulness and importance of the subject, and the perceived enjoyment of mathematical challenge, strongly impact the attitudes adopted by students. Media also influence the messaging about the worth of learning and engaging in mathematics. According to Kiwanuka et al. (2016[79]) and Mata, Monteiro and Peixoto (2012[80]), factors affecting ATM that are within the sphere of influence of education systems include:

• supportive and knowledgeable teachers who model learning behaviours;

• high teacher expectations for student engagement and learning;

• students’ perceptions of personal success;

• connections between mathematics and real-life;

• interaction and collaboration with other students;

• appropriate levels of challenge and support;

• high-quality learning tasks;

• opportunities for personal control of learning, such as goal setting and choice of task.

Furthermore, the design of curriculum and pedagogical approaches can also play a crucial role in shaping students' enjoyment of mathematics. For example, by incorporating mathematical reasoning, problem solving and real-world applications, curricula can significantly enhance students' engagement and enjoyment of the subject. This approach can encourage students to view mathematics as a dynamic and applicable field, fostering a deeper appreciation and a more positive attitude towards the subject.

Certain competencies can also play a pivotal role in helping students overcome the challenges of mathematics anxiety and fear of failure. These competencies are believed to not only improve performance but also shape positive attitudes toward learning mathematics. One of the key competencies that students should possess is a growth mindset, which encourages them to believe that effort, practice and persistence can help them develop, improve and succeed over time. PISA 2022 findings show that students with a growth mindset are less anxious about mathematics and perform better, as they view challenges and setbacks as opportunities to grow (OECD, 2023[33]). Moreover, students with a growth mindset tend to fear failure less than those without it (OECD, 2020[78]). Having a growth mindset can foster resilience, making students more likely to persist in the face of difficulties. Other research also indicates that supporting students' beliefs about their competency in mathematics may be more effective for reducing anxiety than focusing solely on achievement value (Li et al., 2021[81]).

Certain teaching strategies have also been found to improve students’ attitudes towards mathematics. Teaching strategies that favour cognitive activation, for example, play an important role in improving students' attitudes toward mathematics. Cognitive activation encourages students to think deeply about mathematical concepts, connect ideas and apply their knowledge to different situations. According to PISA 2012 Results, teachers who engage students in cognitively activating tasks, such as asking them to explain their reasoning, work through complex problems, and approach tasks from multiple angles, help enhance students' perseverance, motivation and confidence. This approach not only boosts performance but also fosters positive attitudes towards mathematics, reducing anxiety and increasing engagement (OECD, 2013[25]).

Mathematics education also plays a vital role in fostering attitudes and values that contribute to the well-being of society. Beyond its cognitive and technical benefits, mathematics can instil qualities essential for responsible and engaged citizenship, including integrity, co-operation and a commitment to fairness. By engaging students in real-world contexts and collaborative problem solving, mathematics can cultivate values that empower them to participate thoughtfully and ethically in society.

For instance, mathematics education can play a critical role in fostering active citizenship by equipping students with the skills needed to engage in informed, reflective and responsible decision making. According to Maass et al. (2019[82]), mathematics education that integrates socio-scientific issues and emphasises inquiry-based learning can empower students to address real-world challenges such as environmental concerns, economic inequalities and public health issues, all of which require mathematical literacy and ethical judgement. This approach encourages students not only to develop competencies like critical thinking and problem solving but also to engage with the societal implications of these skills, preparing them to become active, responsible citizens capable of navigating complex social issues through informed mathematical perspectives.

Similarly, mathematics education can contribute to citizenship by enabling students to interpret and critically analyse information prevalent in today's data-driven society (Geiger, Gal and Graven, 2023[83]). Students trained in mathematics can better understand social, economic and political issues, thus participating more meaningfully in civic life. This aligns with the idea of mathematics as a tool for justice-oriented citizenship, where individuals are prepared to question and address inequities and contribute constructively to society. By learning to apply mathematical reasoning to civic issues, students can contribute thoughtfully to democratic processes and promote societal well-being through informed decision making (Geiger, Gal and Graven, 2023[83]).

Box 3.8 highlights one example from Japan on how mathematics education can be leveraged to foster social decision making, empathy and ethical values among students. These cases demonstrate how engaging students in inclusive, collaborative problem solving and value-driven projects within mathematics can further reinforce their role as informed, conscientious citizens.

Embedding attitudes and values in curriculum is not without risks and dilemmas. Variations in the attitudes and values held by stakeholders in the education system make establishing consensus on a uniform set of important attitudes and values challenging. Countries may experience dissonance between teacher beliefs and the attitudes and values espoused in curriculum statements. The Mathematics Curriculum Document Analysis study (Schmidt et al., 2022[7]) shows that commonly used texts in all participating countries fall short in providing the types of collaborative, realistic applications, and higher-order thinking tasks, that can lead to societal innovation. Dissonance between curriculum standards, including attitudes and values, and resources presents a significant barrier to implementation. Likewise, countries report that assessments often do not reflect a focus on attitudes and values. That is particularly true of high-stakes examinations. When considering how to embed key attitudes and values in mathematics curricula, policymakers are encouraged to simultaneously consider how to align them with pedagogies and assessment. Box 3.9 illustrates an example from Singapore of a close alignment of curriculum and teacher practices in terms of attitudes and values.

Flexibility and autonomy in curriculum design are essential for ensuring education systems can respond to the diverse needs of students and rapidly changing societal demands. Flexibility allows schools and educators to adjust learning goals, content, pedagogy and assessments to meet the varying needs of learners, creating more relevant and personalised educational experiences. Autonomy, on the other hand, empowers local authorities, schools and teachers to make decisions about curriculum implementation, enhancing their ability to tailor education to specific local contexts (OECD, 2024[5]). Curriculum flexibility has been discussed earlier in this report, so this section will focus mostly on school autonomy.

The latest round of PISA data has identified school autonomy on curriculum as one important factor found in resilient education systems. These are systems that continued to perform well on various fronts in spite of the recent disruption of the COVID-19 pandemic – they maintained high levels of student performance in mathematics while ensuring a good record on equity measures and on students’ well-being (Figure 3.6). Resilient systems were able to adapt and respond to unexpected circumstances (e.g. school closures), and provide continued support and learning opportunities to students (leveraging technology for remote learning as needed), thus becoming stronger and better prepared to face adversity (OECD, 2023[18]).

Behind the resilience demonstrated in these education systems is the agility of well-prepared school leaders and educators at the local level who understand the pressing needs of students and can articulate adequate responses. School autonomy, when well thought out, can make systems stronger and more adaptable.

However, school autonomy in itself is insufficient to support positive student learning and well-being outcomes. Striking the right balance between centralised control and school autonomy can be challenging in practice (Ko, 2016[84]).While granting teachers greater autonomy can enable them to tailor their teaching to their students’ needs and priorities, excessive autonomy may backfire and overwhelm them, especially when they lack sufficient guidance on how to implement the curriculum (and lack opportunities for ongoing professional development), when they do not fully understand the intentions of the curriculum, or when school autonomy is not part of their local school traditions (OECD, 2024[5]). This is particularly true in mathematics, where teachers need support in integrating flexible approaches that do not compromise the rigour of mathematical content.

The E2030 MCDA study (the main findings of which were discussed in Chapter 2) conducted a focused analysis on decision making, specifically related to the mathematics curriculum. This analysis, seen in Table 3.1 highlights the roles and responsibilities shared across various education system levels (national, regional, local (school and teachers)) on curriculum decisions across 19 countries/jurisdictions, specifically examining four curriculum facets: learning goals, content of instruction, teaching methods, and examinations. This provides insight into how mathematics curriculum decisions are made across different systems. The percentages on the table represent the relative influence of a particular decision-making actor/level over a given aspect of the mathematics curriculum in a scale that goes from 0% (meaning the given actor has no formal decision-making role over that area) to 100% (meaning the actor in question has final authority or approval over all aspects of a particular curriculum area, e.g. goals for pupils).

On average across the 19 countries/jurisdictions that participated in the study, national authorities exert greatest influence on the definition of goals for pupils and on examinations; the responsibility for these aspects of the curriculum, however, seems to be widely shared with schools who also carry a strong say – albeit not in a leading role – on these matters. Decisions on the content of instruction in mathematics seem to also follow a model of shared responsibility where the national level exerts similar levels of influence as the school level, while recognising the ultimate decision-making influence of individual teachers. Finally, schools and especially individual teachers, have overwhelming control over the methods of instruction compared to other actors.

These findings are in line with global trends on decision making over various aspects of curriculum in other subject areas (OECD, 2023[18]; OECD, 2024[5]). Individual country variations are also noticeable. In Estonia, Korea and Portugal, schools have greater levels of autonomy over all aspects of the curriculum compared to the average results, and in contrast to Greece and Argentina, for example, where decision making for all aspects of curriculum is largely concentrated at the national level (Schmidt et al., 2022[7]).

The overwhelming convergence across educational systems on the prominent role of teachers as primary decision makers over teaching methods, including the choice of textbooks as well as teaching practices, leaves a lot of room for reflection when considering curriculum reform in mathematics. Given the widespread reliance on textbooks that are often out of date and misaligned with curriculum standards, this shows a gap where policymakers, curriculum designers and teachers can collaborate to better link the intentions of the curriculum to their teaching practices for greater system-level efficiencies (Schmidt et al., 2022[7]).

While increased autonomy can empower teachers and school leaders to adapt education to meet local needs, research suggests that autonomy needs to be accompanied by robust accountability systems. PISA 2022 data indicate that education systems with strong quality-assurance mechanisms – such as teacher mentoring, regular monitoring of classroom practices by inspectors, and systematic tracking of student achievement – experience higher average mathematics performance (Figure 3.7) (OECD, 2023[18]). These accountability measures help ensure that autonomy translates into effective, adaptive teaching practices where consistent quality is crucial.

Such of these measures, such as teacher mentoring, feedback, and school internal self-reflection echo other research suggesting that when strategies are well-designed and effectively implemented, teachers who feel supported and empowered with autonomy are better equipped to provide adaptive and appropriate education, particularly as classrooms become more diverse and students have increasingly individualised learning needs (Paradis, 2019[85]).

A flexible curriculum can empower schools and teachers to use their autonomy to modify, adjust and align content with both societal challenges and individual learning needs. Such flexibility allows for the integration of new content and priorities, ensuring the curriculum remains relevant while also forward-thinking, but teachers need to be well prepared and supported for using such flexibility, as they are critical actors in linking curriculum design to implementation (OECD, 2024[5]). Box 3.10 and Box 3.11 exemplify how curriculum flexibility, underpinned by professional development opportunities and adequate resources, can foster a system where mathematics education is responsive to local contexts and adaptable at the school level.

In countries where local flexibility and autonomy are prioritised, countries need to balance that autonomy with accountability measures and teacher professional development so they can use their freedom to enhance the links between curriculum design and implementation. If accountability mechanisms become overly stringent, however, they can impose excessive pressure on schools and teachers, stifling innovation and diminishing the flexibility that autonomy is intended to foster (Greany and Waterhouse, 2016[90]). This pendulum between autonomy and accountability highlights the need for a balanced approach that maintains educational rigour while allowing schools the freedom to innovate and respond dynamically to students’ needs (OECD, 2024[5]).

[50] Abrahamson, D. et al. (2018), “Enactivism and ethnomethodological conversation analysis as tools for expanding Universal Design for Learning: the case of visually impaired mathematics students”, ZDM, Vol. 51/2, pp. 291-303, https://doi.org/10.1007/s11858-018-0998-1.

[74] Alkhazaleh, Z. and A. Mahasneh (2016), “Fear of failure among a sample of Jordanian undergraduate students”, Psychology Research and Behavior Management, p. 53, https://doi.org/10.2147/prbm.s96384.

[64] Ashcraft, M. and E. Kirk (2001), “The relationships among working memory, math anxiety, and performance.”, Journal of Experimental Psychology: General, Vol. 130/2, pp. 224-237, https://doi.org/10.1037//0096-3445.130.2.224.

[67] Ashcraft, M. and A. Moore (2009), “Mathematics Anxiety and the Affective Drop in Performance”, Journal of Psychoeducational Assessment, Vol. 27/3, pp. 197-205, https://doi.org/10.1177/0734282908330580.

[69] Atkinson, J. (1957), “Motivational determinants of risk-taking behavior.”, Psychological Review, Vol. 64/6, Pt.1, pp. 359-372, https://doi.org/10.1037/h0043445.

[47] Beilock, S. (2008), “Math Performance in Stressful Situations”, Current Directions in Psychological Science, Vol. 17/5, pp. 339-343, https://doi.org/10.1111/j.1467-8721.2008.00602.x.

[48] Beilock, S., R. Rydell and A. McConnell (2007), “Stereotype threat and working memory: Mechanisms, alleviation, and spillover.”, Journal of Experimental Psychology: General, Vol. 136/2, pp. 256-276, https://doi.org/10.1037/0096-3445.136.2.256.

[62] Berger, N., E. Mackenzie and K. Holmes (2020), “Positive attitudes towards mathematics and science are mutually beneficial for student achievement: a latent profile analysis of TIMSS 2015”, The Australian Educational Researcher, Vol. 47/3, pp. 409-444, https://doi.org/10.1007/s13384-020-00379-8.

[77] Borgonovi, F. and S. Han (2020), “Gender disparities in fear of failure among 15‐year‐old students: The role of gender inequality, the organisation of schooling and economic conditions”, Journal of Adolescence, Vol. 86/1, pp. 28-39, https://doi.org/10.1016/j.adolescence.2020.11.009.

[66] Brown, M., P. Brown and T. Bibby (2008), ““I would rather die”: reasons given by 16-year-olds for not continuing their study of mathematics”, Research in Mathematics Education, Vol. 10/1, pp. 3-18, https://doi.org/10.1080/14794800801915814.

[31] Bruner, J. (1960), The Process of Education, Cambridge, MA: Harvard University Press.

[41] Bullen, J. et al. (2020), “A Developmental Study of Mathematics in Children with Autism Spectrum Disorder, Symptoms of Attention Deficit Hyperactivity Disorder, or Typical Development”, Journal of Autism and Developmental Disorders, Vol. 50/12, pp. 4463-4476, https://doi.org/10.1007/s10803-020-04500-9.

[39] Chen, Q. and J. Li (2014), “Association between individual differences in non-symbolic number acuity and math performance: A meta-analysis”, Acta Psychologica, Vol. 148, pp. 163-172, https://doi.org/10.1016/j.actpsy.2014.01.016.

[32] Clements, D. and J. Sarama (2020), Learning and Teaching Early Math, Routledge, Third edition. | New York, NY : Routledge, 2021. |, https://doi.org/10.4324/9781003083528.

[70] Conroy, D., J. Willow and J. Metzler (2002), “Multidimensional Fear of Failure Measurement: The Performance Failure Appraisal Inventory”, Journal of Applied Sport Psychology, Vol. 14/2, pp. 76-90, https://doi.org/10.1080/10413200252907752.

[68] Dowker, A., A. Sarkar and C. Looi (2016), “Mathematics Anxiety: What Have We Learned in 60 Years?”, Frontiers in Psychology, Vol. 7, https://doi.org/10.3389/fpsyg.2016.00508.

[26] Echazarra, A. et al. (2016), “How teachers teach and students learn: Successful strategies for school”, OECD Education Working Papers, No. 130, OECD Publishing, Paris, https://doi.org/10.1787/5jm29kpt0xxx-en.

[71] Elliot, A. and K. Sheldon (1997), “Avoidance achievement motivation: A personal goals analysis.”, Journal of Personality and Social Psychology, Vol. 73/1, pp. 171-185, https://doi.org/10.1037//0022-3514.73.1.171.

[15] Erstad, O. and J. Voogt (2018), “The Twenty-First Century Curriculum: Issues and Challenges”, in Springer International Handbooks of Education, Second Handbook of Information Technology in Primary and Secondary Education, Springer International Publishing, Cham, https://doi.org/10.1007/978-3-319-71054-9_1.

[89] Freudenthal, H. (1973), Mathematics as an Educational Task, Reidel.

[83] Geiger, V., I. Gal and M. Graven (2023), “The connections between citizenship education and mathematics education”, ZDM – Mathematics Education, Vol. 55/5, pp. 923-940, https://doi.org/10.1007/s11858-023-01521-3.

[45] Gervasoni, A. and L. Lindenskov (2010), “Students with ‘Special Rights’ for Mathematics Education”, in Mapping Equity and Quality in Mathematics Education, Springer Netherlands, Dordrecht, https://doi.org/10.1007/978-90-481-9803-0_22.

[36] Gottardis, L., T. Nunes and I. Lunt (2011), “A Synthesis of Research on Deaf and Hearing Children’s Mathematical Achievement”, Deafness & Education International, Vol. 13/3, pp. 131-150, https://doi.org/10.1179/1557069x11y.0000000006.

[90] Greany, T. and J. Waterhouse (2016), “Rebels against the system”, International Journal of Educational Management, Vol. 30/7, pp. 1188-1206, https://doi.org/10.1108/ijem-11-2015-0148.

[72] Gustafsson, H., S. Sagar and A. Stenling (2016), “Fear of failure, psychological stress, and burnout among adolescent athletes competing in high level sport”, Scandinavian Journal of Medicine & Science in Sports, Vol. 27/12, pp. 2091-2102, https://doi.org/10.1111/sms.12797.

[54] Hand, B. et al. (2018), “Improving critical thinking growth for disadvantaged groups within elementary school science: A randomized controlled trial using the Science Writing Heuristic approach”, Science Education, Vol. 102/4, pp. 693-710, https://doi.org/10.1002/sce.21341.

[14] Hao, J. et al. (2024), “Transforming Assessment: The Impacts and Implications of Large Language Models and Generative AI”, Educational Measurement: Issues and Practice, Vol. 43/2, pp. 16-29, https://doi.org/10.1111/emip.12602.

[59] Hayward, L. and E. Spencer (2010), “The complexities of change: formative assessment in Scotland”, The Curriculum Journal, Vol. 21/2, pp. 161-177, https://doi.org/10.1080/09585176.2010.480827.

[10] Honey, M., G. Pearson and H. Schweingruber (eds.) (2014), STEM Integration in K-12 Education, National Academies Press, Washington, D.C., https://doi.org/10.17226/18612.

[21] Hong, W. and P. Youngs (2019), “Why are teachers afraid of curricular autonomy? Contradictory effects of the new national curriculum in South Korea”, in Teachers’ Perceptions, Experience and Learning, Routledge, https://doi.org/10.4324/9781351173285-2.

[30] Jablonka, E. and C. Bergsten (2021), “Addressing the divide between procedural fluency and conceptual understanding in mathematics education research”, ZDM – Mathematics Education, Vol. 53/6, pp. 1173–1189.

[23] Jennings, J. and J. Bearak (2014), ““Teaching to the Test” in the NCLB Era”, Educational Researcher, Vol. 43/8, pp. 381-389, https://doi.org/10.3102/0013189x14554449.

[58] Jonker, H., V. März and J. Voogt (2020), “Curriculum flexibility in a blended curriculum”, Australasian Journal of Educational Technology, https://doi.org/10.14742/ajet.4926.

[79] Kiwanuka, H. et al. (2016), “How do student and classroom characteristics affect attitude toward mathematics? A multivariate multilevel analysis”, School Effectiveness and School Improvement, Vol. 28/1, pp. 1-21, https://doi.org/10.1080/09243453.2016.1201123.

[84] Ko, J. (2016), The development of school autonomy and accountability in Hong Kong: Multiple changes in governance, work, curriculum, and learning, International Journal of Educational Management, https://www.emerald.com/insight/content/doi/10.1108/IJEM-10-2015-0145/full/pdf?title=the-development-of-school-autonomy-and-accountability-in-hong-kong-multiple-changes-in-governance-work-curriculum-and-learning.

[22] Kuiper, W. and J. Berkvens (eds.) (2013), Curriculum regulation and freedom in the Netherlands - A puzzling paradox, CIDREE yearbook.

[19] Kuiper, W. and J. Berkvens (eds.) (2013), Moving up the line - Schools at the hub of policy development in Ireland, CIDREE Yearbook 2013. Enschede, the Netherlands: SLO.

[46] Lambert, R. (2021), “The Magic Is in the Margins: UDL Math”, Mathematics Teacher: Learning and Teaching PK-12, Vol. 114/9, pp. 660-669, https://doi.org/10.5951/mtlt.2020.0282.

[51] Lambert, R. et al. (2021), ““UDL is the What, Design Thinking is the How:” Designing for Differentiation in Mathematics”, Mathematics Teacher Education and Development, Vol. 23.3, pp. 54-77.

[86] Lesh, R. (ed.) (2013), The Dutch maths curriculum: 25 Years of modelling, Springer Science+Business Media Dordrecht, https://doi.org/10.1007/978-94-007-6271-8_53.

[81] Li, Q. et al. (2021), “Relations Between Students’ Mathematics Anxiety and Motivation to Learn Mathematics: a Meta-Analysis”, Educational Psychology Review, Vol. 33/3, pp. 1017-1049, https://doi.org/10.1007/s10648-020-09589-z.

[82] Maass, K. et al. (2019), “Promoting active citizenship in mathematics teaching”, ZDM, Vol. 51/6, pp. 991-1003, https://doi.org/10.1007/s11858-019-01048-6.

[80] Mata, M., V. Monteiro and F. Peixoto (2012), “Attitudes towards Mathematics: Effects of Individual, Motivational, and Social Support Factors”, Child Development Research, Vol. 2012, pp. 1-10, https://doi.org/10.1155/2012/876028.

[60] Mazana, M., C. Montero and R. Casmir (2018), “Investigating Students’ Attitude towards Learning Mathematics”, International Electronic Journal of Mathematics Education, Vol. 14/1, https://doi.org/10.29333/iejme/3997.

[37] Mazzocco, M. et al. (2013), “Limited knowledge of fraction representations differentiates middle school students with mathematics learning disability (dyscalculia) versus low mathematics achievement”, Journal of Experimental Child Psychology, Vol. 115/2, pp. 371-387, https://doi.org/10.1016/j.jecp.2013.01.005.

[75] McGregor, H. and A. Elliot (2005), “The Shame of Failure: Examining the Link Between Fear of Failure and Shame”, Personality and Social Psychology Bulletin, Vol. 31/2, pp. 218-231, https://doi.org/10.1177/0146167204271420.

[49] Meyer, A. and D. Rose (2000), “Universal design for individual differences”, Educational leadership, Vol. 58/3, pp. 39-43.

[11] Modeste, S. et al. (2023), “Coherence and Relevance Relating to Mathematics and Other Disciplines”, in New ICMI Study Series, Mathematics Curriculum Reforms Around the World, Springer International Publishing, Cham, https://doi.org/10.1007/978-3-031-13548-4_11.

[34] Mullis, I. et al. (2020), TIMSS 2019 International results in mathematics and science.

[5] OECD (2024), Curriculum Flexibility and Autonomy: Promoting a Thriving Learning Environment, OECD Publishing, Paris, https://doi.org/10.1787/eccbbac2-en.

[28] OECD (2024), Mathematics for Life and Work: A Comparative Perspective on Mathematics to Inform Upper Secondary Reform in England, OECD Publishing, Paris, https://doi.org/10.1787/26f18d39-en.

[16] OECD (2023), Education at a Glance 2023: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/e13bef63-en.

[38] OECD (2023), Equity and Inclusion in Education: Finding Strength through Diversity, OECD Publishing, Paris, https://doi.org/10.1787/e9072e21-en.

[8] OECD (2023), OECD Learning Compass 2030: The Future We Want for Mathematics, OECD Publishing.

[6] OECD (2023), PISA 2022 Assessment and Analytical Framework, OECD Publishing, Paris, https://doi.org/10.1787/7ea9ee19-en.

[33] OECD (2023), PISA 2022 Results (Volume I): The State of Learning and Equity in Education, PISA, OECD Publishing, Paris, https://doi.org/10.1787/53f23881-en.

[18] OECD (2023), PISA 2022 Results (Volume II): Learning During – and From – Disruption, PISA, OECD Publishing, Paris, https://doi.org/10.1787/a97db61c-en.

[13] OECD (2023), “Putting AI to the test: How does the performance of GPT and 15-year-old students in PISA compare?”, OECD Education Spotlights, No. 6, OECD Publishing, Paris, https://doi.org/10.1787/2c297e0b-en.

[3] OECD (2021), Adapting Curriculum to Bridge Equity Gaps: Towards an Inclusive Curriculum, OECD Publishing, Paris, https://doi.org/10.1787/6b49e118-en.

[4] OECD (2021), Embedding Values and Attitudes in Curriculum: Shaping a Better Future, OECD Publishing, Paris, https://doi.org/10.1787/aee2adcd-en.

[2] OECD (2020), Curriculum Overload: A Way Forward, OECD Publishing, Paris, https://doi.org/10.1787/3081ceca-en.

[78] OECD (2020), “Students’ self-efficacy and fear of failure”, in PISA 2018 Results (Volume III): What School Life Means for Students’ Lives, OECD Publishing, Paris, https://doi.org/10.1787/2f9d3124-en.

[1] OECD (2020), What Students Learn Matters: Towards a 21st Century Curriculum, OECD Publishing, Paris, https://doi.org/10.1787/d86d4d9a-en.

[9] OECD (2019), “OECD future of education and skills 2030 concept note: OECD learning compass 2030”, https://www.oecd.org/en/data/tools/oecd-learning-compass-2030.html.

[17] OECD (2013), Education at a Glance 2013: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/eag-2013-en.

[25] OECD (2013), PISA 2012 Results: Ready to Learn (Volume III): Students’ Engagement, Drive and Self-Beliefs, PISA, OECD Publishing, Paris, https://doi.org/10.1787/9789264201170-en.

[42] Oswald, T. et al. (2015), “Clinical and Cognitive Characteristics Associated with Mathematics Problem Solving in Adolescents with Autism Spectrum Disorder”, Autism Research, Vol. 9/4, pp. 480-490, https://doi.org/10.1002/aur.1524.

[85] Paradis, A. (2019), Towards a relational understanding of teacher autonomy: The role of trust for Canadian and Finnish teachers., SAGE Publications, https://journals.sagepub.com/doi/epub/10.1177/1745499919864252.

[12] Perignat, E. and J. Katz-Buonincontro (2019), “STEAM in practice and research: An integrative literature review”, Thinking Skills and Creativity, Vol. 31, pp. 31-43, https://doi.org/10.1016/j.tsc.2018.10.002.

[29] Peters, A. (2024), “Using the TIMSS curriculum model to develop a framework for coherence and its role in developing mathematical connections”, Research in Mathematics Education, Vol. 26/2, pp. 300-324, https://doi.org/10.1080/14794802.2024.2371026.

[53] Prain, V. et al. (2013), “Personalised learning: lessons to be learnt”, British Educational Research Journal, Vol. 39/4, pp. 654-676, https://doi.org/10.1080/01411926.2012.669747.

[52] Rand (ed.) (2017), Informing Progress: Insights on Personalized Learning Implementation and Effects, https://www.rand.org/pubs/research_reports/RR2042.html.

[65] Richardson, F. and R. Suinn (1972), “The Mathematics Anxiety Rating Scale: Psychometric data.”, Journal of Counseling Psychology, Vol. 19/6, pp. 551-554, https://doi.org/10.1037/h0033456.

[73] Sagar, S., D. Lavallee and C. Spray (2007), “Why young elite athletes fear failure: Consequences of failure”, Journal of Sports Sciences, Vol. 25/11, pp. 1171-1184, https://doi.org/10.1080/02640410601040093.

[43] Samara, M. et al. (2021), “How Can Bullying Victimisation Lead to Lower Academic Achievement? A Systematic Review and Meta-Analysis of the Mediating Role of Cognitive-Motivational Factors”, International Journal of Environmental Research and Public Health, Vol. 18/5, p. 2209, https://doi.org/10.3390/ijerph18052209.

[7] Schmidt, W. et al. (2022), “When practice meets policy in mathematics education: A 19 country/jurisdiction case study”, OECD Education Working Papers, No. 268, OECD Publishing, Paris, https://doi.org/10.1787/07d0eb7d-en.

[24] Schmidt, W. et al. (2001), Why Schools Matter: A Cross-National Comparison of Curriculum and Learning. The Jossey-Bass Education Series., Jossey-Bass, 989 Market Street, San Francisco, CA 94103-1741.

[27] Schmidt, W., H. Wang and C. McKnight (2005), “Curriculum coherence: an examination of US mathematics and science content standards from an international perspective”, Journal of Curriculum Studies, Vol. 37/5, pp. 525-559, https://doi.org/10.1080/0022027042000294682.

[40] Spinczyk, D. et al. (2019), “Factors influencing the process of learning mathematics among visually impaired and blind people”, Computers in Biology and Medicine, Vol. 104, pp. 1-9, https://doi.org/10.1016/j.compbiomed.2018.10.025.

[56] Thadani, V. et al. (2010), “The Possibilities and Limitations of Curriculum-Based Science Inquiry Interventions for Challenging the “Pedagogy of Poverty””, Equity & Excellence in Education, Vol. 43/1, pp. 21-37, https://doi.org/10.1080/10665680903408908.

[57] Therrien, W. et al. (2017), “Explicit Instruction and Next Generation Science Standards Aligned Classrooms: A Fit or a Split?”, Learning Disabilities Research & Practice, Vol. 32/3, pp. 149-154, https://doi.org/10.1111/ldrp.12137.

[87] Thompson, D., M. Huntley and C. Suurtamm (eds.) (2018), Primary school mathematics in the Netherlands. The perspective of the curriculum documents, Information Age Publishing.

[55] Tong, F. et al. (2014), “A Randomized Study of a Literacy-Integrated Science Intervention for Low-Socio-economic Status Middle School Students: Findings from first-year implementation”, International Journal of Science Education, Vol. 36/12, pp. 2083-2109, https://doi.org/10.1080/09500693.2014.883107.

[63] UNESCO (2022), Mathematics for action: supporting science-based decision-making.

[88] Van den Heuvel-Panhauizen, M. and M. Wijers (2005), “Mathematics standards and curricula in the Netherlands”, ZDM – The International Journal on Mathematics Education, Vol. 37/4, pp. 87-307.

[20] Voogt, J., N. Nieveen and S. Klopping (2017), Curriculum Overload: A Literature Study, Unpublished OECD Reference Document.

[76] Wach, F. et al. (2015), “Sex differences in secondary school achievement – The contribution of self-perceived abilities and fear of failure”, Learning and Instruction, Vol. 36, pp. 104-112, https://doi.org/10.1016/j.learninstruc.2015.01.005.

[61] Wen, R. and A. Dubé (2022), “A systematic review of secondary students’ attitudes towards mathematics and its relations with mathematics achievement”, Journal of Numerical Cognition, Vol. 8/2, pp. 295-325, https://doi.org/10.5964/jnc.7937.

[35] Willms, J. (2006), Learning Divides: Ten Policy Questions About the Performance and Equity Of Schools, UNESCO, Institute for Statistics, https://unesdoc.unesco.org/ark:/48223/pf0000147066.

[44] Yu, S. and X. Zhao (2021), “The negative impact of bullying victimization on academic literacy and social integration: Evidence from 51 countries in PISA”, Social Sciences & Humanities Open, Vol. 4/1, p. 100151, https://doi.org/10.1016/j.ssaho.2021.100151.