6. Are children learning and achieving in education?

The purpose of this chapter is to review the available evidence on children’s cognitive development and educational outcomes, and to highlight the types of data that are required to inform the development of sound policies in education and early childhood education and care. The chapter analyses the available cross-national information children’s learning and educational achievements and the key resources and factors influencing child cognitive outcomes. In a few cases, national evidence is used to inform on how future data collection could be improved.

From the very first moments of life, children observe and interact with the world around them. These early impressions drive rapid cognitive development during the early years, a period during which the brain is particularly malleable and most open to learning from experiences. Children learn how to communicate, form their first ideas about words and numbers, and progressively acquire literacy and numeracy skills. The early formation of cognitive skills has long-lasting implications for children’s educational trajectories and achievements as early mastery of skills makes later learning easier, more efficient and more likely to continue (Heckman, 2006[1]).

Gaps and inequalities in cognitive abilities often widen rather than narrow once children start school. It is important that schools offer a stimulating learning environment and sufficient resources to help every student – not just the most able – achieve their highest potential and ensure that no child is left behind (OECD, 2020[2]). Peer relations at school have an impact on individual educational achievement and aspirations (Wentzel, 2017[3]; Wang et al., 2014[4]). Educational achievements and the skills children develop throughout childhood affect later labour market outcomes, health and also their subjective well-being and social inclusion (OECD, 2020[5]). As such, it is particularly important to measure children’s learning and skill development from an early age, as well as their views regarding school work and the learning environment.

To guide the development of better policies, policy-makers need high-quality data on a wide range of areas relating to children’s learning, skill development and satisfaction with the learning environment. This includes measures of core foundational competences like literacy and numeracy, transversal skills such as self-regulated learning, problem solving, and critical and creative thinking, and children’s subjective experience at school or in childcare, among other things. It is also important to collect information on the resources that can promote children's learning and cognitive development at home and in their community and neighbourhood. Data should allow for the identification of delays and inequalities in learning development, and highlight where and when children are at risk of disengaging from education.

The key messages to be taken from this chapter are as follows:

  • In contrast to many other areas of child well-being, there is a relatively broad range of cross-national data available on children’s cognitive development and educational achievement. This is especially the case with respect to the traditional core areas of reading, mathematics and science, which, for children in middle-childhood and adolescence, are covered comprehensively through the major international student assessments programmes (i.e. PISA, PIRLS and TIMMS).

  • Children’s learning and cognitive development in areas outside reading, mathematics and science are less well covered by cross-national data. There is less data, for instance, on children’s transversal cognitive skills (e.g. problem solving, creative thinking, critical thinking), on their self-regulated learning and “learning to learn” skills (e.g. motivation, planning, self-monitoring, self-reflection), and on their digital skills (e.g. data and digital literacy). There is increasing recognition that these kinds of competences are or will be crucial for children growing up in today’s world.

  • There is also relatively little cross-national data on cognitive development in early childhood. Strengthening efforts to collect data on early learning at national and international levels is key, given the large body of evidence underlining that early childhood lays the foundations for cognitive development and educational achievement for the rest of childhood and adult life. The OECD’s International Early Learning and Child Well-being Study (IELS) is a positive development in this regard. The study collects information on 5-year-old’s early learning and well-being. In its first round, it covered three OECD countries: England (United Kingdom), Estonia and the United States.

  • The home learning environment provides key resources for fostering children’s early cognitive development, above and beyond the inherited genetic endowments (Manu, Barros and Victora, 2019[6]; Fernald, Marchman and Weisleder, 2013[7]; Romeo et al., 2018[8]). However, the family is also where inequalities in learning and cognitive development start to develop from infancy on. It is therefore important to have data on parenting practices regarding children's care and education at different stages of childhood, as well as on how parenting practices vary with families’ socio-economic status.

  • Children’s participation in high quality Early childhood Education and Care (ECEC) services before entering school can help build cognitive skills and school-readiness, especially among children from lower socio-economic backgrounds (van Huizen and Plantenga, 2018[9]). In this perspective, it is important to further pursue national and cross-national efforts to measure inequalities in participation, the barriers that may explain these inequalities (lack of availability, affordability, lack of information on the supply, lack of awareness about of the benefits for children, cultural barriers, etc.), and to monitor the quality development of ECEC services.

  • Over the years, a growing body of information has been developed to assess school performance at different ages. Furthermore, there has been a lot of information collected on children's perceptions of their school environment, attitudes towards school work, relationships with teachers and peers, and perceived support from parents. This improvement in data has required changes in survey questionnaires, sometimes generating inconsistencies between information collected across years. To allow robust monitoring of child learning outcomes, it would be desirable to consolidate the core data set to be repeated across survey waves.

The chapter also points to data gaps that could be filled to improve the understanding of where to prioritise actions, including:

  • Data gaps on skill acquisition and learning achievements of highly vulnerable groups of children not covered in general children’s surveys, such as victims of maltreatment, children with disabilities, children in alternative care, or homeless children. Data on learning achievements and needs of these groups of children are crucial to greatly improve their educational outcomes.

  • Data on children’s motivations for learning, educational aspirations and knowledge of education systems and educational tracks, which are key elements in the formation of inequalities in school tracks and career choice. Measuring these aspects from middle childhood onwards is key to prevent school drop outs, provide better guidance regarding school choices, and improve well-being at school.

The chapter begins with an overview over of the central aspects of children's cognitive development and well-being structured around the three stages of childhood: early childhood, middle childhood and adolescence. The subsequent sections review the availability of data and indicators on children’s skill development and learning achievement, as well as the resources provided in the home and school environments. Data gaps are discussed in the last section. As the focus of this chapter is set on cognitive well-being, it does not review the literature and data available on socio-emotional well-being, which is instead discussed in Chapter 5.

Cognitive development is the process by which human beings acquire, organise, and learn to use knowledge (Gauvain and Richert, 2016[10]). One aspect is about “what develops”, or the content of knowledge, and focuses on concepts, the mental groupings of similar objects and other entities that play a fundamental role in organizing knowledge of experience. The other aspect of cognitive development refers to “how knowledge develops”, and involves the processes of memory, problem solving, reasoning, and executive function. The learning process also involves other capacities, such as curiosity and interest and pleasure in learning (Becchetti-Bizot, Houzel and Taddei, 2017[11]; Vincent-Lancrin et al., 2019[12]).

All cognitive skills have their roots in the early years of life. Much evidence shows that gaps in cognitive progresses emerge in early childhood and are remarkably persistent and difficult to close. These early gaps are in fact one of the most important vehicles for socio-economic inequality and low social mobility, often explaining differences in children’s educational trajectories. Children’s capacity to learn from experiences is strong during the early years, due to the high plasticity of the brain, which decreases with age (Knudsen, 2004[13]; NSCDC, 2016[14]). Early childhood provides a powerful window of opportunity to correct early skill inequality, and for this very reason early childhood interventions often offer high rates of return (Heckman, 2006[1]; Hendren and Sprung-Keyser, 2020[15]; Rosholm et al., 2021[16]). However, intervening at other points in childhood also holds promise. Adolescence is described as a second window of opportunity as puberty initiates intense learning and brain development, which lead to numerous structural and functional changes to the brain. Adolescence is also “sensitive period” for a brain development and reorganisation occurs, which also can be strongly influenced by experiences and environmental factors that can impact future functioning (Ismail, Fatemi and Johnston, 2017[17]; Choudhury and Slaby, 2012[18]; UNICEF, 2017[19]).

Table 6.1 provides an overview of key aspects of children’s cognitive development and educational well-being. It is divided into four panels, structured in line with the child well-being measurement framework outlined in Chapter 2:

  • Panel A highlights key child cognitive development and educational outcomes. This includes the age- (or stage-) appropriate development of cognitive skills and abilities and other skills central to learning, such as early language development and emergent literacy and numeracy for young children and a range of domain-specific and transversal cognitive skills for children in middle childhood and adolescence, as well as non-cognitive skills and competences such as self-regulation/self-regulated learning, confidence in learning and children’s satisfaction with what they learn. Children’s educational progression and attainment is also covered here.

  • Panel B focus on child-level drivers and influences of cognitive development and education outcomes. This includes learning activities with parents or caregivers (e.g. shared reading, play) for young children, and parental assistance with and support for learning for older children. It also includes children’s attitudes and behaviours at school (including engagement, motivation, and mind-set), their relationships at school (e.g. with teachers and classmates), and their learning behaviours at home (e.g. homework, reading for leisure). Also relevant here are children’s over-arching educational and career aspirations, especially for older children.

  • Panel C highlights important environment-level drivers of children’s cognitive development and education outcomes. This includes aspects of the home environment, such as access to educational books and toys and, for older children, study supports, as well as several aspects of the environment that children face in ECEC and at school (e.g. disciplinary climate, class size, classroom cooperation and competition). Parent-teacher relationships and communication between parents and children’s schools and ECEC services (e.g. to discuss child progress) is also covered here.

  • Lastly, Panel D covers public policies that can have important effects on children’s cognitive development and education. This includes chiefly policies and regulations relating to ECEC services and compulsory education. But also relevant here are various types of family policy (e.g. family financial supports, family employment-related supports), which can impact children’s learning through the effects on the family and home environment and the time parents are able to spend with children.

The following sections provide more detail on the different aspects of children’s cognitive development and educational progression and attainment.

Cognitive development refers to the ways in which children learn to think, reason, and use language. It covers the development of knowledge, skills and abilities in a range of areas, including literacy, speech, and numeracy. Early cognitive development has an immediate impact on children’s well-being through their ability to communicate and learn. It also has a long-term impact as it is a very strong predictor of later educational achievement (see e.g. Duncan et al. (2007[20])), as well as outcomes in other well-being dimensions (see e.g. Ritchie and Bates (2013[21])). Moreover, different cognitive skill domains are mutually reinforcing, for example, children with strong language development typically develop stronger literacy, and vice versa (OECD, 2020[5]).

Differences in children’s home circumstances are one of the factors behind gaps in cognitive skills that emerge early in life. The high malleability of the brain makes young children very sensitive to external stimuli (Stiles and Jernigan, 2010[22]). These gaps are remarkably persistent over the course of childhood. For instance, there is evidence in the United Kingdom that literacy skill inequalities develop strongly in early childhood up to the end of the first year of compulsory schooling and then remain relatively stable for most children throughout their school career (Taggart et al., 2015[23]). The gaps with the lowest performing group of children at age three also tend to widen with age. It is therefore of high importance to monitor children’s cognitive development early on, preferably even before they start school when emergent cognitive skills are developing in the absence of formal instruction, in order to prevent mid- and long-term widening of skill formation inequalities.

Language development is one of the most fundamental parts of children’s early cognitive development. Language is important in enabling both cognitive and social development. Developing language concepts helps build infants’ and toddlers’ brains and gives them the means to think and develop ideas and express themselves. Babies' and young children's language development is strongly influenced by the language they hear spoken around them and to them. The more babies and young children are exposed to language, through caregivers talking to and responding to non-verbal and verbal cues, the more opportunities they'll have to learn and practice. Not only does it enrich children’s possibilities of communication, self-expression and learning, it also lays foundations for forming connections with peers and caregivers and thus highly important for children’s socio-emotional development and well-being (OECD, 2020[5]).

The development of communication and language in the early months of life follows specific milestones, which can help parents and paediatricians to detect language and speech delays. Typically, children first develop receptive abilities that allow them to understand easy gestures, mimic and language while processing the information expressed in a basic way. Over the first year of life, this develops to the ability of understanding gesture-supported commands. Initial expressive abilities are limited to crying, but eventually develop to cooing, pointing and babbling before children develop the ability to speak first words around 10 to 15 months of age. After initial slow lexical growth - about 1-2 words per week - children rapidly increase their vocabulary and expressive ability by 1-2 words per day in the “vocabulary spurt” between months 18 and 24. Over the following years children further expand their vocabulary and refine their grammatical skills, which typically culminates in a full acquisition and mastery of all language fundamentals by the time they start school (Feldman, 2019[24]; McLaughlin, 2011[25]).

Children that fall behind in meeting these key milestones are at risk of adverse cognitive development and compromised academic achievement. For example, children suffering from language impairment in kindergarten are at highly elevated risk of reading disabilities and overall worse reading outcomes in second and fourth grade of elementary school (Catts et al., 2002[32]). For those primary school age children who suffer from lasting speech and language problems , many also have from poor writing skills, in particular punctuation and spelling (Bishop and Clarkson, 2003[33]). In addition to academic scores, early language problems can negatively affect psycho-social outcomes (Snowling et al., 2006[34]) and increase the risk for mental health disorders (Law et al., 2009[35]).

Healthy speech and language development depends on a variety of genetic and environmental factors. For example, genetic variations influence the number of words children can speak at 15 to 18 months of age (St Pourcain et al., 2014[36]). On the other hand, household socio-economic status also play a critical role (Letourneau et al., 2011[37]). Resulting from compounding evidence, a lively debate on children language gap has evolved in academia and the public alike, also focussing on the level and quality of parent-child interactions in the household (Box 6.2). Children typically develop emergent literacy through interactions with parents and caregivers (Whitehurst and Lonigan, 1998[38]). Rather than a concept of reading readiness, emergent literacy comprises a continuum of skills, among others, awareness of print and phonology as well as knowledge of syntax and verbal processing, all of which may develop well before receiving formal instruction (Byrnes and Wasik, 2019[39]). While acquiring first concepts of verbal language, children also develop ideas about counting, numbers and their relations to each other (Nelissen, 2018[40]).

Delays in emerging literacy can often persist and impair literacy development throughout school and later life. For example, emerging literacy concepts measured at school-entry, kindergarten or earlier are linked to later literacy outcomes in elementary school (Duncan et al., 2007[20]; Claessens, 2009[46]; Shanahan and Lonigan, 2010[47]). In addition, cognitive ability in reading and math measured in kindergarten is also highly correlated with later adulthood earnings, college attendance, home ownership, and retirement savings (Chetty et al., 2011[48]). As such, alphabet knowledge, phonological awareness, rapid naming tasks (e.g. digits or colours), phonological memory and the ability to write the own name are positively influencing conventional literacy outcomes, such as decoding, reading comprehension and spelling later on (Shanahan and Lonigan, 2010[47]). Similarly, early mathematical knowledge in kindergarten, in particular number competencies and developmental number sense, is strongly related to mathematical abilities over primary school (Jordan et al., 2009[49]; 2007[50]). In summary, gaps in emergent literacy and numeracy may translate into significant gaps in conventional reading and math literacy outcomes throughout school and later life, but they also impact each other and the outcomes on a wider range of skill domains, including working memory (OECD, 2020[5]).

The abilities of abstract thinking and reasoning, which are the precursors of scientific literacy, is often already developing in children even before entering school (Becchetti-Bizot, Houzel and Taddei, 2017[11]). An introduction to scientific reasoning in the pre-school years provides important opportunities for further evidence-based learning and discovery of their surrounding natural world (Gropen et al., 2017[51]). However, science teaching is rare in the early years and in the primary school classroom, in part because many teachers think children are not developmentally ready to formally learn about scientific approaches and concepts (Whittaker et al., 2020[52]).

Care and educational facilities can have a substantial impact on children’s cognitive development, academic achievement and other long-term outcomes. When children enter school, differences in cognitive abilities, such as those often seen along socio-economic lines, can persist. On top of that, differences in reading literacy at school entry, in particular in vocabulary and phonological awareness, can even widen the gap in literacy outcomes. This not only impacts academic performance, but can also affect immediate well-being and mental health outcomes (Clark and Teravainen-Goff, 2018[53]). At the same time, children with impaired literacy development also engage less in activities that may enhance reading literacy further, such as reading (see e.g. Horowitz‐Kraus and Hutton (2018[54])). These patterns last throughout childhood and affect adolescents similarly. However, even though adolescents and older children spend increasing amounts of time in non-traditional reading practices, such as texting, it is only traditional reading (e.g. books) that is associated with better literacy (Zebroff and Kaufman, 2017[55]).

Throughout middle childhood and adolescence, gaps between children with better reading literacy (i.e. those that are more likely to read in their free time) and those with delayed literacy often widen. This feedback loop puts children from disadvantaged backgrounds at risk of being left behind even more in the long-run, than what socio-economic factors explain at school entry (Buckingham, Wheldall and Beaman-Wheldall, 2013[56]; Sullivan and Brown, 2015[57]). However, good school environments, in particular those with sufficient resources for remedial help, can help children with language problems in early childhood to become competent readers at the end of primary school (Parsons et al., 2011[58]). Nevertheless, even though school environments and formal learning offer the possibilities for children to catch up on deficiencies in numeracy, the achievement gap between those that start school with well-developed emergent numeracy and those without remains persistent (Sylva et al., 2008[59]).

Impairments and gaps in literacy development have been linked to a wide range of outcomes later in life, including labour market outcomes, mental health outcomes, and life satisfaction (Law et al., 2009[35]; Crawford and Cribb, 2015[60]; Flèche, Lekfuangfu and Clark, 2017[61]). As such, progress in reading literacy during the early school years may have meaningful effects on later socio-economic status, and may be a crucial factor limiting social mobility (Ritchie and Bates, 2013[21]). Numeracy skills in the early school years also have strong links to later socio-economic status (Ritchie and Bates, 2013[21]). Schoon et al. (Schoon et al., 2015[62]), reviewing a number of longitudinal studies, find considerable evidence linking both verbal and numerical skills in the early school years to later outcomes in several areas – including educational attainment, adult socio-economic status, income, and health behaviours – even after other important factors are controlled for.

During the course of their schooling, some children are subject to grade repetition when they do not meet the criteria to progress on to the next school grade. This practice is not found in all OECD countries, with evidence from PISA 2009 suggesting that it is only applied in half. Whether this practice is to children’s benefit or not is subject to some academic debate (Ikeda and García, 2014[63]). Findings on the effect of grade repetition on academic performance and social and emotional development are inconsistent. The practice may help improve children’s academic performance through the repetition of concepts that they have not yet mastered and through minimising the risk of them falling even further behind had they progressed onwards. The positive effect on school grades may only be short term, however. There is also no conclusive evidence on whether grade retention leads to higher graduation rates (Mahjoub, 2017[64]; Schwerdt, West and Winters, 2017[65]). In particular, some studies highlight negative consequences for later academic achievement, concentrated in primary school grade retention (Ikeda and García, 2014[63]; Diris, 2017[66]). Academic motivation may suffer as students who already have low learning motivation and confidence may feel discouraged (Kretschmann et al., 2019[67]). Additionally, children’s well-being typically suffers substantially (Rathmann, Loter and Vockert, 2020[68]).

In terms of educational attainment, it is also important to measure graduation and dropout rates of compulsory schooling. On average, early school leavers fare substantially worse than those who finished secondary school, in areas of earnings, health status, and life satisfaction (Messacar and Oreopoulos, 2013[69]). The transition from compulsory education involves adolescents making the choice of either continuing with upper secondary education or engaging in the labour market. In the OECD, high shares of young people aged between 15 and 24 years of age are not in employment, education nor training (NEET), during the time in life that is critically important for establishing future educational and labour market careers; not being in education or employment during adolescence is associated with lower educational aspirations and career prospects and adverse mental outcomes in later life (Gutiérrez-García et al., 2017[70]).

Finally, how educational attainment is perceived can take very different forms, depending on group socio-economic status and the country. For some groups, proceeding to do a Bachelor’s degree at third level will be perceived as an achievement, while for others the goal posts are set higher and only graduating with honours, or being able to access a selective track, will be regarded as good enough. For example, in France, the “massification” of education, illustrated by the fact that 80 percent of a generation born each year now has a baccalaureate, has led to greater competition for the top grades. However, not all families have the information and networks to access prestigious programmes and the best job opportunities (Dubet and Duru-Bellat, 2019[71]). While the proportion of young people reaching a tertiary degree has increased, grade inequalities within each level of education remain high. The information system on educational attainment does not fully reflect the inequalities that exist across the various education streams and training programs (Dubet and Duru-Bellat, 2019[71]).

Children’s educational motivations and aspirations play an important role in academic outcomes and achievement. Children’s motivation and achievement values are linked to academic performance (Meyer et al., 2009[72]; Özen, 2017[73]), and students with low or no motivation are found to perform significantly worse than highly motived peers (Walkey et al., 2013[74]). In addition, students who believe that ability is not fixed and is something that can be improved (i.e. students with a growth mind-set) have higher academic achievement across all socioeconomic strata (Claro, Paunesku and Dweck, 2016[75]).

Several variables of children’s and adolescent’s environment have a particular influence on educational aspirations. In particular, children of lower socio-economic status have greater aspirations regarding academic achievement when they have a better home learning environment, parents who are more involved in their school life and hold higher expectations regarding academic achievement. Higher levels of peer support also exert a positive influence (Berzin, 2010[76]).

Children’s educational aspirations are susceptible to diminishing with age as children’s understanding grows about what is possible and the constraints imposed by previous choices and achievements (Gutman and Akerman, 2008[77]). Kao and Tienda (1998[78]) note that younger children are more idealistic in the aspirations that they hold before becoming more aware as they get older about the barriers against them in the educational and academic system. This is particular evident among children from lower socio-economic backgrounds who are more likely to face multiple barriers to their educational success. As a general rule, the educational aspirations of younger low-income children are higher than those of older low-income children (Berzin, 2010[76]). Gender stereotypes also influence the formation of educational and career aspirations. At the same time, parents adjust their expectations to what they think are their children’s abilities and the performance required by the educational system. As a result, parental expectations often decline as children age, especially among lower-income households, and this is informed by changes in perceptions over household financial constraints, children’s abilities and their more or less informed knowledge of educational opportunities (Gutman and Akerman, 2008[77]). Such a big shift in aspiration among children and parents highlights that measuring the development of educational aspirations between middle-childhood and adolescence could help better understand student and parental perceptions of barriers and opportunities in the educational system.

In addition to children’s educational aspirations, their attitudes toward education and their behaviour in the classroom also matter for academic achievement. A particularly salient sign of student motivation can be truancy. Regular truancy has a wide range of negative consequences, especially on academic performance and achievement (Aucejo and Romano, 2016[79]; Smerillo et al., 2018[80]). In addition, chronic absences are more concentrated among already socioeconomically disadvantaged children (Gershenson, Jacknowitz and Brannegan, 2017[81]). While school absence in itself has implications for academic achievement and school outcomes, the reasons for these, including authorised and unauthorised absences, matter as well (Hancock, Gottfried and Zubrick, 2018[82]). The effects of absenteeism are also not only concentrated among chronically absent students themselves, but these behaviours affect the classroom climate through disruptions and peer resentment as well (Wilson et al., 2008[83]).

Aside from learning in the classroom, children also engage in educational activities outside of school, in particular homework activities. In general, the time spent on homework is related to better academic performance (OECD, 2014[84]; Scheerens, 2014[85]). While this may be related to student engagement, homework helps to consolidate concepts that were previously introduced and learned in school (Guthrie and Klauda, 2014[86]). Homework in primary school might not necessarily be related to academic achievement, which potentially indicates that format and design of assigned tasks are not optimal for young students (Jerrim, Lopez‐Agudo and Marcenaro‐Gutierrez, 2020[87]).

School engagement, expressed though children’s psychological and behavioural engagement, is also linked to academic achievement and other life outcomes. Students with lower engagement are more likely to have lower school grades and to drop out of school early, but are also more prone to delinquency, depression and substance misuse over time (Dotterer and Lowe, 2010[88]; Li and Lerner, 2011[89]; Wang and Fredricks, 2014[90]). Two of the main contributing factors to low psychological and behavioural engagement with school are low levels of teacher- and parent support. This highlights how environmental factors are a critical force for shaping children’s and adolescent’s school engagement (Fall and Roberts, 2012[91]).

A large body of literature highlights the association between household income and socio-economic status and children’s family on academic achievement (see e.g. Blanden and Gregg (2008[92]) or Akee et al. (2010[93])) and overall cognitive development (see e.g. Letourneau et al. (2011[37]) or Macours, Schady and Vakis (2012[94])). At as young as 18 months old, children in families of low socio-economic status show significant differences in vocabulary and language processing efficiency to children from families of high socio-economic status (Fernald, Marchman and Weisleder, 2013[7]). In terms of early literacy development, many of these gaps arising along socio-economic lines may be explained by more stimulating home literacy environments, increased investment in educational resources and a higher parental involvement in literacy-enhancing activities for families of higher socioeconomic status (Kluczniok et al., 2013[95]; Neumann, 2016[96]; Kaushal, Magnuson and Waldfogel, 2011[97]).

The impact of growing up in lower socio-economic -status households on language seems to be particularly strong for boys (Barbu et al., 2015[98]; Zambrana, Ystrom and Pons, 2012[99]). The reasons behind these gender differences are not fully explained. Parents’ talkativeness may matter, as parents have been found to speak more to girls than to boys during infancy and toddlerhood (Leaper, Anderson and Sanders, 1998[100]; Leaper, 2002[101]). More broadly, it is argued that parents from lower SES backgrounds are both more concerned about their children conforming to societal expectations and are more directive and less conversational than parents from higher SES backgrounds, with both potentially impact on gender language socialisation (Bornstein, 2015[102]). Differences in the socio-emotional responsiveness of boys and girls – the latter showing earlier and higher social responsiveness – are also seen as a potential source of sex differences in language development (Blakemore, Berenbaum and Liben, 2013[103]). Biological factors may also play a role. High levels of testosterone, for instance, appears to be a risk factor in delaying boys’ language acquisition (Whitehouse et al., 2012[104]), with effects mediated by children’s socio-emotional engagement that also depends on children’s environment (Farrant et al., 2012[105]). Overall, complex interactions between biological, environmental and behavioural factors seem to drive gender differences in language development in the early years.

The home literacy and numeracy environment (HLE & HNE) are particularly important factors enhancing children’s literacy and numeracy development early in life (Melhuish et al., 2008[106]). As such, educational resources as well as the perceived support from caregivers are critical. For example, the HLE in terms of access to age-appropriate educational resources, such as books and toys, can stimulate early literacy development, in particular those that are subject to weak early language achievements (Law et al., 2018[107]; Manu, Barros and Victora, 2019[6]). In addition, a more stimulating HNE is linked to stronger emergent numeracy as well as longer-term development of mathematical abilities (Niklas and Schneider, 2014[108]).

Related to the home environment, the time and activities that children share with parents can be strongly influence development. One example is language development and the important role of language rich parent-child interactions and shared reading (i.e. parents reading to children) (see Box 6.2). Children show higher levels of emergent literacy scores when parents frequently read to them as well when the households has more children’s books (OECD, 2020[5]). Field experiments suggest that that there are significant inequalities in information and awareness of the beneficial effects of shared reading between families. Programmes that help fill this information gap and guide families towards good practices can have beneficial effects on language development in early childhood, especially for children from disadvantaged families (Barone and Chambouleyron, 2019[109]; Barone, Fougère and Pin, 2020[110]). The underlying process of this can potentially be attributed to higher levels of brain white matter integrity for children in households with good literacy and learning environment which, in turn, is associated with better language development and emergent literacy as well as other cognitive outcomes before school (Hutton et al., 2020[111]).

Much evidence links parental involvement and support, in terms of parents’ participation in children’s learning processes and experiences of higher academic achievement. Parental involvement is important for at all stages of a child’s life for their academic success, but its nature changes as children mature and make the transition into adulthood (Boonk et al., 2018[112]). For adolescents, parental expectations regarding educational outcomes and their capacity to be a loving and supportive carer who can maintain sufficient discipline in the household are strongly associated with academic achievement (Pinquart and Ebeling, 2020[113]; Jeynes, 2007[114]; Ulferts, 2020[115]). In contrast, with younger children parental involvement is more direct, for example, the shared reading of books or educational materials as well as learning together. As emphasized in Chapter 5, parental behaviours, such as their warmth and control, are also associated with a range of socio-emotional outcomes. However, while parental expectations about children’s and adolescents education generally have positive effects on actual academic achievement, stronger discrepancies between parents and children along these lines can potentially have negative effects. In particular, the level of actual and perceived discrepancies are negatively linked to academic outcomes, as well as adolescent life satisfaction and depression (Ulferts, 2020[115]; Wang and Benner, 2014[116]).

The level of public spending on education matters when it comes to improving educational outcomes (see below). However, private household spending on children’s education is another big factor. Private household spending includes that on educational materials and private tutoring, which is for example widely used in Korea and Japan. However, the literature is inconclusive on the effects of the latter on student achievements (Bray, 2014[117]). For example, evidence from Germany shows that, while both parents and students stated improvements in mathematic abilities after a course of private tutoring between grades seven and eight, there was no significant effect on actual educational outcomes (Guill and Bos, 2014[118]). On the other hand, Lee (2013[119]) suggests that while private tutoring can increase the achievement gap between high and low performing students in middle-school, it is also a tool to narrow the gap in high-school. Kang (2007[120]) suggests that such positive effects are modest, but in the same range as public expenditures on education.

As the central spaces where children learn, pre-school and school environments have an essential influence on the cognitive development and academic achievement of students throughout their academic trajectory. For children, the first years of life after birth are marked by considerable variations in childcare settings, depending on social norms and on how parental leave entitlements fit with the provision of care and education services (Thévenon, 2011[121]; Adema, Clarke and Thévenon, 2020[122]). Formal Early Childhood Education and Care (ECEC) is often children’s first experience of care outside of the home setting. As such, it can be an important factor that can generate long-term improvements in cognitive development and other outcomes such as school-readiness. Child outcomes may also be indirectly affected if access to formal care services has positive spill over effects into the family environment and improves family functioning by, for example, enabling parents (particularly mothers) to better balance work and family roles (Bianchi and Milkie, 2010[123]), thereby reducing parental stress, and improving the quality of parent-child interactions and time spent together (Hsin and Felfe, 2014[124]).

On the whole, the evidence on the effects of participation in early childhood care and education services on children's cognitive development and academic achievement is quite mixed, though the evidence is more promising for children from lower socio-economic status families (Burger, 2010[125]; van Huizen and Plantenga, 2018[9]). The heterogeneity of the evidence may, in part, reflect differences in programs and services characteristics (including quality and timing thereof), analytic methods (identification strategies), counterfactual conditions, and in how developmental outcomes are defined and measured (Shager et al., 2013[126]; van Huizen and Plantenga, 2018[9]). Nevertheless, children's participation in good-quality ECEC services is found to have a positive effect on young children's verbal and language skills at school entry, sometimes with lasting effects on children’s attainment, progress and social-behavioural development over the school years (Taggart et al., 2015[23]; van Huizen and Plantenga, 2018[9]; Berger, Panico and Solaz, 2020[127]).

The participation of young children in high-quality ECEC services can help improve outcomes for socio-economically disadvantaged children, chrildren with special needs, and children from diverse social, cultural and linguistic backgrounds. For example, in the United Kingdom, participation in ECEC services before the age of three has been found to enable children from low socio-economic backgrounds to achieve the minimum reading level required at school by the age of seven (Taggart et al., 2015[23]). In Norway, the steep increase in the participation in high quality ECEC after the 1975 reform was also found to have has significant positive effects on long-term educational achievement and young adult labour market status as well as in reducing welfare dependency (Havnes and Mogstad, 2011[128]).

The provision of ECEC services has the potential to reduce the socio-economic inequalities in cognitive development and educational achievement that emerge early in children's lives. However, at present, ECEC services in many countries do not seem to fulfil this goal. Participation in ECEC is often not evenly distributed across social groups. Children with migrant backgrounds, children from low-income households, and children from non-native speaking families are all less likely to attend ECEC than children with non-migrant backgrounds, children from higher-income households, or children from native-speaking families (OECD, 2019[129]). On average across European OECD countries, children from low-income families are about 1.5 times less likely than others to participate in childcare before the age of three (Adema, Clarke and Thévenon, 2016[130]; OECD, 2020[131]). ECEC costs may play a role (see Chapter 3), as also might the availability, quality, and convenience of services (OECD, 2020[131]; OECD, 2019[129]). But cultural preferences and values may also contribute to differences in participation in ECEC across groups, too (OECD, 2019[129]). In this respect, it is important that ECEC services are adaptable and can respond to the needs of children from disadvantaged and diverse backgrounds, such as thorugh their activities and in their pratices.

School climate and quality is also central for children in compulsory education. Schools and their teachers are important not only for children’s academic learning and cognitive outcomes, but also for a range of socio-emotional outcomes, including motivation, interest, and educational aspirations and ambitions (OECD, 2021[132]). A good school environment make students feel physically and emotionally safe. It fosters strong and supportive relationships between students and teachers, and among students themselves. The school environment and the student’s perceptions of it are positively linked to school engagement and classroom participation, which in turn positively impacts academic achievement (Wang and Holcombe, 2010[133]; Reyes et al., 2012[134]). A sufficiently stimulating school environment might even help mitigate the negative impact of lower socio-economic status on academic achievement (Berkowitz et al., 2017[135]).

There are a number of factors that make for a strong and positive school environment (OECD, 2020[2]; Wang and Degol, 2015[136]). These include, among other things, teachers’ classroom practices (e.g. classroom management, pacing and clarity of instruction, providing feedback), classroom characteristics (e.g. student composition), school culture (e.g. student-teacher relations, academic pressure, parental and community involvement) and school leadership (e.g. instructional leadership) (OECD, 2021[132]). Teachers’ use of working time (e.g. task management and the allocation of non-teaching time) and their well-being and job satisfaction may also be important for children’s learning outcomes (OECD, 2021[132]).

One important feature of the school environment is classroom size. In larger classes, teachers have less time to devote to each individual student and with more students, disruptions of the class are more likely. Disruptive behaviours in the classroom (e.g. untimely talking/laughing/crying, snoring in class, yelling inside or outside of the classroom, unyielding argument or debate) have consistently been associated with lower academic achievement (OECD, 2020[2]; Ning et al., 2015[137]). Classrooms with lower levels of disruptive behaviour – that is, with low levels of noise and disorder – help students to better concentrate and the teachers to devote more time to focus on the curriculum (Mostafa, Echazarra and Guillou, 2018[138]). A large body of research has investigated the associations between class size and academic achievement suggest that class size has more of an impact on academic attainment for children who are socioeconomically disadvantaged, including children with a migrant background (Schanzenbach, 2020[139]). While effects of smaller classes on test scores are fairly well established, the debate on the size of long-term effects on wages after graduation is still ongoing. Some find strong effects on later wages that even make up for the substantial costs of class-size reductions (e.g. Fredriksson, Öckert and Oosterbeek (2013[140])), while others find no significant effects on later income (e.g. Leuven and Løkken (2020[141])).

In relation to the potential importance of class size, children’s educational achievement appears also to be influenced by the resources that local and national governments spend on the schooling system (Jackson, 2018[142]; OECD, 2018[143]). For example, school finance reforms that removed the funding disparities between U.S. school have resulted in large increases in educational achievement across disadvantaged schools, yet the effects do not address within-school gaps in educational outcomes (Lafortune, Rothstein and Schanzenbach, 2018[144]; Jackson, Johnson and Persico, 2016[145]). Further, within schools specific funding mechanisms can reduce the gap in performance across students. For example, a textbook subsidy for students that fell below a certain threshold of academic performance, as low as approximately USD 100, significantly raised the average test scores of these students (Holden, 2016[146]). Reducing inequalities in students access to school resources also requires a responsive governance of school networks that involves effective steering and co-ordination and cross-regional alignment educational levels, sectors and programmes to facilitate students transition across educational programmes and tracks (OECD, 2018[143]).

The classroom itself, as the central space where children learn, is of high importance. For example, co-operative student environments where students support each other lead to better relationships inside of the classroom and increased academic achievement (Roseth, Johnson and Johnson, 2008[147]). This is in part the case as these classrooms are more conductive to learning and cognitive development (Jennings and Greenberg, 2009[148]). In addition to achievement, co-operative learning environments also positively impact attitudes of students in the classroom (Kyndt et al., 2013[149]). However, healthy competition among students, especially in co-operative environments, can also be an important contributor to academic success as it can enhance motivation and under clearly specific goal structures (Madrid, Canas and Ortega-Medina, 2007[150]; OECD, 2020[2]). Especially, inter-team competitions which combine co-operative behaviour within teams and competitive behaviour across teams, can be even more successful for children’s performance than purely co-operative environments (Morschheuser, Hamari and Maedche, 2019[151]).

Related to co-operative and competitive environments in the classroom, peer and social connections in school are also important. For example, children that who enjoy positive relations with their peers on average are performing better at school (Wentzel, 2017[3]) (see Chapter 5 for a detailed discussion of socio-emotional skills). In contrast, negative social school environments, in particular peer victimization, are related to lower academic performance (Wang et al., 2014[4]). Related to social and peer connections, as well as the school environment is the sense of belonging at school, which refers to a student’s feelings of being accepted, respected and supported in the school environment (OECD, 2020[2]). Students that feel a sense of belonging at school show better academic outcomes, a higher school-related motivation and report also better self-esteem (Slaten et al., 2015[152]; Wang and Holcombe, 2010[133]; OECD, 2020[2]).

Educational tracking systems can have substantial impact on educational inequalities. The practice differs substantially between countries. While some countries place students into ability-related schools or programmes as early as age 10, others keep students of different abilities in the same tracks. While these tracking efforts aim to provide each student with learning environments that are adequate for their ability, these positive effects typically exist for high achieving students, lower achieving students suffer from negative effects, potentially driven by worse school and peer environments in lower track schools. Overall, effects of tracking appear to be negative for student achievement across the population of pupils, especially early tracking that separates students according to ability after only a few years of primary school (Hanushek and Wößmann, 2006[153]; Lavrijsen and Nicaise, 2016[154]).

Obtaining internationally comparable data on cognitive abilities and educational achievement can be complicated as achievement tests, ability measurements and definitions often widely differ between countries. However, large scale efforts of implementing regular international student assessments have offered possibilities to study differences in cognitive outcomes and educational performance across a wide range of countries. As such, high-quality data on cognitive abilities and educational achievement can primarily be obtained from international student assessments, such as the OECD’s Programme for International Student Assessment (PISA) or the Progress in International Reading Literacy Study (PIRLS) and the Trends in Mathematics and Science Study (TIMSS) from the International Association for the Evaluation of Educational Achievement (IEA).

Importantly, these assessments collect a wide range of background information that helps to identify inequalities in children’s environments and early household conditions. This covers not only the socio-economic family background, but also the presence of educational resources at home, the level of parental support and the school climate. In this regard, PISA, PIRLS and TIMSS can shed light on children’s achievements, and uncover underlying conditions that may stimulate or inhibit successful cognitive development.

The remainder of this section reviews the availability of information in each of the dimensions pf children’s cognitive well-being identified above. Table 6.2 presents a rough mapping of the data availability, with more detailed tables in the Annex.

As shown in Annex 6.A, cross-national data on early learning and cognitive achievements, including emergent literacy and numeracy, are very rare and generally are not presented in a coherent international framework comparing more than just a few OECD countries. One of the few evaluations, the OECD International Early Learning and Child Well-Being Study (IELS) is available only for England (United Kingdom), Estonia and the United States. The IELS measures children’s development and learning across key indicators at five years of age, including emergent literacy and emergent numeracy, self-regulation, and social-emotional skills (OECD, 2020[5]). While the study measures important aspects of early cognitive development, its country reach is not (yet) wide enough for it to be used for full international comparison. It is expected that more countries will be covered in future survey rounds.

Another evaluation study on young children is the Measuring Early Learning and Outcomes (MELQO) project, which ran in 2014 as a joint initiative between UNESCO, the World Bank, the Brookings Institution’s Centre for Universal Education, and UNICEF. The project measures children’s development and learning at the start of primary school (i.e. between four and six years of age). The measurement includes literacy and numeracy outcomes and outcomes in a few other domains. However, as its main focus is children in middle- and low-income countries, participation by OECD countries is small and only includes Colombia to date.

In many countries, children’s school-readiness is examined before entering school, either as part of pre-primary education or through specific pre-school exams. For example, in Sweden, the cognitive development of children in compulsory pre-school classes in the year before starting primary school is evaluated using nationally standardized survey materials. This evaluation examines whether children’s cognitive abilities are sufficient to meet the requirement for Swedish (i.e. the language of school instruction) and math classes in the first years of primary school (Skolverket, 2020[155]). Other examples include Australia’s Australian Early Development Census (AEDC) – a teacher-completed assessment of children in the first year of full-time school, conducted every three years, covering children’s physical health, learning and cognitive development, and socio-emotional skills (AEDC, 2019[156]) – and England’s (United Kingdom) Early Years Foundation Stage Profile (EYFSP) statutory assessment – also a teacher-completed assessment, conducted at the end of pre-primary education, covering children’s physical development and personal, social and emotional development, as well as early cognitive development (DfE, 2019[157]). In future, these kinds of data, if sufficiently streamlined across countries, may be useful for building indicators on the state of children’s cognitive abilities upon school-entry. However, as discussed above, many gaps in cognitive development exist already before children enter school. Thus, even if a large scale synthesis of pre-school exam data could give a good picture of gaps existing for young children starting school do, evaluations at an even earlier point would still be necessary to identify children at risk as these children need to be reached much earlier.

In some countries, children are evaluated upon school entry in order to give teachers a baseline understanding of their cognitive abilities in order to better align teaching and supports to the needs of students. In France, for example, short nationally-standardised examinations are administered in the first and second grade of primary school to measure children’s French and math skills. The French Ministry of Education publishes statistics based on these results (DEPP, 2019[158]). Some countries also perform regular national test that examine all students at different ages and across various subject areas. Denmark, for example, has national tests that measures academic performance for children as young as the 2nd grade until they graduated from the 8th grade. These tests cover reading, mathematics, English, and a range of science topics and the resulting data is of high quality (Nandrup and Beuchert-Pedersen, 2017[159]). However, using such data for an evidence-informed framework with comparable data requires test scores being harmonised across countries. Moreover, only a number of countries use systematic testing systems on students.

Data on reading, mathematics and science literacy for representative samples of fourth graders is available in PIRLS and TIMSS. In specific cases literacy is measured among fifth graders in order to better match achievement levels across participating countries, in particular when school enrolment begins at a younger age (e.g. in New Zealand and the United Kingdom). While TIMSS also measures children’s abilities towards the end of compulsory school (8th grade), PISA offers more details on the children’s background and the school and home environment. As mentioned below, PISA also offers a better country coverage.

At five year intervals, PIRLS measures reading literacy achievements for children, currently in 58 countries, 32 of which are OECD members. The latest study was implemented in 2016, with the next evaluation planned for 2021. PIRLS evaluates two separate dimensions of literacy: the purposes of reading and the process of comprehension. The purposes of reading dimension contains domains measuring literacy experience, and the acquisition and use of information. The reading comprehension dimension covers information retrieval, the ability to make straightforward inference, interpretation and integration of information, as well as the evaluation and critique of textual content (IEA, 2015[160]).

TIMSS, on the other hand, measures mathematics and science abilities in 60 countries, 28 of which are OECD members. The latest study was implemented in 2019, with the data and results publicly available in late 2020. Math and science abilities are examined around both content and cognitive dimensions. The content dimensions specify the content matter which is assessed, including numbers, measurements and geometry, and data for math literacy as well as life science, physical science, and earth science for science literacy. The cognitive dimension for both areas measures thinking processes: knowing, applying and reasoning (IEA, 2017[161]).

Both PIRLS and TIMSS collect a wider range of parent-reported information on children’s home environment, including educational resources (e.g. number of children’s books, or presence of a computer). Similarly, there is information on whether parents engaged in early literacy and numeracy activities, such as reading books, counting things or playing with alphabet or number-related toys, as well as data on literacy and numeracy ability at school start. As a result, these surveys may also be used to understand reading, mathematics and science literacy along differences in children’s home environments. However, while both surveys contain parental educational backgrounds, there is no comprehensive index of the household’s socio-economic status. This information would be useful for a more comprehensive mapping of inequalities in children’s academic achievement.

For adolescents, the OECD’s Programme for International Student Assessment (PISA) study provides a rich source of comparable data on skills and abilities at age 15. The study is implemented every three years in all OECD member countries using nationally representative samples of 15-year-old students. It was first ran in 2000, with the latest results being for 2018. Each round of PISA contains assessments in reading, mathematics, and science literacy (Box 6.3), as well as an additional assessment in what PISA calls an “innovative domain” – usually one-off assessments, run on an ad-hoc basis, on aspects or areas not covered by the regular assessments. These innovative assessments often focus on transversal competences (e.g. problem solving), although in some rounds they concentrate on areas complimentary to one of the three regular assessments (e.g. attitudes towards science).

In addition to its detailed assessment data, the PISA study also collects a range of important background information on students, their home life, and their school environment, among other things. This data is valuable in and of itself, but also allows for the disaggregation of assessment results by, for example, socio-economic background and the home environment, including the presence of educational resources.

Even though international student assessments are particularly useful in identifying differences in student achievements across countries, there are potential problems arising from their administration in different languages. Specific language idiosyncrasies may place different cognitive demands on students which can undermine the fairness of evaluations as well as miss the conceptual domains that are being targeted in each test (El Masri, Baird and Graesser, 2016[163]). Nevertheless, both OECD and IEA (which administers TIMMS and PIRLS) make considerable efforts to ensure that the tests are as comparable as far as it is possible across countries and the varying school systems.

In terms of sampling, the methods used can sometimes misrepresent the overall student population and as a result lead to biased estimates of a countries student achievement and their evolution (Girardin, Lequesne and Thévenon, 2019[164]). This is of particularly importance for PISA, TIMSS and PIRLS, as they all use sampling methods and do not assess the whole student population country. However, there is potential for this to be corrected for by applying post-stratification methods (Freitas et al., 2016[165]).

When measuring student performance at different ages, and in particular when using two different representative tests, it is critical to identify the level of comparability between central frameworks, concepts and methodologies. The aforementioned tests measure academic performance at different ages. In the case of PIRLS & TIMSS at 10 years old and of PISA at aged 15 years old. As such, it is desirable to have a substantial degree of overlap between the tests in order to ensure that the resulting indicators represents similar domains of reading, math and science literacy across ages (Box 6.4).

There is a fairly good mapping of educational attainment in adolescence across the OECD. Data on educational attainment are available through the OECD Education Database and OECD Education at a Glance, and include, for example, the share of adolescents in secondary education and upper secondary school graduation rates. This data also inform on the share of adolescents leaving school early and not completing their formal education. The OECD also has data on the share of adolescents not in employment, education or training (NEET). In PISA, adolescents are also asked whether they repeated any grades (as well as at which grade level). This data can be used to identify grade repetition rates in both primary and secondary school.

PISA provides substantial information on children’s educational attitudes, behaviours and aspirations (see Annex Table 6.A.2 in Annex 6.A). For instance, PISA has information on the children’s own expectations regarding their educational achievement, broken down by different International Standard Classification of Education (ISCED) qualification levels. As similar information is missing from PIRLS and TIMSS data, it is not possible to infer how the educational aspirations of children evolve between middle childhood and adolescence.

Earlier PISA rounds contained questions asking students the degree to which they agreed with statements like “I want top grades in most or all of my courses”, “I want to be able to select from among the best opportunities available when I graduate” or “I want to be one of the best students in my class”. The latest PISA round in 2018 did not contain these survey items, suggesting that this type of information may no longer be collected in the future. However, PISA 2018 did contain new measures that, for the first time, looked to capture the presence and strength of a “growth mind-set”, that is, the belief that one’s own skills and abilities are malleable, as opposed to innate.

Both PISA and PIRLS contain information on student’s education-related behaviour such as the frequency of students’ absence from lessons. PIRLS only records the frequency of school absenteeism, but PISA additionally inquires whether children just skipped some classes or they arrived late to school. However, there is no information to indicate whether these absences are authorised or unauthorised. Some national sources are useful to supplement this data. For example, England (United Kingdom) collects information annual on absenteeism rates which is broken down by reason, while the Danish Ministry of Education publishes similar data, further broken down by different school levels.

PISA also contains information on children’s learning behaviours at home, especially concerning homework and studying outside of school hours. For example, adolescents are asked how many hours and minutes they spent studying before and after school on the most recent day they attended school. PIRLS only has teacher-reported data on how much homework is typically assigned, meaning that data on the share of children who are doing or not doing their assigned homework is missing.

Students’ attitudes toward school and learning activities are also included in PISA and PIRLS. In PIRLS, 4th graders rate their engagement with reading activities in school and report how much they enjoy reading for fun outside of school. PISA also asks adolescents how much they read for fun and to which degree they enjoy reading. However, PISA additionally asks adolescents to break down their reading activities into specific sources (e.g. newspapers, novels or comic books), and how often they read on paper versus on digital devices, and how much time they spend reading emails, text messages, emails and online news.

As shown in Annex Table 6.A.3 in Annex 6.A, both PISA and PIRLS and TIMSS contain a wide range of items on students’ learning environment, self-reported by students and reported by parents, teachers and school principals. Both sources provide data on the home environment. In PIRLS and TIMSS, fourth graders are asked about the number of books at home and the presence of educational supports (e.g. own bedroom, home computer), while parents are asked how many books children have. Unfortunately, there is an absence of any information being collected on whether the household owns any specific items that would contribute to the HNE. In PISA, adolescents provide information on whether a range of educational supports and resources are present in their home (e.g. desk, a quiet place to study, home computer, and internet connection), as well as the number of books in the household. Again, there is no differentiation made between books and resources that stimulate the HNE or HLE.

In terms of parental involvement and interactions with children, information is gathered in PISA and, to a lesser extent in PIRLS and TIMSS. PISA data contains parent-reported information on parents’ interactions with the adolescents (e.g. discussing school life, helping with homework, talking about books or politics). In addition, there are multiple items that inquire about the level of parental direct involvement with the adolescent’s school community (e.g. discussing children’s progress with a teacher, participation in local school government). Both, parents and children also report on how supportive parents are of the children’s efforts at school. In contrast, PIRLS does not provide such information. Despite the potential of recall bias, both PISA, and PIRLS and TIMSS provide parent-reports on early literacy and numeracy enhancing activities that parents engaged in with children such as reading books, telling stories, and playing word or counting games at home: in first grade (PISA), or before starting primary school (PIRLS and TIMSS).

PISA contains information on parents’ expectations of children’s educational achievement, indicated by level of educational qualifications, as well as similar information on children’s own expectations. This information makes it possible to identify parental aspirations for their children, and potential divergences in children’s own expectations with those of parents. Neither PIRLS nor TIMSS contain this type of information on younger children. Furthermore, OECD reports private spending by parents on education, including expenditure on textbooks and private tutoring (OECD, 2020[172]).

In terms of the school and classroom environment, again PISA and PIRLS are rich sources of information and data. In PIRLS and TIMSS, school safety and the disciplinary climate for fourth and eighth graders are reported by children’s teachers. This includes information on the safety of the neighbourhood where the school is located, whether children show respect for teachers and school property, and whether students conduct themselves in an orderly manner. At the same time, school principals report to which degree they feel that truancy, vandalism, theft, cheating and classroom disturbances, among other things, are problems within the school. PISA contains an index of the schools disciplinary climate. This is based on student’s reports, taking into account factors like how often students do not listen to teachers and how much noise and behavioural disturbances order are present during class time. PISA also often collects self-reported data from students on perceptions of teacher support and how fairly they are believe they are treated.

At the same time PISA reports data on children’s perceptions on cooperation and competition when it comes to learning. Children are asked how much as individuals they value cooperation and competition, and how much cooperation and competition are valued in their school. In addition, PISA and PIRLS contain information on student’s sense of belonging at school. Students also report on bullying and peer victimization, for example, whether other children spread lies about somebody else, and whether there are incidences are name-calling, physical violence, and of rumours being spread around. Both PISA and PIRLS collect information on (average) class sizes, as reported by either teachers or school principals.

The OECD collects data on educational spending, disaggregated by level (i.e. primary school and secondary school). This data makes it possible to build indicators on school spending across OECD. But data on ECEC spending on pre-school children is much more difficult to obtain, as the fiscal responsibilities are often shared between ministries (e.g. education or social affairs) and local governments utilise a variety of different funding streams to finance its spending. Some ECEC spending comes from non-earmarked grants, which makes it hard to estimate the exact share spent on childcare services. Overall, these estimates lack information on spending across different school districts and whether countries target specific funding programs at disadvantaged and low-performing students.

The discussion in this chapter has shown that, compared to several other areas of child well-being, there is a relatively broad range of cross-national data available on children’s cognitive development and educational achievement. This is especially the case with respect to the traditional core areas of reading, mathematics and science, which are covered comprehensively through the major international assessments (PISA, PIRLS and TIMMS). In addition, these international assessments provide a range of useful background information on children’s learning environment, which can be used to construct indicators or disaggregated data to show potential inequalities in educational achievement and cognitive abilities.

There are, however, still important gaps. One lies in the general lack of comparable cross-national data on young children’s learning. Strengthening policies to enhance children’s learning and skill development requires information on the key competences children need to develop from infancy on to both magnify learning capacities and to maintain them if they experience adversity later in childhood (i.e. a better assessment of protective factors behind learning capacities is needed). However, a shortcoming of international educational assessment frameworks is that they only measure children’s achievements at two stages of childhood: middle childhood and adolescence. Efforts to collect for data on younger children could be strengthened.

A second key gap lies in the scope of the competences covered by available cross-national data. While the information on reading, mathematics and science literacy provided by the major international assessments is hugely valuable and informative, these are not the only aspects of children’s learning relevant for well-being. As discussed earlier in Box 6.1, there is increasing recognition that children need a number of other cognitive and non-cognitive competences outside the traditional core areas, including critical and creative thinking and self-regulated learning. At present, these kinds of competences are covered irregularly or not at all by the available cross-national data.

Early language development is the precursor of communication abilities, both in speech and written form. It lays the foundation for emergent literacy and numeracy development, which are important precursors of cognitive ability gaps throughout school and later life. While typically such gaps widen further as children progress through school, early measurement of cognitive delays can pave the way for interventions that can potentially close these emerging gaps (Heckman, 2006[1]). Therefore, it is critically important to measure potential inequalities in early cognitive development, including in language development (Schoon et al., 2015[62]; Shuey and Kankaras, 2018[173]). In contrast to other emergent cognitive abilities, language development follows very clear and well established milestones beginning in the very first months of life (Feldman, 2019[24]; McLaughlin, 2011[25]). However, it is important to understand that early development is highly diverse and it should not be measured too early, as it risks natural heterogeneity across children’s development being misidentified as a cognitive delay. The appropriate age to start measuring the attainment of milestones may be around age three or a few months later (Schoon et al., 2015[62]).

While no sufficient internationally data source measuring the state of language acquisition currently exists, data on emergent literacy and numeracy is sparse. For example, the International Early Learning and Child Well-Being Study (IELS) provides some data on early cognitive skills, yet the study is limited to information on three countries only: England (United Kingdom), Estonia and the United States. The Measuring Early Learning and Outcomes (MELQO) project offers some data for low- and middle-income countries, and covers one OECD member country, Colombia. It is necessary to expand current assessment efforts to improve the measurement of emergent literacy and numeracy. For instance, a greater country coverage in the IELS could give a better understanding of where gaps in emergent cognitive development exist, and on which domains and sub-populations interventions may be most promising. Alternatively, as many countries employ school-readiness examinations, potential avenues for the future collection of administrative data could contain data on school readiness examinations, much as could also be done for data on health and cognition checks. Though this would give a more comprehensive picture of gaps in children’s cognitive abilities before entering school, it still misses the earlier years of cognitive development.

Another possibility to collect information on emerging languages and numeracy competences is to rely on care and education and health checks settings. Babies and toddlers are subject to routine health and developmental examinations that measure physical and cognitive development, eventually culminating in school-readiness examinations which determine whether the child is fit for the formal education system. As some countries already routinely record their citizen’s interactions with the health system in administrative datasets, the type of information recorded could potentially be extended to include measures of attainment of key language development milestones for infants and toddlers. The timing of these milestones are typically similar across contexts, which could make it to source such information from future administrative data collections.

While cross-national data on the skills and competences of children in middle childhood and adolescence has improved considerably in recent decades – thanks in large part to TIMMS, PIRLS and PISA – there are still important gaps in and limits to what is currently measured. As outlined in Box 6.1, there is increasing recognition that, in addition to reading, mathematics and science skills, today’s children need a range of further competences to flourish and thrive. These competences stretch from transversal cognitive skills such as problem solving, critical thinking and creative thinking, to meta-cognitive skills, socio-emotional skills (see Chapter 5), and digital skills, among other things.

Through its “innovative domain”, the OECD’s PISA study has collected valuable but limited cross-national information on certain transversal skills. Previous rounds have assessed student’s problem solving (2003), creative problem solving (2012) and collaborative problem solving (2015), for example, while PISA 2022 will run an assessment on creative thinking. PISA 2018 also contains some questions on children’s learning strategies. However, so far, this information has been collected irregularly on an ad-hoc basis, which reduces its usefulness for well-being monitoring. It also comes with the limitation that PISA covers 15-year-old students, only.

There is a general need to widen coverage and assess more consistently a broader range of children’s skills and competences outside the traditional big three areas of reading, mathematics and science. This includes a better and more regular assessment of children’s transversal cognitive skills, of self-regulated learning and “learning to learn” skills (e.g. motivation, planning, self-monitoring, self-reflection), and of digital skills (e.g. data and digital literacy). As discussed in Chapter 5, there is also a need for better comparable data on children’s socio-emotional skills more generally. As and where relevant, this kinds of assessments should also be extended in age-appropriate ways to children in early and middle childhood.

Another important limitation of data is that there is no regular and comparable data on learning outcomes of highly vulnerable groups of children, such as children who are homeless or transient, children living in out-of-care, children experiencing maltreatment at home, children with physical disabilities as well as for children who are out of school and those growing up in extreme poverty. Not much is known about their achievements and the obstacle to learning they face, but the existing evidence highlight the high risks of lower intellectual functioning and educational achievement (Parks, Stevens and Spence, 2007[174]; Fry, Langley and Shelton, 2017[175]; Geoffroy et al., 2016[176]).

While it is not uncommon for children to experience minor delays in their cognitive development or in the acquisition of concepts, some children have special education needs (SEN) and experience significant difficulties in keeping up with the speed of learning in the classroom and require additional learning supports from teachers and caregivers. These learning difficulties and disabilities span from functional disabilities to intellectual disabilities, behavioural difficulties, and can involve children having limited knowledge of the instruction and test language. These children are among the most vulnerable in the classroom, therefore it is important to measure not only their cognitive development and educational achievement but also their behaviours and attitudes.

To date there has been little effort made to integrate specific measures for SEN students into PISA, PIRLS or TIMSS (Schuelka, 2013[177]). For example, PIRLS actively excludes those students with functional and intellectual disabilities as well as non-native speakers, unless their teachers deem them fully capable of participating in the test. Though PISA typically includes a small share of students with SEN, many schools that primarily serve SEN students as well as a sizeable share of SEN students within regular schools are excluded. In addition, the final results of PISA do not offer any indicators specifically on SEN students, meaning that their cognitive development and educational outcomes stay invisible to policy makers (LeRoy et al., 2019[178]).

The long-term strategy of PISA’s (until 2024) seeks “ways to widen access of PISA for students with disabilities and other special education needs”. This has resulted in a recent special education needs feasibility study among SEN students in Canada, Dubai (United Arab Emirates), the Netherlands, Scotland (United Kingdom) and Spain, with the aim of identifying key priorities to make PISA more inclusive. The study found that while most PISA items are at the minimum partially accessible to SEN children, the item pool could be restricted and the layout simplified to ease access. Furthermore guidelines for human assistance and accessibility training could better prepare children and educators prior to assessment (OECD, 2018[179]). Future PISA implementations may offer the possibility to ascertain a more comprehensive picture of SEN students, particularly if indicators are developed that make use of an extended SEN sample. Simultaneously, it would be beneficial if PIRLS and TIMSS would undertake similar efforts to ease accessibility for SEN populations and subsequently present data on their cognitive abilities in fourth grade.

An important factor linked to children’s educational outcomes and educational achievement are their motivation to learn, mastery orientation and educational aspirations. Educational aspirations are typically formed in early life, but are reactive to experiences, both inside and outside of the school system. They are highly influenced by children’s motivations to learn and by the existence of mastery goals where students focus on mastery of a task and have the desire to acquire new skills (Hsieh, 2011[180]). Children’s awareness about educational tracks and opportunities is also key to foster their motivation to learn and help them form educational aspiration.

Measuring the development of children’s educational aspirations is important for policy makers to assess the need to develop guidance and help families navigate in the educational system. There are not sufficient data on the educational aspirations of younger children, although one thing understood is that younger children are typically more idealistic in their aspirations, with lower exposure to educational systems barriers being one of the reasons for. A high share of children hold high aspirations early in their academic trajectory but this has dropped significantly by the end of compulsory schooling. For some children, this drop in aspirations may signal disappointment, loss of motivation, a lack of support at school or in the family or a disengagement from school work. A better understanding of the factors driving change in educational aspirations is needed to develop adequate policy responses.

Currently, internationally comparable data on educational aspirations is only available for adolescents through the PISA survey. Neither PIRLS nor TIMSS inquires about the educational and occupational aspirations of students. Measuring the aspirations of children at the stage of middle childhood would help view that helping children fulfil their educational expectations is a key policy challenge.

In conclusion, compared to other areas of children’s well-being, there is currently a relatively good range of available cross-national data on children’s cognitive development and educational well-being. This is especially the case for children in middle childhood and adolescence, and especially with respect to their abilities in reading, mathematics and science. However, there is a strong need to develop data on early cognitive development, early educational aspirations, and the situation of the most vulnerable children. There is also a need to widen the range of skills and competences covered for children of all ages. To some extent, it may be possible to close some of these gaps expanding smaller cross-country studies across OECD countries, or by utilising and streamlining data collection in early health and development assessments and school-readiness examinations. Other gaps, however, will require more extensive data collection efforts.

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