Prior achievement, socioeconomic status, and baseline proficiency in PISA 2022: Evidence from linked national and international large-scale assessment data in Peru

Prior achievement, socioeconomic status, and baseline proficiency in PISA 2022: Evidence from linked national and international large-scale assessment data in Peru | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prior achievement, socioeconomic status, and baseline proficiency in PISA 2022: Evidence from linked national and international large-scale assessment data in Peru Giovanna Moreano, Alvaro Darcourt, Sadith Ramos This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8744794/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background. This study examines how students’ prior learning trajectories shape the PISA 2022 outcomes of Peruvian students. Although recent results from large-scale assessments show improvements in average learning levels, a substantial proportion of students still fail to reach expected academic standards. Understanding the origins of this persistent learning lag requires examining students’ earlier achievement and its relationship with later outcomes. Methods. National and international large-scale assessment datasets were linked to construct measures of prior achievement ( n = 4,666). Multilevel mixed-effects logistic regression models were estimated to examine the association between prior achievement and students’ odds of reaching baseline proficiency (Level 2 or above) in PISA 2022. The additional contributions of socioeconomic background at both the individual and school levels, attitudinal and instructional factors, and student gender were examined after controlling for prior achievement. Results. Students who attained higher achievement levels in the national test were substantially more likely to reach baseline proficiency in PISA. The effects of socioeconomic background were markedly smaller than those of prior achievement. Growth mindset was positively associated with higher odds of reaching Level 2 in reading. In mathematics, domain-specific attitudinal variables showed a heterogeneous pattern of associations, while none of the instructional variables were statistically significant. Male students exhibited lower odds of reaching baseline proficiency in reading but higher odds in mathematics compared to female students. Conclusions. Overall, the results highlight the cumulative nature of learning and reveal substantial learning gaps affecting students from disadvantaged contexts. They underscore the challenges faced by the Peruvian education system in ensuring that students who fall behind in the early stages of schooling develop the mathematical and reading skills required for full participation in society. The findings point to the importance of early, targeted educational policies aimed at supporting lagging students, particularly those from economically disadvantaged backgrounds. Prior achievement Socioeconomic status PISA Data linkage Large-scale assessment Introduction Large-scale learning assessments indicate that the learning outcomes of Peruvian students have improved in recent years across different grade levels, based on cross-sectional evidence. However, these observed gains remain below expected standards. High levels of educational lag persist and tend to accumulate as students progress through the school system, affecting their educational trajectories in heterogeneous ways. Results from the Programme for International Student Assessment (PISA) show that Peru has achieved larger gains in the proficiency levels attained by 15-year-old students than most other countries in the region. Nevertheless, these improvements remain insufficient to approach the OECD [1] average or the performance levels observed in the highest-performing Latin American country. Within this context, it is essential to examine how Peruvian students develop their learning trajectories from the earliest stages of schooling and to make visible the inequalities that may later constrain the development of complex skills, such as those assessed by PISA. Educational quality in Peru Peru has participated continuously in PISA since 2009. Using the benchmark for minimum proficiency set by OECD, Level 2, in the 2009 cycle only 26.5% of students reached at least this level in mathematics and 35.2% in reading (Ministerio de Educación del Perú, [Minedu] 2024a). By 2022, these proportions increased to 33.8% and 49.6%, respectively. Although these gains are modest, the most notable improvement lies in the reduction of the proportion of students performing below the OECD minimum standard. In 2009, 64.8% of students in reading and 73.5% in mathematics did not reach Level 2; by 2022, these figures declined to 50.4% and 66.2%, respectively. These improvements are particularly noteworthy in the Latin American context. During the same period, several countries that initially showed stronger performance either stagnated or experienced declines (Minedu, 2024a). Peru stands out as the only country in the region with a consistently positive trend over time. According to the OECD (2023), between 2012 and 2022 Peru recorded increases of 22.2 points in reading and 23.9 points in mathematics. Importantly, this upward trend persisted despite the disruptions caused by the COVID-19 pandemic. In contrast, Chile—the highest-performing Latin American country—experienced a decline of 11.7 points in mathematics and a non-significant increase of 3.7 points in reading. National large-scale assessments tell a more nuanced story. Since the introduction of annual census assessments in 2007, learning outcomes in second grade of primary school initially showed substantial improvement. For instance, the percentage of students reaching the expected ( satisfactory ) level in reading increased from 15.9% in 2007 to 46.4% in 2016 (Minedu, 2017b). However, this progress was not sustained: a decline was observed in 2018 (Minedu, 2019), and learning levels have not fully recovered, particularly following the COVID-19 pandemic. The most recent available data, from 2023, indicate that only 33.6% of students reached the expected level in reading at this grade (Minedu, 2024b). Taken together, evidence from national and international large-scale assessments suggests that, despite recent improvements, the Peruvian education system continues to face substantial challenges in providing equitable learning opportunities. Most students still fail to reach the minimum proficiency level established by the OECD for PISA or the satisfactory level defined in national assessments. Socioeconomic inequality and learning outcomes Background socioeconomic status (SES) is an essential predictor of school outcomes. This relationship is well documented in meta-analytic research (White, 1982; Sirin, 2005) and large-scale international assessments (Chmielewski & Reardon, 2016; Chmielewski, 2019). Moreover, the achievement gap between high and low SES students is present in wealthy (Chmielewski & Reardon, 2016) as well as developing countries (OECD, 2019; OECD, 2023) and has increased globally since 1964 (Chmielewski, 2019). The socioeconomic achievement gap reflects not only the effect of individual SES but also the impact of peer SES at the aggregate level, i.e., the socioeconomic composition of the school or classroom. Thus, children who attend schools with a larger number of higher SES students show better academic performance (Agirdag et al., 2012), an effect that persists after controlling for individual SES and other student and school variables (Caldas & Bankston, 2007; Agirdag et al., 2012). In addition, lower SES children not only perform worse in school but also report lower self-concepts of intelligence and creativity (Ivcevic & Kaufman, 2013) and lower expectations regarding their future performance (Wiederkehr et al., 2015). This evidence shows that SES would affect not only students’ achievement but also their attitudes towards learning. A substantial body of national research underscores the central role of SES in shaping academic achievement in Peru (León & Collahua, 2016; Minedu, 2016, 2024a). National assessments have shown that socioeconomic characteristics account for a significant proportion of the variance in student performance (Garret et al, 2021, Minedu 2016, 2022). Complementary analyses based on PISA contextual questionnaires indicate that Peru exhibits one of the widest socioeconomic disparities among participating countries, alongside Morocco, Guatemala, Paraguay, and Panama (OEcCD, 2023). These disparities are measured as the gap between students at the 90th and 10th percentiles of the PISA socioeconomic status index. In PISA 2022, the difference in mathematics performance between students in the top and bottom socioeconomic quartiles in Peru was 86 score points (Minedu, 2024a). Similarly, disaggregated analyses of national assessments reveal systematic achievement gaps between student subpopulations defined by socioeconomic characteristics. For example, students attending private schools generally outperform those in public schools; however, when SES is statistically controlled for, these differences are substantially reduced, highlighting the role of socioeconomic inequality and segregation within the Peruvian education system (Minedu, 2016, 2022). Comparable patterns are observed between rural and urban schools, where rural students—who typically come from more disadvantaged socioeconomic backgrounds—consistently perform at lower levels. At the school level, Peruvian students from lower socioeconomic backgrounds are disproportionately concentrated in public schools, low-cost private schools, and rural schools—contexts that often offer fewer opportunities to learn, employ less qualified teachers, and face shortages in educational resources and infrastructure (Balarin, 2015; Cueto et al., 2014; Minedu, 2024a). This situation exposes disadvantaged students to a double risk: the challenges associated with low SES are compounded by attendance at schools that frequently lack adequate conditions for academic and personal development. As a result, these students are more likely to accumulate learning deficits over the course of their schooling. The persistent socioeconomic inequities affecting the achievement of disadvantaged students, together with the fact that a substantial share of students still fails to meet the learning standards expected for their grade or educational level, underscore the importance of examining students’ performance at earlier stages of schooling. The following section provides a brief review of selected empirical evidence on prior achievement and its implications for understanding the well-documented relationship between SES and academic achievement. It also discusses the role of prior achievement in shaping the interpretation of attitudinal, instructional, and school-level factors when modeling school effects on students’ learning outcomes. Prior achievement as a predictor of later academic performance A growing body of recent research conducted across different contexts has empirically confirmed the importance of prior achievement for students’ subsequent academic performance. Using data from the National Assessment of Educational Progress (NAEP) in the United States, Kiss et al. (2019) examined the predictive relationship between early mathematics skills assessed in first grade and mathematics achievement in third grade. Early mathematics skills—operationalized as a combination of early numeracy and computation skills—were found to be strong predictors of later performance across different mathematical domains. The study also showed that the level of achievement attained in first grade (e.g., proficient versus below proficient) significantly influenced mathematics performance two years later. Evidence from Australia has similarly demonstrated the relevance of incorporating prior achievement into value-added models of student performance. Using data from cohorts assessed through the National Assessment Program – Literacy and Numeracy (NAPLAN), Getenet and Beswick (2021) showed that, after adjusting for student- and school-level sociodemographic variables, prior numeracy achievement measured in third grade was by far the strongest predictor of numeracy achievement in fifth grade. The inclusion of prior achievement led to substantial increases in explained variance ( ΔR² = 49–50%). These results were replicated across cohorts participating in the 2014–2017 assessment cycles, further underscoring the robustness of prior achievement as a predictor of later academic performance. In a similar vein, Marks (2017) also used NAPLAN cohort data to examine how the magnitude of school effects varied across different model specifications estimated for five achievement domains (Numeracy, Reading, Writing, Spelling, and Grammar). The models sequentially controlled for SES, student aptitude, prior achievement in the same domain, prior achievement across all domains, and a combination of aptitude and prior achievement. Across all specifications, estimated school effects ranged between 0.05 and 0.15. Importantly, the study found that adjusting only for SES or student aptitude tended to overestimate school effects. The author concluded that, in general, controlling for prior achievement is sufficient to obtain reliable estimates of schools’ value added to student learning. A promising, albeit debated, line of research has specifically examined the relative contribution of prior achievement and SES in value-added models of student performance (Armor et al., 2018; Marks, 2015, 2017, 2023, 2024; Marks & O’Connell, 2021). Drawing on a review of empirical studies analyzing the relationship between SES and achievement in the context of PISA, Marks and O’Connell (2021) concluded that SES (1) lacks a consensual operational definition and is frequently measured using indicators with limited reliability and only moderate intercorrelations; (2) shows low cross-national comparability, (3) operates largely through intermediary mechanisms (e.g., parental beliefs, language codes, educational resources and the required skills to use them, etc.); (4) may partly reflect statistical artifacts; and (5) exhibits effects that largely diminish once prior achievement is taken into account. Although the authors attribute several of these findings primarily to a genetic transmission of cognitive ability—an interpretation we do not endorse—their results nonetheless converge with a growing body of empirical evidence showing that the statistical association between SES and achievement is substantially attenuated, and in some cases nearly eliminated, once prior achievement is adequately controlled for. In the same vein, Marks (2022) argues that, outside the school effectiveness literature, the role of prior achievement is often overlooked in educational research, theory, and policy discussions. According to the author, this neglect is largely driven by the widespread assumption that prior achievement, like current achievement, is itself a function of students’ socioeconomic background and related characteristics. Using NAPLAN data, Marks showed that prior achievement exhibited by far the largest effect on subsequent performance and substantially reduced the estimated coefficients associated with SES. From this perspective, conclusions about the relationship between SES and current achievement derived from models that do not control for prior achievement are likely to be spurious. Beyond its statistical role, prior achievement also shapes the way students engage with the teaching–learning process. Students with stronger prior achievement are more likely to participate actively in learning activities and to benefit from instructional opportunities. In this regard, previous research has highlighted the role of prior achievement in shaping students’ interest in learning, attitudes toward school, perseverance on task, and sense of self-efficacy (Hemmings & Kay, 2010; Piñeiro et al., 2019). From a methodological standpoint, however, Caro et al. (2017) further argue that associations between student performance and contextual or instructional variables estimated using cross-sectional national or international assessment data cannot be interpreted as causal effects. More specifically, because prior achievement is typically unavailable in such datasets, it is not possible to rule out the possibility that observed associations are confounded by unobserved measures of earlier performance. In line with this argument, several studies using PISA data have reported small or even negative associations between constructivist teaching practices and student achievement in mathematics (Caro et al., 2016) and science (Chi et al., 2018; Cairns & Areepattamannil, 2017). As suggested by Caro et al. (2017), these counterintuitive findings may reflect omitted prior achievement bias , whereby estimated associations capture not only the expected positive effects of instructional practices but also a negative—likely remedial—correlation between such practices and students’ unobserved prior achievement. Recent evidence based on Peru’s participation in PISA 2022 further supports these claims (Minedu, 2025). A study reported, for instance, negative associations between self-efficacy related to mathematical reasoning and so-called “21st-century mathematics” and mathematics performance. It also found null effects for two forms of cognitive activation—one focused on fostering reasoning and the other on promoting mathematical thinking. Although the study suggested that some of these associations could be explained by non-linear relationships, the absence of prior achievement measures in the models precluded a direct examination of potential remedial dynamics. At the same time, other factors—such as growth mindset, self-efficacy in formal and applied mathematics, proactive learning behaviors, subjective familiarity with mathematical concepts, and mathematics anxiety—were significantly associated with mathematics performance, albeit with varying magnitudes and directions. Taken together, these findings highlight both the relevance of attitudinal and instructional variables and the importance of incorporating prior achievement when modeling student performance in large-scale assessments. The present study The Peruvian education system has shown measurable improvements over the last two decades; however, a substantial proportion of students still fail to develop the competencies expected at their grade level. As a result, educational quality and equity remain pressing challenges. Ensuring that most Peruvian students achieve the expected learning profile upon graduation requires a clearer understanding of how learning in the early stages of schooling relates to later academic achievement. Incorporating measures of prior achievement makes it possible to account for the cumulative nature of learning and to better interpret results derived from cross-sectional assessments such as PISA (Harris & Robinson, 2007; Stevens et al., 2008). Moreover, prior achievement measures allow for more accurate estimation of the relationships between educational inputs—such as classroom instructional practices—and student performance, by reducing bias associated with unobserved earlier learning (Caro et al., 2016; O’Dwyer et al., 2015). Accessing such information requires the integration of data from international large-scale assessments (e.g., PISA) with data from national assessments that capture students’ earlier learning outcomes. Against this background, the present study aims to contribute to a better understanding of Peru’s PISA 2022 results by examining the role of students’ prior achievement. To this end, we link data from the 2016 Student Census Evaluation (ECE 2016) with data from PISA 2022. Thus, the study addressed the following research questions (RQ): RQ1. To what extent is students’ prior achievement in the national assessment (ECE 2016) associated with their probability of reaching baseline proficiency (Level 2 or above) in reading and mathematics in PISA 2022? RQ2. To what extent are students' SES—at both the individual and school-aggregated levels—associated with their probability of reaching baseline proficiency in PISA 2022, after accounting for prior achievement? RQ3. Do attitudinal and instructional factors explain additional variation in the probability of reaching baseline proficiency in PISA 2022, beyond that explained by prior achievement and SES? RQ4. Does gender explain additional variation in the probability of reaching baseline proficiency in reading and mathematics in PISA 2022, after controlling for prior achievement, socioeconomic background, and attitudinal and instructional factors? Methods Dataset Both assessments were administered to the same student cohort at different points in time. The ECE 2016 ( N = 485,808) was administered when students were enrolled in the fourth grade of primary education (approximately 9 years old), whereas PISA 2022 ( n = 6,968) was administered when students were 15 years old and enrolled in secondary education. In Peru, 15-year-old students are typically enrolled in the fourth grade of secondary education. Based on this modal grade, most students assessed in PISA 2022 had previously participated in ECE 2016; accordingly, the matching procedure relied primarily on data from the 2016 assessment. This implies that the matched students progressed through the education system without grade repetition. Due to the sensitive nature of the data required to link the PISA and ECE databases, we requested the Peruvian Ministry of Education to provide the already linked datasets containing the information corresponding to Peruvian students who participated in both assessments. In total, 4,666 students from 320 schools were successfully matched, representing approximately 67% of the PISA 2022 sample. Because this linkage procedure does not necessarily preserve PISA’s original sampling design, we assessed potential bias across key subpopulations used in Peru’s sampling framework (gender, school sector, and school setting). The distribution of matched students closely approximated that of the target population, with the exception of rural areas, which were underrepresented in the matched sample (see Table 1). Table 1. Subpopulation distribution of students in the linked sample compared to PISA 2022 target population (%) PISA-ECE linked sample PISA 2022 target population Gender Male 50.3 51.1 Female 49.7 48.9 School sector Public 71.0 77.5 Private 29.0 22.5 School setting Urban 86.5 78.3 Rural 13.5 21.7 Table 2 shows that the distribution of students by achievement level in ECE 2016 is broadly similar in the linked dataset and in the full assessment sample. This similarity suggests that the matched sample adequately reflects the achievement distribution observed in ECE 2016. Table 2. Distribution of students across ECE 2016 achievement levels: national results and linked sample (%) ECE 2016 achievement level PISA-ECE linked sample National results Reading Pre-beginnig 5.1 9.1 Beginning 21.3 26.2 In process 35.3 33.2 Satisfactory 38.3 31.4 Math Pre-beginnig 6.2 10.7 Beginning 18.5 22.5 In process 43.9 41.6 Satisfactory 31.4 25.2 Participants A total of 4,666 Peruvian students participated in both PISA 2022, at age 15, and ECE 2016, which was administered when they were enrolled in the fourth grade of primary education (approximately 9 years old). The sample was approximately evenly distributed by gender (males = 2,346; females = 2,320). Most students attended public schools (71.0%) and were enrolled in urban schools (86.5%). The data linkage process resulted in a sample that, as noted above, closely approximates the population of 15-year-old Peruvian students enrolled in school. Table 3 presents the distribution of key subpopulations included in the analysis. Table 3. Distribution of students in the linked ECE–PISA sample by subpopulation n % Gender Male 2,346 50.3 Female 2,320 49.7 School sector Public 3,312 71.0 Private 1,354 29.0 School setting Urban 4,036 86.5 Rural 630 13.5 Measures PISA 2022 performance Student performance in mathematics and reading was assessed in PISA 2022 using computer-based adaptive tests calibrated under two-parameter logistic (2PL) item response theory (IRT) models (OECD, 2024). Achievement estimates ( M = 500, SD = 100) were operationalized through the plausible values methodology, with ten plausible values provided for each student per domain. In addition to continuous achievement estimates, PISA classifies students into qualitative proficiency levels based on their performance. In this study, achievement was operationalized as a dichotomous outcome [2] indicating whether students reached proficiency Level 2 or above (1) or did not reach Level 2 (0), which represents the OECD-defined baseline level of functional proficiency (OECD, 2024). Prior achievement The ECE is a curriculum-based assessment that provides nationally representative information on students’ learning outcomes in Peru. Similar to PISA, achievement in reading and mathematics is reported using standardized scores ( M = 500, SD = 100) derived from Rasch models (Minedu, 2018b) and classified into four achievement levels: satisfactory , in process , beginning , and pre-beginning . The satisfactory level indicates that students met the learning standards expected for the educational cycle, whereas in process reflects partial attainment of the expected learning outcomes. The beginning level indicates that students demonstrate only basic mastery of the expected content, and the pre-beginning level denotes insufficient acquisition of the skills required to reach the beginning level. In this study, ECE 2016 results in reading and mathematics were included in the models as indicators of students’ prior achievement. For analytical purposes, ECE achievement levels were treated as categorical measures of prior academic performance. Socioeconomic background Given evidence that the PISA index of economic, social, and cultural status (ESCS) tends to underestimate the socioeconomic status of Peruvian students (Minedu, 2017a), a more contextually appropriate national measure was developed and subsequently validated using principal component analysis (Minedu, 2018a). This measure is based on student self-reported information on parental education, household possessions and infrastructure, and access to basic utilities (Minedu, 2024a). This information was collected during the PISA 2022 assessment administration. Given the high level of socioeconomic segregation in the Peruvian education system (Garret et al., 2021; Minedu, 2018a), SES was included in the statistical models at both the individual level and the school-aggregated level. School-level SES was operationalized as the average SES of students within each school. Including SES at both levels allows disentangling individual and compositional effects associated with socioeconomic segregation. Control variables To construct the indices of contextual factors included in the statistical models, PISA applies 2PL IRT models to dichotomous items and the generalized partial credit model (GPCM) to polytomous items. Detailed information on the scaling procedures, as well as the validity and reliability of these indices, is provided in the PISA 2022 Technical Report (OECD, 2024). The following student-level attitudinal and instructional indices from PISA 2022 were included as control variables: Growth mindset (GROSAGR) [3] . Index reflecting students’ agreement with statements related to beliefs about the malleability of personal attributes. This scale consists of four items (ST263) [4] with four response categories ranging from “Strongly disagree” to “Strongly agree”. Subjective familiarity with mathematics concepts (FAMCON). Index capturing students’ self-reported familiarity with a set of mathematical concepts of varying difficulty, including fictitious items. The scale comprises twelve items (ST289) with five response categories ranging from “Never heard of it” to “Know it well, understand the concept”. Disciplinary climate in mathematics (DISCLIM). Index measuring the frequency with which students reported experiencing specific classroom situations in mathematics lessons. This scale includes seven items (ST273) with four response options ranging from “Every lesson” to “Never or almost never”. Exposure to formal and applied mathematics tasks (EXPOFA). Index reflecting the frequency with which students encountered different formal and applied mathematics tasks in class. This scale includes six items (ST275) with four response categories ranging from “Frequently” to “Never”. Cognitive activation in mathematics (COGACMCO; COGACRCO). Indices reflecting the frequency with which students reported that their mathematics teachers promoted mathematical thinking (COGACMCO) and reasoning (COGACRCO) during the school year. These scales consist of nine items each (ST283; ST285), with five response categories ranging from “Never or almost never” to “Every lesson or almost every lesson”. Mathematics self-efficacy: Formal and applied mathematics (MATHEFF). Index reflecting students’ confidence in performing formal and applied mathematics tasks. The scale consists of nine items (ST290) with four response options ranging from “Not at all confident” to “Very confident”. Mathematics anxiety (ANXMAT). Index capturing students’ agreement with statements reflecting anxiety toward mathematics (e.g., worry about difficulties or fear of failure). This scale includes six items (ST292) with four response categories ranging from “Strongly agree” to “Strongly disagree”. Finally, students’ gender (1 = male, 0 = female) was included as an additional control variable. Analytical approach Similar to standard logistic regression, multilevel logistic regression estimates fixed effects, that is, coefficients associated with the predictors. These coefficients reflect the association between the predictors and the log-odds of the outcome variable. The key difference between the two modeling approaches lies in the inclusion of random effects in the multilevel framework, which account for variation across groups (Snijders & Bosker, 2012). In this study, this specification allows for the capture of variability in student performance across schools. Random effects are typically assumed to be normally distributed with a mean of zero and a specific variance. Given the hierarchical structure of the data, the model simultaneously accounts for within-cluster variation (i.e., among students within the same school) and between-cluster variation (i.e., across schools). Ignoring this hierarchical structure would result in underestimated standard errors and potentially biased inferences. The multilevel logistic regression model is specified as follows: Y ij = β 0 + β 1 X 1ij + β 2 X 2ij + … + β k X kij + U j + e ij (1) Where Y ij represents the probability of reaching baseline proficiency (Level 2 or above) in PISA 2022 mathematics or reading for student i in school j ; β 0 denotes the fixed intercept; β 1 ,…,β k are the fixed-effect coefficients associated with student- and school-level predictors X 1ij ,…, X kij (e.g., prior achievement in ECE 2016, gender, student’s and school SES, and instructional and attitudinal variables from PISA 2022); U j represents the random intercept for school j ; and e ij denotes the individual-level residual term. The random effect U j captures unobserved school-level characteristics that influence the odds of reaching baseline proficiency for all students within the same school. The residual term e ij reflects unobserved individual-level variation in student performance. Statistical analysis First, we examined the distribution of students in this dataset according to their performance on both assessments. Additionally, we estimated Pearson correlations to analyze associations between constructs. Then, we fitted multilevel logistic regression models to examine the association between students’ prior achievement in ECE 2016 (RQ1) and their performance in PISA 2022, while accounting for the hierarchical structure of the data (students nested within schools). The models included students’ prior achievement in ECE 2016 (RQ1), students’ SES at both the individual level and the school-aggregated level (RQ2), as well as a set of attitudinal and instructional indices (RQ3) and student gender (RQ4), which were included as statistical controls. Given PISA 2022’s emphasis on mathematics, domain-specific attitudinal and instructional indices were included only in the mathematics models, whereas the domain-general growth mindset index (GROSAGR) was included in both reading and mathematics models. The outcome variable, PISA 2022 performance, was coded as 1 (reached Level 2 or above) and 0 (did not reach Level 2). Model fit was evaluated using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), both of which balance model fit and complexity by penalizing the inclusion of additional parameters. Compared to the AIC, the BIC imposes a stronger penalty on model complexity, favoring more parsimonious models with fewer parameters and potentially greater generalizability. For both criteria, lower values indicate better relative model fit (Kuha, 2004). All analyses were performed using the R environment (version “4.4.3”; R Core Team, 2025). We used the lme4 package (version “1.1-37”; Bates et al., 2015) to fit the multilevel mixed-effects logistic regression models. Supplementary material with all statistical analysis performed can be retrieved from the Open Science Framework (OSF) page (https://osf.io/7caud/overview?view_only=6b059cfe045149beb516f3343a455c9f). Results ECE 2016 and PISA 2022 performance As shown in Table 4 , a large majority of students classified at the pre-beginning level in ECE did not reach proficiency Level 2 in PISA, whereas most students who attained the satisfactory level in the national assessment reached or exceeded this benchmark in both reading and mathematics. Table 4 Distribution of students according to ECE achievement levels and PISA 2022 performance levels (%) ECE 2016 achievement level PISA 2022 proficiency level Level 2 or above Below Level 2 Reading Pre-beginnig 10.6 89.4 Beginning 29.0 71.0 In process 50.6 49.4 Satisfactory 80.2 19.8 Math Pre-beginnig 3.1 96.9 Beginning 11.7 88.3 In process 34.9 65.1 Satisfactory 66.5 33.5 Correlations Pearson’s correlation coefficients between study variables are shown in Table 5 . Cross-domain correlations were high in both PISA 2022 ( r = .75) and ECE 2016 ( r = .73). Achievement in ECE 2016 was strongly correlated with performance in PISA 2022 in both mathematics ( r = .56) and reading ( r = .58). Students’ SES showed stronger associations with mathematics ( r = .39) and reading ( r = .40) performance in PISA 2022—where all three variables were measured concurrently—than with mathematics ( r = .23) and reading ( r = .32) achievement in ECE 2016. A similar pattern was observed for school-level SES. Notably, correlations between SES and achievement in both assessments were consistently stronger at the school-aggregated level than at the individual student level. Interestingly, both student- and school-level SES exhibited stronger associations with reading ( r = .32–.37) than with mathematics ( r = .23–.26) outcomes in ECE 2016. Finally, the attitudinal and instructional variables measured in PISA 2022 showed generally weak associations with student achievement. Table 5 Correlations between study variables with confidence intervals 1 2 3 4 5 6 7 8 9 10 11 12 13 1. Reading a 2. Math a .75** [.74, .77] 3. Reading b .58** .58** [.56, .60] [.56, .59] 4. Math b .50** .56** .73** [.48, .52] [.54, .58] [.71, .74] 5. Student SES c .40** .39** .32** .23** [.37, .42] [.37, .42] [.29, .34] [.20, .26] 6. School SES c .46** .45** .37** .26** .74** [.44, .48] [.43, .48] [.34, .39] [.24, .29] [.73, .75] 7. GROSAGR a .21** .22** .13** .13** .05** .07** [.18, .23] [.19, .24] [.10, .16] [.10, .16] [.02, .08] [.04, .10] 8. FAMCON a .25** .33** .20** .20** .21** .23** .10** [.22, .28] [.30, .36] [.17, .23] [.17, .23] [.17, .24] [.20, .26] [.07, .13] 9. MATHEFF a .23** .33** .14** .21** .12** .10** .14** .39** [.20, .26] [.30, .35] [.11, .17] [.17, .24] [.09, .15] [.06, .13] [.11, .17] [.36, .42] 10. DISCLIM a .11** .12** .07** .05** .03 .03* .09** .13** .12** [.08, .14] [.09, .15] [.04, .10] [.01, .08] [-.00, .06] [.00, .06] [.06, .12] [.10, .16] [.09, .15] 11. EXPOFA a − .02 − .00 .01 .01 .02 − .00 .01 .19** .21** .06** [-.05, .02] [-.03, .03] [-.02, .04] [-.02, .04] [-.01, .05] [-.03, .03] [-.02, .04] [.16, .22] [.18, .24] [.02, .09] 12. COGACRCO a .03* .01 .03 .01 − .02 − .04* − .01 .13** .16** .13** .16** [.00, .06] [-.02, .04] [-.01, .06] [-.02, .04] [-.05, .01] [-.07, − .01] [-.04, .02] [.09, .16] [.13, .19] [.10, .16] [.13, .19] 13. COGACMCO a − .03* − .06** − .04* − .03 − .11** − .15** .01 .15** .20** .17** .22** .58** [-.06, − .00] [-.09, − .03] [-.07, − .01] [-.06, .00] [-.14, − .08] [-.18, − .12] [-.02, .04] [.12, .18] [.17, .23] [.14, .20] [.19, .25] [.56, .60] 14. ANXMAT a − .11** − .22** − .07** − .17** − .03* − .01 − .17** − .23** − .35** − .11** − .05** − .05** − .07** [-.14, − .08] [-.25, − .19] [-.10, − .04] [-.21, − .14] [-.07, − .00] [-.04, .02] [-.20, − .14] [-.26, − .20] [-.38, − .32] [-.14, − .08] [-.08, − .01] [-.08, − .01] [-.10, − .03] Note. Values in square brackets indicate the 95% confidence interval for each correlation. a PISA 2022 score, b ECE 2016 score, c Measured concurrently with PISA 2022. SES = Socioeconomic status, GROSAGR = Growth mindset, FAMCON = Subjective familiarity with mathematics concepts, MATHEFF = Mathematics self-efficacy: Formal and applied mathematics, DISCLIM = Disciplinary climate in mathematics, EXPOFA = Exposure to formal and applied mathematics tasks, COGACRCO = Cognitive activation in mathematics: Foster reasoning, COGACMCO = Cognitive activation in mathematics: Encourage mathematical thinking, ANXMAT = Mathematics anxiety. * p < .05, ** p < .01, *** p < .001. Multilevel mixed-effects logistic regression Model specification and baseline variance Tables 6 and 7 report the results of the multilevel mixed-effects logistic regression models estimated for reading and mathematics, respectively. These models estimate the odds of reaching Proficiency Level 2 or above in PISA 2022 and were specified with students nested within schools, including a random intercept at the school level. Results from the unconditional (null) models indicate statistically significant between-school variation in the odds of reaching baseline proficiency, thereby justifying the use of a multilevel modeling approach. The estimated variance of the school-level random intercept was σ² u = 1.20 ( SE = 1.10) for reading and σ² u = 1.38 ( SE = 1.18) for mathematics. Using the latent-variable approach, this corresponds to an intraclass correlation coefficient (ICC) of approximately .27 for reading, indicating that 27% of the total variance in the probability of reaching proficiency Level 2 is attributable to differences between schools. For mathematics, the estimated ICC was .30, suggesting that 30% of the variance in baseline proficiency is explained by between-school differences. Model fit and model comparison As shown in Tables 6 and 7 , the successive inclusion of prior achievement (Model 2), socioeconomic status at both the individual and school levels (Model 3), attitudinal and instructional variables (Model 4), and gender (Model 5) resulted in progressive improvements in model fit, as evidenced by decreasing AIC and BIC values across model specifications. Given its stronger penalty for model complexity, the BIC consistently favored more parsimonious models. The final specification achieved the best balance between explanatory power and parsimony and was therefore retained for substantive interpretation. Effects of prior achievement (RQ1) Across all model specifications, students’ prior achievement in the national assessment (ECE 2016) emerged as a strong predictor of performance in PISA 2022. Relative to students classified at the pre-beginning level in ECE, those in higher achievement categories exhibited significantly higher odds of reaching proficiency Level 2 or above in PISA. In the fully adjusted models, prior achievement remained strongly associated with PISA performance in both domains. For reading, students who attained the satisfactory level in ECE 2016 had higher odds ( OR = 20.51, 95% CI [12.30, 34.21], p < .01) of reaching Level 2 in PISA 2022 compared to students in the pre-beginning category. A similarly strong association was observed in mathematics, where attainment of the satisfactory level in ECE 2016 was associated with higher odds ( OR = 23.15, 95% CI [10.56, 50.77], p < .01) of reaching baseline proficiency. Socioeconomic background effects (RQ2) Although socioeconomic status was significantly associated with the odds of reaching baseline proficiency in reading and mathematics at both the individual and school levels (see Tables 6 and 7 ), its effects were substantially smaller than those of prior achievement in ECE 2016, which consistently emerged as the dominant predictor of PISA 2022 performance. Control variables (RQ3 & RQ4) In the fully adjusted model, growth mindset (GROSAGR) was positively associated with higher odds of reaching Level (see Table 6 ) 2 in reading in PISA 2022 ( OR = 1.56, 95% CI [1.43, 1.71], p < .001). In mathematics (see Table 7 ), general- and domain-specific attitudinal variables exhibited a more heterogeneous pattern of associations. Specifically, positive associations were observed for GROSAGR (OR = 1.30; 95% CI [1.17, 1.44], p < .001) subjective familiarity with mathematics concepts (FAMCON; OR = 1.17, 95% CI [1.11, 1.24], p < .001), and mathematics self-efficacy (MATHEFF; OR = 1.38 95% CI [1.24, 1.53], p < .001), whereas negative associations were found for exposure to formal and applied mathematics tasks (EXPOFA; OR = 0.81, 95% CI [0.73, 0.91], p < .001) and mathematics anxiety (ANXMAT; OR = 0.89, 95% CI [0.80, 0.98], p < .001). In contrast, none of the instructional variables—disciplinary climate in mathematics (DISCLIM) and cognitive activation (COGACMCO, COGACRCO)—showed statistically significant associations with the odds of reaching baseline proficiency. Controlling for prior achievement, socioeconomic background, and attitudinal and instructional variables, gender differences were observed in the odds of reaching proficiency Level 2. Male students exhibited lower odds ( OR = 0.856, 95% CI [0.7, 1.0], p < .05) of reaching baseline proficiency in reading compared to female students, indicating weak evidence of a gender difference in reading. In contrast, male students displayed significantly higher odds of reaching baseline proficiency in mathematics ( OR = 1.381, 95% CI [1.2, 1.7], p < .01). Table 6 Models predicting the odds of reaching proficiency Level 2 or above in PISA 2022 (Reading) Model 1 Model 2 Model 3 Model 4 Model 5 Beginning 3.721*** 3.190*** 2.494*** 2.489*** (0.243) (0.249) (0.263) (0.263) In process 9.263*** 7.106*** 5.847*** 5.807*** (0.238) (0.244) (0.257) (0.257) Satisfactory 35.766*** 25.920*** 20.739*** 20.512*** (0.242) (0.248) (0.261) (0.261) Student SES 1.239*** 1.196*** 1.206*** (0.054) (0.057) (0.058) School SES 2.270*** 2.226*** 2.212*** (0.085) (0.090) (0.089) GROSAGR 1.567*** 1.560*** (0.045) (0.046) Gender (Male) 0.856* (0.078) Intercept 1.110 0.108*** 0.139*** 0.166*** 0.181*** (0.073) (0.236) (0.239) (0.252) (0.255) Model information AIC 5,871.40 5,160.11 4,868.84 4,382.91 4,981.23 BIC 5,884.29 5,192.35 4,913.86 4,433.67 4,438.34 ICC (%) 26.78 18.54 6.84 6.93 6.79 N students 4,664 4,664 4,591 4,209 4,209 N schools 320 320 320 313 313 Note . Coefficients are reported as odds ratios (OR); standard errors are shown in parentheses. a The reference category for prior achievement corresponds to the pre-beginning level. SES = Socioeconomic status, GROSAGR = Growth mindset. * p < .05, ** p < .01, *** p < .001. Table 7 Models predicting the odds of reaching proficiency Level 2 or above in PISA 2022 (Math) Model 1 Model 2 Model 3 Model 4 Model 5 Beginning a 3.050*** 2.385** 1.713 1.768 (0.338) (0.342) (0.414) (0.416) In process a 13.805*** 10.196*** 7.516*** 7.714*** (0.323) (0.326) (0.395) (0.396) Satisfactory a 52.144*** 36.782*** 22.466*** 23.150*** (0.326) (0.328) (0.398) (0.400) Student SES 1.236*** 1.177** 1.166** (0.058) (0.068) (0.068) School SES 2.951*** 2.866*** 2.924*** (0.098) (0.112) (0.113) GROSAGR 1.303*** 1.300*** (0.052) (0.052) FAMCON 1.172*** 1.172*** (0.029) (0.030) DISCLIM 1.052 1.068 (0.055) (0.055) COGACMCO 0.899* 0.911 (0.058) (0.059) MATHEFF 1.401*** 1.377*** (0.052) (0.052) EXPOFA 0.815*** 0.813*** (0.055) (0.055) COGACRCO 1.000 0.998 (0.054) (0.054) ANXMAT 0.862** 0.885** (0.053) (0.054) Gender (Male) 1.381*** (0.093) Intercept 0.475*** 0.033*** 0.042*** 0.079*** 0.063*** (0.08) (0.325) (0.324) (0.397) (0.405) Model information AIC 5,595.21 4,846.31 4,570.51 3,474.14 3,462.45 BIC 5,608.10 4,878.55 4,615.53 3,560.20 3,554.66 ICC (%) 29.6 26.12 9.97 8.68 8.95 N students 4,665 4,665 4,593 3,440 3,440 N schools 320 320 304 297 297 Note . Coefficients are reported as odds ratios (OR); standard errors are shown in parentheses. a The reference category for prior achievement corresponds to the pre-beginning level. SES = Socioeconomic status, GROSAGR = Growth mindset, FAMCON = Subjective familiarity with mathematics concepts, ISCLIM = Disciplinary climate in mathematics, COGACMCO = Cognitive activation in mathematics: Encourage mathematical thinking, MATHEFF = Mathematics self-efficacy, EXPOFA = Exposure to formal and applied mathematics tasks, COGACRCO = Cognitive activation in mathematics: Foster reasoning,, ANXMAT = Mathematics anxiety. * p < .05, ** p < .01, *** p < .001. Discussion The objective of this study was to improve understanding of Peruvian students’ achievement by examining the role of prior academic performance. In a context marked by persistent educational inequality—where recent large-scale assessments, particularly those administered near the end of compulsory schooling such as PISA, have shown measurable improvements in average learning levels—it is essential to consider students’ learning trajectories over time. From this perspective, the linkage of data from a national assessment administered in fourth grade of primary education with PISA 2022 yielded promising results. This data integration enabled us to examine the relationship between early achievement and later performance (RQ1), as well as to assess the contribution of socioeconomic background at both the individual and school-aggregated levels after accounting for prior achievement (RQ2). Recognizing that educational outcomes are inherently multicausal, these relationships were further examined net of attitudinal and instructional factors (RQ3) and student gender (RQ4). With respect to RQ1, the analyses indicate that the development of reading and mathematics skills during the intermediate years of primary education—specifically in fourth grade—exerts a strong influence on students’ subsequent performance in PISA. These findings reinforce existing evidence on the cumulative nature of learning (Kiss et al., 2019 ; Getenet & Beswick, 2021 ), according to which early academic foundations play a decisive role in shaping later educational outcomes. Results from the multilevel mixed-effects logistic regression models further show that the association between prior and current achievement takes on a clearly predictive character. In both reading and mathematics, students’ performance in fourth grade strongly predicted their odds of reaching baseline proficiency (Level 2 or above) in PISA approximately six years later. Students who attained the satisfactory level in the national census assessment were substantially more likely to reach Level 2 in PISA than those who performed at lower achievement levels. Consistent with previous research (Kiss et al., 2019 ; Getenet & Beswick, 2021 ), these results suggest that a solid base of prior knowledge facilitates the acquisition of more complex skills required in later stages of schooling. Similar patterns have been documented in national studies conducted by the Peruvian Ministry of Education, which report strong effects of prior performance on learning development during secondary education (Minedu, 2016, 2022). Taken together, these findings provide further evidence that achievement gaps between high- and low-performing students persist throughout schooling. This persistence places the learning opportunities offered by the Peruvian education system under critical scrutiny. Considering students’ performance in standardized assessments, the system appears to face difficulties in enabling most students to develop the competencies expected at each educational stage. Moreover, the results suggest that existing efforts to remediate early learning difficulties remain limited and insufficient to ensure that all students progress academically from their initial starting points. With respect to RQ2, and consistent with previous evidence on the salience of socioeconomic status in educational outcomes in Peru (León & Collahua, 2013), the findings indicate that socioeconomic background remained a significant factor influencing students’ odds of reaching baseline proficiency (Level 2 or above) in PISA 2022 across model specifications. Notably, the effect of school-level socioeconomic status exceeded that of individual socioeconomic status. This result aligns with research documenting the high degree of socioeconomic segregation in the Peruvian education system, a structural feature that disproportionately affects students from low-SES backgrounds (Balarin, 2015 ; Cueto et al., 2014 ). As extensively documented in the literature, school socioeconomic composition shapes peer effects, instructional quality, access to educational resources, teacher expectations, and exposure to social and cultural capital—all of which are closely related to the development of academic skills (Cherng et al., 2013 ; Chzhen & Leesch, 2023 ). Furthermore, the aggregated SES effect was stronger in mathematics than in reading, a pattern that may reflect the greater degree of instructional specialization and institutional support required for the development of mathematical competencies, which may be more readily available in socioeconomically advantaged school environments. Despite the relevance of socioeconomic status at both the individual and school levels, its effects were substantially smaller than those of prior achievement measured in fourth grade, which consistently emerged as the strongest predictor of performance in PISA 2022 across all analyses. This finding is in line with a growing body of evidence emphasizing the central role of prior achievement in shaping later academic outcomes. Prior achievement has been shown to be not only a robust predictor in multivariate models (Kiss et al., 2019 ; Getenet & Beswick, 2021 ), but also to exert effects that are often markedly larger than those associated with background variables such as individual and school-level socioeconomic status (Marks, 2018; Marks & O’Connell, 2021 ; Marks, 2022, 2024 ). Several explanations have been proposed to account for this pattern. One line of argument holds that socioeconomic status is conceptually and empirically insufficient to explain variation in students’ academic achievement, and that many of the SES effects reported in the literature may largely reflect statistical artefacts (Marks, 2015 ; Marks & O’Connell, 2021 ). From this perspective, prior achievement represents a more informative statistical control when estimating the contribution of contextual and instructional factors to academic outcomes (Marks, 2018; Marks & O’Connell, 2021 ; Marks, 2022). Nevertheless, prior achievement is frequently omitted from both empirical research and policy-oriented analyses. This omission has been attributed to two main factors. Methodologically, the limitations inherent in cross-sectional large-scale assessments—which typically lack measures of earlier performance—are often overlooked (Caro et al., 2017 ; Marks, 2022). At a more ideological level, there is a widespread assumption that prior achievement, much like current achievement, merely acts as a proxy for students’ socioeconomic background and other origin-related characteristics (Marks & O’Connell, 2021 ; Marks, 2022). Within this framework, some authors have argued that a cognitive ability or genetic transmission model offers a more appropriate explanation for a wide range of empirical findings in achievement research (Marks & O’Connell, 2021 ). From an opposing standpoint, methodological critiques and alternative analytical approaches have been advanced, particularly in relation to evidence derived from fixed-effects models. Critics have argued that such models may be ill-suited to capturing the full complexity of socioeconomic composition effects and their relationship with academic achievement (Schiffer et al., 2020; Schiffer et al., 2021). In response, structural equation modeling approaches have been proposed as an alternative, as they allow for the explicit modeling of measurement error and may therefore be better suited to addressing potential overestimations of compositional SES effects (see Marks, 2015 ). Beyond these methodological debates, the interpretation of the empirical findings remains a central issue. Building on the consistently larger magnitude of prior achievement effects relative to those of socioeconomic status, Marks and O’Connell ( 2021 ) have argued that the traditional SES–achievement model should be replaced by a cognitive ability or genetic transmission model. However, this position overlooks a fundamental question: to what extent do differences in prior achievement already exist before students enter formal schooling? Addressing this issue, Passaretta et al. (2022) examined the role of social origin in shaping achievement inequality during schooling in Germany, the Netherlands, and the United Kingdom by estimating the extent to which later educational inequalities originate prior to entry into primary education. Their findings showed that social inequalities in language achievement were already firmly established before school entry. Moreover, these gaps remained largely stable throughout primary education in the United Kingdom and Germany, while they widened in the Netherlands. Overall, between 50% and 80% of the language achievement gaps observed at the end of primary school could be traced back to disparities that existed prior to formal schooling, whereas no more than 20% to 50% of school-age gaps emerged during schooling itself. Taking these findings into account, it becomes crucial to examine the role of prior achievement observed in the early years of primary education—or even in early childhood education—in shaping students’ performance in later stages of schooling, such as PISA. Moreover, given that educational inequality in the Peruvian education system does not diminish but rather persists throughout students’ school trajectories, an important question arises as to whether the prior achievement observed in fourth grade already reflects students’ socioeconomic background and the learning opportunities to which they had access, both at the individual level and through the schools they attended. With respect to RQ3, the results indicate that prior performance in fourth grade of primary education, while highly predictive, is not the sole factor shaping students’ PISA outcomes. In other words, low achievement in primary school does not mechanically translate into low performance at later stages of schooling. As shown in Table 4 , a non-negligible proportion of students classified in the lowest achievement levels of the national census assessment nonetheless managed to reach baseline proficiency (Level 2 or above) in PISA. Similarly, socioeconomic status did not fully account for the observed variation in achievement. Taken together, these findings point to the presence of additional factors that may enable some students to overcome initial conditions of academic disadvantage. In this study, several attitudinal factors—such as growth mindset (GROSAGR), mathematics self-efficacy (MATHEFF), and mathematics anxiety (ANXMAT)—emerged as statistically significant predictors, sustaining their effects even after controlling for both individual- and school-level socioeconomic status as well as prior achievement. These dispositions shape how students approach learning by influencing their engagement, persistence, and responses to academic challenges, thereby contributing to learning trajectories that extend beyond formal classroom instruction. In addition, the study examined instructional factors related to students’ opportunities to learn. Subjective familiarity with mathematics concepts (FAMCON) and exposure to formal and applied mathematics tasks (EXPOFA) showed statistically significant associations with achievement. These results suggest that greater exposure to academic content and learning opportunities is associated with higher odds of reaching baseline proficiency in PISA. Finally, with respect to RQ4, the findings revealed statistically significant gender differences in the odds of reaching Level 2 proficiency. After accounting for prior achievement, socioeconomic background, and attitudinal and instructional factors, male students exhibited lower odds of reaching baseline proficiency in reading compared to female students, whereas they showed higher odds of reaching the standard in mathematics. The persistence of these gender differences net of prior achievement and background characteristics suggests the influence of additional factors, potentially linked to gendered socialization processes, that differentially shape learning experiences and outcomes. Evidence from the Peruvian context indicates, for example, that girls report higher levels of mathematics anxiety and lower perceived teacher support in mathematics (Minedu, 2024c), while simultaneously exhibiting higher motivation and autonomy in reading activities compared to their male peers (Minedu, 2024d). Taken together, these findings underscore the importance of addressing not only early learning gaps but also the attitudinal, instructional, and social factors that shape students’ learning trajectories over time. They point to the need for educational interventions that move beyond remediation of early deficits and actively promote the dispositions, learning opportunities, and supportive environments that enable all students—particularly those from disadvantaged backgrounds—to fully develop their academic potential. Methodological Considerations and Implications Several methodological considerations warrant discussion. First, the inclusion of prior achievement —derived from a census-based national assessment (ECE) administered several years earlier— allowed us to explicitly address the risk that the associations estimated in the models are confounded by unobserved differences in earlier performance (see Caro et al., 2017 ). By accounting for students’ prior achievement, the analyses strengthen the internal validity of the estimated relationships and more adequately reflect the cumulative nature of learning processes. Second, although ECE scores are not directly comparable to those reported by PISA—given that the former is a curriculum-based assessment and the latter focuses on competencies required for effective participation in adult life—the results obtained in ECE 2016 proved to be highly predictive of the performance of the same students in PISA 2022. This finding supports the substantive validity of national assessment outcomes as indicators of prior achievement in models aimed at explaining performance in international large-scale assessments such as PISA. Third, it is plausible that the analytical strategy adopted in this study influenced some of the estimated effects, particularly those related to school-level socioeconomic status. Previous research suggests that latent variable approaches, such as structural equation modeling, may be better suited to explicitly account for measurement error and to mitigate potential overestimation of compositional SES effects at the school level. Future research could therefore benefit from combining data linkage strategies with latent-variable modeling frameworks. Finally, the data used in this study were derived from the linkage of two datasets: one from a national standardized assessment (ECE) and the other from an international assessment (PISA). Although the linkage procedure does not preserve PISA’s original sampling design, potential biases were explicitly examined across key strata in Peru’s sampling framework (gender, school sector, and school setting). The matched sample closely resembled the PISA target population, with rural areas constituting the most affected subgroup. Moreover, the distribution of students across achievement levels in ECE 2016 was broadly similar in the linked dataset and in the full population targeted by PISA 2022. Taken together, these results suggest that the representativeness of the findings was not seriously compromised. More broadly, these considerations underscore the value of research designs that integrate national and international large-scale assessment data. Such longitudinal and linked approaches offer a promising avenue for advancing understanding of learning trajectories, equity, and cumulative processes in education. Conclusions This study demonstrates that Peruvian students’ performance in PISA 2022 is strongly shaped by the learning they develop during primary education. Students who met the expected curricular standards in fourth grade of primary school were substantially more likely to reach the minimum level of proficiency established by the OECD, whereas those who lagged in primary school were considerably less likely to demonstrate the basic competencies assessed by PISA. Importantly, the effect of prior achievement exceeded that of socioeconomic status, a variable that traditionally explains a large share of variation in learning outcomes in the Peruvian context. The findings further show that learning gaps tend to persist over time. Although instances of academic resilience were identified—and factors related to gender, student attitudes, and instructional practices contributed to students’ likelihood of reaching baseline proficiency—the overall pattern underscores the cumulative nature of learning trajectories. Taken together, these results highlight the need for the Peruvian education system to strengthen policies and interventions that ensure effective learning from the earliest stages of schooling, particularly for students who fall behind early in their educational trajectories. Abbreviations 2PL two-parameter logistic AIC Akaike information criterion ANXMAT Mathematics anxiety BIC Bayesian information criterion COGACRCO Cognitive activation in mathematics:Foster reasoning COGACMCO Cognitive activation in mathematics:Encourage mathematical thinking DISCLIM Disciplinary climate in mathematics ECE Student Census Evaluation ESCS PISA composite index of economic, social and cultural status EXPOFA Exposure to formal and applied mathematics tasks FAMCON Subjective familiarity with mathematics concepts GROSAGR Growth mindset ICC Intraclass correlation coefficient IRT Item response theory MATHEFF Mathematics self-efficacy:Formal and applied mathematics NAEP National Assessment of Educational Progress NAPLAN National Assessment Program – Literacy and Numeracy OECD Organisation for Economic Co-operation and Development OR Odds ratio OSF Open Science Framework PISA Programme for International Student Assessment RQ Research question SES Socioeconomic status Declarations Author Contribution AD: Conceptualization, Methodology, Project administration, Supervision, Validation, Writing—original draft, Writing—review and editing. 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Review of Education , 9 (3), e3293. https://doi.org/10.1002/rev3.3293 Marks, G. N. (2023). The overwhelming importance of prior achievement when assessing school effects: Evidence from the Australian national assessments. School Effectiveness and School Improvement , 34 (1), 65–89. https://doi.org/10.1080/09243453.2022.2102042 Marks, G. N. (2024). No substantive effects of school socioeconomic composition on student achievement in Australia: A response to Sciffer, Perry and McConney. Large-scale Assessments in Education , 12 ., Article 8. https://doi.org/10.1186/s40536-024-00196-w Ministerio de Educación del Perú (2016). ¿Cuánto aprenden nuestros estudiantes al término de la educación primaria? Informe de logros de aprendizaje y sus factores asociados en la Evaluación Muestral 2013 . Oficina de Medición de la Calidad de los Aprendizajes. https://hdl.handle.net/20.500.12799/4632 Ministerio de Educación del Perú (2017a). El Perú en PISA 2015. Informe nacional de resultados . Lima: Oficina de Medición de la Calidad de los Aprendizajes. https://hdl.handle.net/20.500.12799/5896 Ministerio de Educación del Perú (2017b). Resultados de la Evaluación Censal de Estudiantes ECE 2016 . Oficina de Medición de la Calidad de los Aprendizajes. http://umc.minedu.gob.pe/wp-content/uploads/2017/04/ECE-2016-presentaci%C3%B3n-de-resultados-web.pdf Ministerio de Educación del Perú (2018a). Desafíos en la medición y el análisis del estatus socioeconómico de los estudiantes peruanos . Oficina de Medición de la Calidad de los Aprendizajes. https://hdl.handle.net/20.500.12799/5862 Ministerio de Educación del Perú (2018b). Reporte técnico de la Evaluación Censal de Estudiantes (ECE 2016). 2.° grado y 4.° grado de primaria (EBR y EIB), 2.° grado de secundaria . Oficina de Medición de la Calidad de los Aprendizajes. http://umc.minedu.gob.pe/wp-content/uploads/2018/03/Reporte-Tecnico-ECE-2016.pdf Ministerio de Educación del Perú (2019). Resultados 2018. Evaluaciones de logros de aprendizaje . Oficina de Medición de la Calidad de los Aprendizajes. http://umc.minedu.gob.pe/wp-content/uploads/2019/04/presentacion-web-ECE2018-1.pdf Ministerio de Educación del Perú (2022). El Perú en PISA 2018. Informe nacional de resultados . Oficina de Medición de la Calidad de los Aprendizajes. https://hdl.handle.net/20.500.12799/7725 Ministerio de Educación del Perú (2024a). El Perú en PISA 2022. Informe nacional de resultados . Oficina de Medición de la Calidad de los Aprendizajes. http://umc.minedu.gob.pe/wp-content/uploads/2024/12/El-Perú-en-PISA-2022-Informe-nacional-de-resultados.pdf Ministerio de Educación del Perú (2024b). ENLA 2023. Resultados de aprendizaje. Oficina de Medición de la Calidad de los Aprendizajes. http://umc.minedu.gob.pe/wp-content/uploads/2024/05/Presentacion_de_logros_de_aprendizaje_ENLA_2023.pdf Ministerio de Educación del Perú (2024c). La matemática me pone nervioso. Cómo varió la ansiedad hacia la matemática en los estudiantes peruanos en los últimos años. Zoom educativo, 13 . https://repositorio.minedu.gob.pe/handle/20.500.12799/10815?show=full Ministerio de Educación del Perú (2024d). Perfiles de motivación hacia la lectura de estudiantes de 2.° de secundaria y su relación con el rendimiento. Zoom educativo, 9 . https://hdl.handle.net/20.500.12799/10803 Ministerio de Educación del Perú (2025). Activación cognitiva, disposiciones individuales y competencia matemática en PISA 2022: relaciones lineales y no lineales . Estudios Breves, 16 . Oficina de Medición de la Calidad de los Aprendizajes. https://hdl.handle.net/20.500.12799/11491 Organisation for Economic Co-operation and Development (2019). PISA 2018 Results (Volume II): Where All Students Can Succeed. OECD Publishing. https://doi.org/10.1787/b5fd1b8f-en Organisation for Economic Co-operation and Development. (2023). PISA 2022 results (I): The state of learning and equity in education. OECD Publishing . https://doi.org/10.1787/53f23881-en PISA 2022 Technical Report . OECD Organisation for Economic Co-operation and Development, & Publishing (2024). https://doi.org/10.1787/01820d6d-en O’Dwyer, L. M., Wang, Y., & y Shields, K. A. (2015). Teaching for conceptual understanding: A cross-national comparison of the relationship between teachers’ instructional practices and student achievement in mathematics. Large-scale Assessments in Education , 3 (1), 1–30. https://doi.org/10.1186/s40536-014-0011-6 Piñeiro, I., Estévez, I., Freire, C., de Caso, A., Souto, A., & González-Sanmamed, M. (2019). The role of prior achievement as an antecedent to student homework engagement. Frontiers in Psychology , 10 (140). https://doi.org/10.3389/fpsyg.2019.00140 R Core Team. (2025). R: A Language and Environment for Statistical Computing . R Foundation for Statistical Computing. https://www.r-project.org/ Sirin, S. R. (2005). Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research. Review of Educational Research , 75 (3), 417–453. https://doi.org/10.3102/00346543075003417 Snijders, T., & Bosker, R. (2012). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling . Sage. Stevens, T., To, Y. M., Stevenson, S. J., & Lochbaum, M. (2008). The importance of physical activity and physical education in the prediction of academic achievement. Journal of sport behavior , 31 , 368–388. White, K. R. (1982). The relation between socioeconomic status and academic achievement. Psychological Bulletin , 91 (3), 461–481. https://doi.org/10.1037/0033-2909.91.3.461 Wiederkehr, V., Darnon, C., Chazal, S., Guimond, S., & Martinot, D. (2015). From social class to self-efficacy: Internalization of low social status pupils’ school performance. Social Psychology of Education , 18 (4), 769–784. https://doi.org/10.1007/s11218-015-9308-8 Footnotes Organisation for Economic Co-operation and Development These dichotomous variables were constructed using the proficiency cut-off points provided by PISA (see OECD, 2024). For each domain, the construction of the binary indicators relied on a single plausible value, which was randomly selected for analytical purposes (mathematics: PVMATH5; reading: PVREAD6). Variable names are reported in parentheses as they appear in the original PISA 2022 dataset. Item codes are reported as they appear in the PISA 2022 dataset. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8744794","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602732535,"identity":"001d5970-641b-465a-8244-8738e441901b","order_by":0,"name":"Giovanna Moreano","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACZnQBftK1SDaQbK3BAQIK5N2Zj0n83HFHnkHs8LEPH2ruyBnfSH726QbDHTlcWgwPs6VJ9p55ZtggnZY8c8axZ8ZmN9KMZ+cwPDPGqaWZx9iAt+0wY4N0jjEzD9vhxG03EoyZcxgOJ+LyE0iL4d+2w/ZgLX/+HU7cPCP9M0hLPS4t8sw8ho+BtiSCtTACGRskcsC2JOBymAEzW+Jj2bbDyW1AvzD29h02ljjzppg5xwDoO1y29B8+cPBt22Hbfunkwww/vh2W429P38ycUwEMQ1y2HIAy2HCIY7EFl/W4tYyCUTAKRsGIAwBQYFauMd0CYwAAAABJRU5ErkJggg==","orcid":"","institution":"Universidad Antonio Ruiz de Montoya","correspondingAuthor":true,"prefix":"","firstName":"Giovanna","middleName":"","lastName":"Moreano","suffix":""},{"id":602732536,"identity":"b051e34e-36ae-41d2-8808-5447ef2a3f88","order_by":1,"name":"Alvaro Darcourt","email":"","orcid":"","institution":"Innova Teaching School","correspondingAuthor":false,"prefix":"","firstName":"Alvaro","middleName":"","lastName":"Darcourt","suffix":""},{"id":602732537,"identity":"48f767bb-7f88-4608-9748-3f35c3cbcda6","order_by":2,"name":"Sadith Ramos","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sadith","middleName":"","lastName":"Ramos","suffix":""}],"badges":[],"createdAt":"2026-01-30 20:39:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8744794/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8744794/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104780030,"identity":"74dfbb89-3dbb-4e93-b849-1c39b8f83af6","added_by":"auto","created_at":"2026-03-17 07:49:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1561313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8744794/v1/05663cb0-3be8-4e54-8f68-2ecdc874cc40.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prior achievement, socioeconomic status, and baseline proficiency in PISA 2022: Evidence from linked national and international large-scale assessment data in Peru","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLarge-scale learning assessments indicate that the learning outcomes of Peruvian students have improved in recent years across different grade levels, based on cross-sectional evidence. However, these observed gains remain below expected standards. High levels of educational lag persist and tend to accumulate as students progress through the school system, affecting their educational trajectories in heterogeneous ways. Results from the Programme for International Student Assessment (PISA) show that Peru has achieved larger gains in the proficiency levels attained by 15-year-old students than most other countries in the region. Nevertheless, these improvements remain insufficient to approach the OECD\u003csup\u003e\u003csup\u003e[1]\u003c/sup\u003e\u003c/sup\u003e average or the performance levels observed in the highest-performing Latin American country. Within this context, it is essential to examine how Peruvian students develop their learning trajectories from the earliest stages of schooling and to make visible the inequalities that may later constrain the development of complex skills, such as those assessed by PISA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEducational quality in Peru\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeru has participated continuously in PISA since 2009. Using the benchmark for minimum proficiency set by OECD, Level 2, in the 2009 cycle only 26.5% of students reached at least this level in mathematics and 35.2% in reading (Ministerio de Educaci\u0026oacute;n del Per\u0026uacute;, [Minedu] 2024a). By 2022, these proportions increased to 33.8% and 49.6%, respectively. Although these gains are modest, the most notable improvement lies in the reduction of the proportion of students performing below the OECD minimum standard. In 2009, 64.8% of students in reading and 73.5% in mathematics did not reach Level 2; by 2022, these figures \u0026nbsp;declined to 50.4% and 66.2%, respectively.\u003c/p\u003e\n\u003cp\u003eThese improvements are particularly noteworthy in the Latin American context. During the same period, several countries that initially showed stronger performance either stagnated or experienced declines (Minedu, 2024a). Peru stands out as the only country in the region with a consistently positive trend over time. According to the OECD (2023), between 2012 and 2022 Peru recorded increases of 22.2 points in reading and 23.9 points in mathematics. Importantly, this upward trend persisted despite the disruptions caused by the COVID-19 pandemic. In contrast, Chile\u0026mdash;the highest-performing Latin American country\u0026mdash;experienced a decline of 11.7 points in mathematics and a non-significant increase of 3.7 points in reading.\u003c/p\u003e\n\u003cp\u003eNational large-scale assessments tell a more nuanced story. Since the introduction of annual census assessments in 2007, learning outcomes in second grade of primary school initially showed substantial improvement. For instance, the percentage of students reaching the expected (\u003cem\u003esatisfactory\u003c/em\u003e) level in reading increased from 15.9% in 2007 to 46.4% in 2016 (Minedu, 2017b). However, this progress was not sustained: a decline was observed in 2018 (Minedu, 2019), and learning levels have not fully recovered, particularly following the COVID-19 pandemic. The most recent available data, from 2023, indicate that only 33.6% of students reached the expected level in reading at this grade (Minedu, 2024b).\u003c/p\u003e\n\u003cp\u003eTaken together, evidence from national and international large-scale assessments suggests that, despite recent improvements, the Peruvian education system continues to face substantial challenges in providing equitable learning opportunities. Most students still fail to reach the minimum proficiency level established by the OECD for PISA or the satisfactory level defined in national assessments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocioeconomic inequality and learning outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBackground socioeconomic status (SES) is an essential predictor of school outcomes. This relationship is well documented in meta-analytic research (White, 1982; Sirin, 2005) and large-scale international assessments (Chmielewski \u0026amp; Reardon, 2016; Chmielewski, 2019). Moreover, the achievement gap between high and low SES students is present in wealthy (Chmielewski \u0026amp; Reardon, 2016) as well as developing countries (OECD, 2019; OECD, 2023) and has increased globally since 1964 (Chmielewski, 2019).\u003c/p\u003e\n\u003cp\u003eThe socioeconomic achievement gap reflects not only the effect of individual SES but also the impact of peer SES at the aggregate level, i.e., the socioeconomic composition of the school or classroom. Thus, children who attend schools with a larger number of higher SES students show better academic performance (Agirdag et al., 2012), an effect that persists after controlling for individual SES and other student and school variables (Caldas \u0026amp; Bankston, 2007; Agirdag et al., 2012). In addition, lower SES children not only perform worse in school but also report lower self-concepts of intelligence and creativity (Ivcevic \u0026amp; Kaufman, 2013) and lower expectations regarding their future performance (Wiederkehr et al., 2015). This evidence shows that SES would affect not only students\u0026rsquo; achievement but also their attitudes towards learning.\u003c/p\u003e\n\u003cp\u003eA substantial body of national research underscores the central role of SES in shaping academic achievement in Peru (Le\u0026oacute;n \u0026amp; Collahua, 2016; Minedu, 2016, 2024a). National assessments have shown that socioeconomic characteristics account for a significant proportion of the variance in student performance (Garret et al, 2021, Minedu 2016, 2022). Complementary analyses based on PISA contextual questionnaires indicate that Peru exhibits one of the widest socioeconomic disparities among participating countries, alongside Morocco, Guatemala, Paraguay, and Panama (OEcCD, 2023). These disparities are measured as the gap between students at the 90th and 10th percentiles of the PISA socioeconomic status index.\u003c/p\u003e\n\u003cp\u003eIn PISA 2022, the difference in mathematics performance between students in the top and bottom socioeconomic quartiles in Peru was 86 score points (Minedu, 2024a). Similarly, disaggregated analyses of national assessments reveal systematic achievement gaps between student subpopulations defined by socioeconomic characteristics. For example, students attending private schools generally outperform those in public schools; however, when SES is statistically controlled for, these differences are substantially reduced, highlighting the role of socioeconomic inequality and segregation within the Peruvian education system (Minedu, 2016, 2022). Comparable patterns are observed between rural and urban schools, where rural students\u0026mdash;who typically come from more disadvantaged socioeconomic backgrounds\u0026mdash;consistently perform at lower levels.\u003c/p\u003e\n\u003cp\u003eAt the school level, Peruvian students from lower socioeconomic backgrounds are disproportionately concentrated in public schools, low-cost private schools, and rural schools\u0026mdash;contexts that often offer fewer opportunities to learn, employ less qualified teachers, and face shortages in educational resources and infrastructure (Balarin, 2015; Cueto et al., 2014; Minedu, 2024a). This situation exposes disadvantaged students to a double risk: the challenges associated with low SES are compounded by attendance at schools that frequently lack adequate conditions for academic and personal development. As a result, these students are more likely to accumulate learning deficits over the course of their schooling.\u003c/p\u003e\n\u003cp\u003eThe persistent socioeconomic inequities affecting the achievement of disadvantaged students, together with the fact that a substantial share of students still fails to meet the learning standards expected for their grade or educational level, underscore the importance of examining students\u0026rsquo; performance at earlier stages of schooling. The following section provides a brief review of selected empirical evidence on prior achievement and its implications for understanding the well-documented relationship between SES and academic achievement. It also discusses the role of prior achievement in shaping the interpretation of attitudinal, instructional, and school-level factors when modeling school effects on students\u0026rsquo; learning outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrior achievement as a predictor of later academic performance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA growing body of recent research conducted across different contexts has empirically confirmed the importance of prior achievement for students\u0026rsquo; subsequent academic performance. Using data from the National Assessment of Educational Progress (NAEP) in the United States, Kiss et al. (2019) examined the predictive relationship between early mathematics skills assessed in first grade and mathematics achievement in third grade. Early mathematics skills\u0026mdash;operationalized as a combination of early numeracy and computation skills\u0026mdash;were found to be strong predictors of later performance across different mathematical domains. The study also showed that the level of achievement attained in first grade (e.g., proficient versus below proficient) significantly influenced mathematics performance two years later.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEvidence from Australia has similarly demonstrated the relevance of incorporating prior achievement into value-added models of student performance. Using data from cohorts assessed through the National Assessment Program \u0026ndash; Literacy and Numeracy (NAPLAN), Getenet and Beswick (2021) showed that, after adjusting for student- and school-level sociodemographic variables, prior numeracy achievement measured in third grade was by far the strongest predictor of numeracy achievement in fifth grade. The inclusion of prior achievement led to substantial increases in explained variance (\u003cem\u003e\u0026Delta;R\u0026sup2;\u003c/em\u003e = 49\u0026ndash;50%). These results were replicated across cohorts participating in the 2014\u0026ndash;2017 assessment cycles, further underscoring the robustness of prior achievement as a predictor of later academic performance. In a similar vein, Marks (2017) also used NAPLAN cohort data to examine how the magnitude of school effects varied across different model specifications estimated for five achievement domains (Numeracy, Reading, Writing, Spelling, and Grammar). The models sequentially controlled for SES, student aptitude, prior achievement in the same domain, prior achievement across all domains, and a combination of aptitude and prior achievement. Across all specifications, estimated school effects ranged between 0.05 and 0.15. Importantly, the study found that adjusting only for SES or student aptitude tended to overestimate school effects. The author concluded that, in general, controlling for prior achievement is sufficient to obtain reliable estimates of schools\u0026rsquo; value added to student learning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA promising, albeit debated, line of research has specifically examined the relative contribution of prior achievement and SES in value-added models of student performance (Armor et al., 2018; Marks, 2015, 2017, 2023, 2024; Marks \u0026amp; O\u0026rsquo;Connell, 2021). Drawing on a review of empirical studies analyzing the relationship between SES and achievement in the context of PISA, Marks and O\u0026rsquo;Connell (2021) concluded that SES (1) lacks a consensual operational definition and is frequently measured using indicators with limited reliability and only moderate intercorrelations; (2) shows low cross-national comparability, (3) operates largely through intermediary mechanisms (e.g., parental beliefs, language codes, educational resources and the required skills to use them, etc.); (4) may partly reflect statistical artifacts; and (5) exhibits effects that largely diminish once prior achievement is taken into account. Although the authors attribute several of these findings primarily to a genetic transmission of cognitive ability\u0026mdash;an interpretation we do not endorse\u0026mdash;their results nonetheless converge with a growing body of empirical evidence showing that the statistical association between SES and achievement is substantially attenuated, and in some cases nearly eliminated, once prior achievement is adequately controlled for.\u003c/p\u003e\n\u003cp\u003eIn the same vein, Marks (2022) argues that, outside the school effectiveness literature, the role of prior achievement is often overlooked in educational research, theory, and policy discussions. According to the author, this neglect is largely driven by the widespread assumption that prior achievement, like current achievement, is itself a function of students\u0026rsquo; socioeconomic background and related characteristics. Using NAPLAN data, Marks showed that prior achievement exhibited by far the largest effect on subsequent performance and substantially reduced the estimated coefficients associated with SES. From this perspective, conclusions about the relationship between SES and current achievement derived from models that do not control for prior achievement are likely to be spurious.\u003c/p\u003e\n\u003cp\u003eBeyond its statistical role, prior achievement also shapes the way students engage with the teaching\u0026ndash;learning process. Students with stronger prior achievement are more likely to participate actively in learning activities and to benefit from instructional opportunities. In this regard, previous research has highlighted the role of prior achievement in shaping students\u0026rsquo; interest in learning, attitudes toward school, perseverance on task, and sense of self-efficacy (Hemmings \u0026amp; Kay, 2010; Pi\u0026ntilde;eiro et al., 2019).\u003c/p\u003e\n\u003cp\u003eFrom a methodological standpoint, however, Caro et al. (2017) further argue that associations between student performance and contextual or instructional variables estimated using cross-sectional national or international assessment data cannot be interpreted as causal effects. More specifically, because prior achievement is typically unavailable in such datasets, it is not possible to rule out the possibility that observed associations are confounded by unobserved measures of earlier performance. In line with this argument, several studies using PISA data have reported small or even negative associations between constructivist teaching practices and student achievement in mathematics (Caro et al., 2016) and science (Chi et al., 2018; Cairns \u0026amp; Areepattamannil, 2017). As suggested by Caro et al. (2017), these counterintuitive findings may reflect omitted \u003cem\u003eprior achievement bias\u003c/em\u003e, whereby estimated associations capture not only the expected positive effects of instructional practices but also a negative\u0026mdash;likely remedial\u0026mdash;correlation between such practices and students\u0026rsquo; unobserved prior achievement.\u003c/p\u003e\n\u003cp\u003eRecent evidence based on Peru\u0026rsquo;s participation in PISA 2022 further supports these claims (Minedu, 2025). A study reported, for instance, negative associations between self-efficacy related to mathematical reasoning and so-called \u0026ldquo;21st-century mathematics\u0026rdquo; and mathematics performance. It also found null effects for two forms of cognitive activation\u0026mdash;one focused on fostering reasoning and the other on promoting mathematical thinking. Although the study suggested that some of these associations could be explained by non-linear relationships, the absence of prior achievement measures in the models precluded a direct examination of potential remedial dynamics. At the same time, other factors\u0026mdash;such as growth mindset, self-efficacy in formal and applied mathematics, proactive learning behaviors, subjective familiarity with mathematical concepts, and mathematics anxiety\u0026mdash;were significantly associated with mathematics performance, albeit with varying magnitudes and directions. Taken together, these findings highlight both the relevance of attitudinal and instructional variables and the importance of incorporating prior achievement when modeling student performance in large-scale assessments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe present study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Peruvian education system has shown measurable improvements over the last two decades; however, a substantial proportion of students still fail to develop the competencies expected at their grade level. As a result, educational quality and equity remain pressing challenges. Ensuring that most Peruvian students achieve the expected learning profile upon graduation requires a clearer understanding of how learning in the early stages of schooling relates to later academic achievement.\u003c/p\u003e\n\u003cp\u003eIncorporating measures of prior achievement makes it possible to account for the cumulative nature of learning and to better interpret results derived from cross-sectional assessments such as PISA (Harris \u0026amp; Robinson, 2007; Stevens et al., 2008). Moreover, prior achievement measures allow for more accurate estimation of the relationships between educational inputs\u0026mdash;such as classroom instructional practices\u0026mdash;and student performance, by reducing bias associated with unobserved earlier learning (Caro et al., 2016; O\u0026rsquo;Dwyer et al., 2015). Accessing such information requires the integration of data from international large-scale assessments (e.g., PISA) with data from national assessments that capture students\u0026rsquo; earlier learning outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAgainst this background, the present study aims to contribute to a better understanding of Peru\u0026rsquo;s PISA 2022 results by examining the role of students\u0026rsquo; prior achievement. To this end, we link data from the 2016 Student Census Evaluation (ECE 2016) with data from PISA 2022. Thus, the study addressed the following research questions (RQ):\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eRQ1.\u003c/strong\u003e To what extent is students\u0026rsquo; prior achievement in the national assessment (ECE 2016) associated with their probability of reaching baseline proficiency (Level 2 or above) in reading and mathematics in PISA 2022?\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRQ2.\u003c/strong\u003e To what extent are students\u0026apos; SES\u0026mdash;at both the individual and school-aggregated levels\u0026mdash;associated with their probability of reaching baseline proficiency in PISA 2022, after accounting for prior achievement?\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRQ3.\u003c/strong\u003e Do attitudinal and instructional factors explain additional variation in the probability of reaching baseline proficiency in PISA 2022, beyond that explained by prior achievement and SES?\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRQ4.\u003c/strong\u003e Does gender explain additional variation in the probability of reaching baseline proficiency in reading and mathematics in PISA 2022, after controlling for prior achievement, socioeconomic background, and attitudinal and instructional factors?\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eDataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth assessments were administered to the same student cohort at different points in time. The ECE 2016 (\u003cem\u003eN\u003c/em\u003e = 485,808) was administered when students were enrolled in the fourth grade of primary education (approximately 9 years old), whereas PISA 2022 (\u003cem\u003en\u003c/em\u003e = 6,968) was administered when students were 15 years old and enrolled in secondary education.\u003c/p\u003e\n\u003cp\u003eIn Peru, 15-year-old students are typically enrolled in the fourth grade of secondary education. Based on this modal grade, most students assessed in PISA 2022 had previously participated in ECE 2016; accordingly, the matching procedure relied primarily on data from the 2016 assessment. This implies that the matched students progressed through the education system without grade repetition.\u003c/p\u003e\n\u003cp\u003eDue to the sensitive nature of the data required to link the PISA and ECE databases, we requested the Peruvian Ministry of Education to provide the already linked datasets containing the information corresponding to Peruvian students who participated in both assessments. In total, 4,666 students from 320 schools were successfully matched, representing approximately 67% of the PISA 2022 sample. Because this linkage procedure does not necessarily preserve PISA\u0026rsquo;s original sampling design, we assessed potential bias across key subpopulations used in Peru\u0026rsquo;s sampling framework (gender, school sector, and school setting). The distribution of matched students closely approximated that of the target population, with the exception of rural areas, which were underrepresented in the matched sample (see Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eSubpopulation distribution of students in the linked sample compared to PISA 2022 target population (%)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"512\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePISA-ECE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;linked sample\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePISA 2022 target population\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e51.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSchool sector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Public\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e71.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e77.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Private\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSchool setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 shows that the distribution of students by achievement level in ECE 2016 is broadly similar in the linked dataset and in the full assessment sample. This similarity suggests that the matched sample adequately reflects the achievement distribution observed in ECE 2016.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eDistribution of students across ECE 2016 achievement levels: national results and linked sample (%)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"454\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp align=\"center\"\u003eECE 2016 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;achievement level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003ePISA-ECE linked sample \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003eNational results\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eReading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-beginnig\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e9.1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeginning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e\u0026nbsp;26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIn process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e35.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e\u0026nbsp;33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSatisfactory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e38.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e\u0026nbsp;31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eMath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePre-beginnig\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e10.7\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBeginning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e\u0026nbsp;22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIn process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e43.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e\u0026nbsp;41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSatisfactory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp align=\"center\"\u003e\u0026nbsp;25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 4,666 Peruvian students participated in both PISA 2022, at age 15, and ECE 2016, which was administered when they were enrolled in the fourth grade of primary education (approximately 9 years old). The sample was approximately evenly distributed by gender (males = 2,346; females = 2,320). Most students attended public schools (71.0%) and were enrolled in urban schools (86.5%). The data linkage process resulted in a sample that, as noted above, closely approximates the population of 15-year-old Peruvian students enrolled in school. Table 3 presents the distribution of key subpopulations included in the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3.\u003c/strong\u003e \u003cem\u003eDistribution of students in the linked ECE\u0026ndash;PISA sample by subpopulation\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"340\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSchool sector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePublic \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eSchool setting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4,036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eRural\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePISA 2022 performance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudent performance in mathematics and reading was assessed in PISA 2022 using computer-based adaptive tests calibrated under two-parameter logistic (2PL) item response theory (IRT) models (OECD, 2024). Achievement estimates (\u003cem\u003eM\u003c/em\u003e = 500, \u003cem\u003eSD\u003c/em\u003e = 100) were operationalized through the plausible values methodology, with ten plausible values provided for each student per domain. In addition to continuous achievement estimates, PISA classifies students into qualitative proficiency levels based on their performance. In this study, achievement was operationalized as a dichotomous outcome\u003csup\u003e\u003csup\u003e[2]\u003c/sup\u003e\u003c/sup\u003e indicating whether students reached proficiency Level 2 or above (1) or did not reach Level 2 (0), which represents the OECD-defined baseline level of functional proficiency (OECD, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePrior achievement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ECE is a curriculum-based assessment that provides nationally representative information on students\u0026rsquo; learning outcomes in Peru. Similar to PISA, achievement in reading and mathematics is reported using standardized scores (\u003cem\u003eM\u003c/em\u003e = 500, \u003cem\u003eSD\u003c/em\u003e = 100) derived from Rasch models (Minedu, 2018b) and classified into four achievement levels: \u003cem\u003esatisfactory\u003c/em\u003e, \u003cem\u003ein process\u003c/em\u003e, \u003cem\u003ebeginning\u003c/em\u003e, and \u003cem\u003epre-beginning\u003c/em\u003e. The \u003cem\u003esatisfactory\u003c/em\u003e level indicates that students met the learning standards expected for the educational cycle, whereas \u003cem\u003ein process\u003c/em\u003e reflects partial attainment of the expected learning outcomes. The \u003cem\u003ebeginning\u003c/em\u003e level indicates that students demonstrate only basic mastery of the expected content, and the \u003cem\u003epre-beginning\u003c/em\u003e level denotes insufficient acquisition of the skills required to reach the beginning level. In this study, ECE 2016 results in reading and mathematics were included in the models as indicators of students\u0026rsquo; prior achievement. For analytical purposes, ECE achievement levels were treated as categorical measures of prior academic performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSocioeconomic background\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven evidence that the PISA index of economic, social, and cultural status (ESCS) tends to underestimate the socioeconomic status of Peruvian students (Minedu, 2017a), a more contextually appropriate national measure was developed and subsequently validated using principal component analysis (Minedu, 2018a). This measure is based on student self-reported information on parental education, household possessions and infrastructure, and access to basic utilities (Minedu, 2024a). This information was collected during the PISA 2022 assessment administration.\u003c/p\u003e\n\u003cp\u003eGiven the high level of socioeconomic segregation in the Peruvian education system (Garret et al., 2021; Minedu, 2018a), SES was included in the statistical models at both the individual level and the school-aggregated level. School-level SES was operationalized as the average SES of students within each school. Including SES at both levels allows disentangling individual and compositional effects associated with socioeconomic segregation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eControl variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo construct the indices of contextual factors included in the statistical models, PISA applies 2PL IRT models to dichotomous items and the generalized partial credit model (GPCM) to polytomous items. Detailed information on the scaling procedures, as well as the validity and reliability of these indices, is provided in the \u003cem\u003ePISA 2022 Technical Report\u003c/em\u003e (OECD, 2024). The following student-level attitudinal and instructional indices from PISA 2022 were included as control variables:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrowth mindset (GROSAGR)\u003csup\u003e\u003cstrong\u003e\u003csup\u003e[3]\u003c/sup\u003e\u003c/strong\u003e\u003c/sup\u003e.\u003c/strong\u003e Index reflecting students\u0026rsquo; agreement with statements related to beliefs about the malleability of personal attributes. This scale consists of four items (ST263)\u003csup\u003e\u003csup\u003e[4]\u003c/sup\u003e\u003c/sup\u003e with four response categories ranging from \u0026ldquo;Strongly disagree\u0026rdquo; to \u0026ldquo;Strongly agree\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubjective familiarity with mathematics concepts (FAMCON).\u003c/strong\u003e Index capturing students\u0026rsquo; self-reported familiarity with a set of mathematical concepts of varying difficulty, including fictitious items. The scale comprises twelve items (ST289) with five response categories ranging from \u0026ldquo;Never heard of it\u0026rdquo; to \u0026ldquo;Know it well, understand the concept\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisciplinary climate in mathematics (DISCLIM).\u003c/strong\u003e Index measuring the frequency with which students reported experiencing specific classroom situations in mathematics lessons. This scale includes seven items (ST273) with four response options ranging from \u0026ldquo;Every lesson\u0026rdquo; to \u0026ldquo;Never or almost never\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure to formal and applied mathematics tasks (EXPOFA).\u003c/strong\u003e Index reflecting the frequency with which students encountered different formal and applied mathematics tasks in class. This scale includes six items (ST275) with four response categories ranging from \u0026ldquo;Frequently\u0026rdquo; to \u0026ldquo;Never\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCognitive activation in mathematics (COGACMCO; COGACRCO).\u003c/strong\u003e Indices reflecting the frequency with which students reported that their mathematics teachers promoted mathematical thinking (COGACMCO) and reasoning (COGACRCO) during the school year. These scales consist of nine items each (ST283; ST285), with five response categories ranging from \u0026ldquo;Never or almost never\u0026rdquo; to \u0026ldquo;Every lesson or almost every lesson\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMathematics self-efficacy: Formal and applied mathematics (MATHEFF).\u003c/strong\u003e Index reflecting students\u0026rsquo; confidence in performing formal and applied mathematics tasks. The scale consists of nine items (ST290) with four response options ranging from \u0026ldquo;Not at all confident\u0026rdquo; to \u0026ldquo;Very confident\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMathematics anxiety (ANXMAT).\u003c/strong\u003e Index capturing students\u0026rsquo; agreement with statements reflecting anxiety toward mathematics (e.g., worry about difficulties or fear of failure). This scale includes six items (ST292) with four response categories ranging from \u0026ldquo;Strongly agree\u0026rdquo; to \u0026ldquo;Strongly disagree\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eFinally, students\u0026rsquo; gender (1 = male, 0 = female) was included as an additional control variable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimilar to standard logistic regression, multilevel logistic regression estimates fixed effects, that is, coefficients associated with the predictors. These coefficients reflect the association between the predictors and the log-odds of the outcome variable. The key difference between the two modeling approaches lies in the inclusion of random effects in the multilevel framework, which account for variation across groups (Snijders \u0026amp; Bosker, 2012). In this study, this specification allows for the capture of variability in student performance across schools. Random effects are typically assumed to be normally distributed with a mean of zero and a specific variance.\u003c/p\u003e\n\u003cp\u003eGiven the hierarchical structure of the data, the model simultaneously accounts for within-cluster variation (i.e., among students within the same school) and between-cluster variation (i.e., across schools). Ignoring this hierarchical structure would result in underestimated standard errors and potentially biased inferences. The multilevel logistic regression model is specified as follows:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eY\u003csub\u003eij\u0026nbsp;\u003c/sub\u003e= \u0026beta;\u003csub\u003e0\u003c/sub\u003e + \u0026beta;\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003e1ij\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e+ \u003cem\u003e\u0026beta;\u003csub\u003e2\u003c/sub\u003eX\u003csub\u003e2ij\u003c/sub\u003e\u003c/em\u003e + \u0026hellip; + \u003cem\u003e\u0026beta;\u003csub\u003ek\u003c/sub\u003eX\u003csub\u003ekij\u003c/sub\u003e\u003c/em\u003e + \u003cem\u003eU\u003csub\u003ej\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e+ \u003cem\u003ee\u003csub\u003eij\u003c/sub\u003e\u003c/em\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(1)\u003c/p\u003e\n\u003cp\u003eWhere \u003cem\u003eY\u003csub\u003eij\u003c/sub\u003e\u003c/em\u003e represents the probability of reaching baseline proficiency (Level 2 or above) in PISA 2022 mathematics or reading for student \u003cem\u003ei\u003c/em\u003e in school \u003cem\u003ej\u003c/em\u003e; \u003cem\u003e\u0026beta;\u003csub\u003e0\u003c/sub\u003e\u003c/em\u003e denotes the fixed intercept; \u003cem\u003e\u0026beta;\u003csub\u003e1\u003c/sub\u003e,\u0026hellip;,\u0026beta;\u003csub\u003ek\u003c/sub\u003e\u003c/em\u003e are the fixed-effect coefficients associated with student- and school-level predictors \u003cem\u003eX\u003csub\u003e1ij\u003c/sub\u003e\u003c/em\u003e,\u0026hellip;,\u003cem\u003eX\u003csub\u003ekij\u003c/sub\u003e\u003c/em\u003e (e.g., prior achievement in ECE 2016, gender, student\u0026rsquo;s and school SES, and instructional and attitudinal variables from PISA 2022); \u003cem\u003eU\u003csub\u003ej\u003c/sub\u003e\u003c/em\u003e represents the random intercept for school \u003cem\u003ej\u003c/em\u003e; and \u003cem\u003ee\u003csub\u003eij\u003c/sub\u003e\u003c/em\u003e denotes the individual-level residual term.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe random effect \u003cem\u003eU\u003csub\u003ej\u003c/sub\u003e\u003c/em\u003e captures unobserved school-level characteristics that influence the odds of reaching baseline proficiency for all students within the same school. The residual term \u003cem\u003ee\u003csub\u003eij\u003c/sub\u003e\u003c/em\u003e reflects unobserved individual-level variation in student performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we examined the distribution of students in this dataset according to their performance on both assessments. Additionally, we estimated Pearson correlations to analyze associations between constructs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThen, we fitted multilevel logistic regression models to examine the association between students\u0026rsquo; prior achievement in ECE 2016 (RQ1) and their performance in PISA 2022, while accounting for the hierarchical structure of the data (students nested within schools). The models included students\u0026rsquo; prior achievement in ECE 2016 (RQ1), students\u0026rsquo; SES at both the individual level and the school-aggregated level (RQ2), as well as a set of attitudinal and instructional indices (RQ3) and student gender (RQ4), which were included as statistical controls. Given PISA 2022\u0026rsquo;s emphasis on mathematics, domain-specific attitudinal and instructional indices were included only in the mathematics models, whereas the domain-general growth mindset index (GROSAGR) was included in both reading and mathematics models. The outcome variable, PISA 2022 performance, was coded as 1 (reached Level 2 or above) and 0 (did not reach Level 2).\u003c/p\u003e\n\u003cp\u003eModel fit was evaluated using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), both of which balance model fit and complexity by penalizing the inclusion of additional parameters. Compared to the AIC, the BIC imposes a stronger penalty on model complexity, favoring more parsimonious models with fewer parameters and potentially greater generalizability. For both criteria, lower values indicate better relative model fit (Kuha, 2004).\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using the R environment (version \u0026ldquo;4.4.3\u0026rdquo;; R Core Team, 2025). We used the \u003cem\u003elme4\u003c/em\u003e package (version \u0026ldquo;1.1-37\u0026rdquo;; Bates et al., 2015) to fit the multilevel mixed-effects logistic regression models. Supplementary material with all statistical analysis performed can be retrieved from the Open Science Framework (OSF) page (https://osf.io/7caud/overview?view_only=6b059cfe045149beb516f3343a455c9f).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eECE 2016 and PISA 2022 performance\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, a large majority of students classified at the pre-beginning level in ECE did not reach proficiency Level 2 in PISA, whereas most students who attained the satisfactory level in the national assessment reached or exceeded this benchmark in both reading and mathematics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eDistribution of students according to ECE achievement levels and PISA 2022 performance levels (%)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eECE 2016\u003c/p\u003e \u003cp\u003eachievement level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePISA 2022\u003c/p\u003e \u003cp\u003eproficiency level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel 2\u003c/p\u003e \u003cp\u003eor above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBelow Level 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eReading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-beginnig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeginning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSatisfactory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-beginnig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeginning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSatisfactory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations\u003c/h2\u003e \u003cp\u003ePearson\u0026rsquo;s correlation coefficients between study variables are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Cross-domain correlations were high in both PISA 2022 (\u003cem\u003er\u003c/em\u003e = .75) and ECE 2016 (\u003cem\u003er\u003c/em\u003e = .73). Achievement in ECE 2016 was strongly correlated with performance in PISA 2022 in both mathematics (\u003cem\u003er\u003c/em\u003e = .56) and reading (\u003cem\u003er\u003c/em\u003e = .58). Students\u0026rsquo; SES showed stronger associations with mathematics (\u003cem\u003er\u003c/em\u003e = .39) and reading (\u003cem\u003er\u003c/em\u003e = .40) performance in PISA 2022\u0026mdash;where all three variables were measured concurrently\u0026mdash;than with mathematics (\u003cem\u003er\u003c/em\u003e = .23) and reading (\u003cem\u003er\u003c/em\u003e = .32) achievement in ECE 2016. A similar pattern was observed for school-level SES. Notably, correlations between SES and achievement in both assessments were consistently stronger at the school-aggregated level than at the individual student level. Interestingly, both student- and school-level SES exhibited stronger associations with reading (\u003cem\u003er\u003c/em\u003e = .32\u0026ndash;.37) than with mathematics (\u003cem\u003er\u003c/em\u003e = .23\u0026ndash;.26) outcomes in ECE 2016. Finally, the attitudinal and instructional variables measured in PISA 2022 showed generally weak associations with student achievement.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCorrelations between study variables with confidence intervals\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Reading\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Math\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.75**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[.74, .77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Reading\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.58**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.58**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[.56, .60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[.56, .59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Math\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.50**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.56**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.73**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[.48, .52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[.54, .58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[.71, .74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Student SES\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.40**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.39**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.32**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.23**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[.37, .42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[.37, .42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[.29, .34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[.20, .26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. School SES\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.46**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.45**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.37**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.26**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.74**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[.44, .48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[.43, .48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[.34, .39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[.24, .29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[.73, .75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. GROSAGR\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.13**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.13**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.05**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.07**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[.18, .23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[.19, .24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[.10, .16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[.10, .16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[.02, .08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[.04, .10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8. FAMCON\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.33**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.20**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.20**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.23**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.10**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[.22, .28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[.30, .36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[.17, .23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[.17, .23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[.17, .24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[.20, .26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[.07, .13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9. MATHEFF\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.23**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.33**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.14**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.12**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.10**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.14**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.39**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[.20, .26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[.30, .35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[.11, .17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[.17, .24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[.09, .15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[.06, .13]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[.11, .17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[.36, .42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10. DISCLIM\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.11**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.12**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.07**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.05**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.09**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.13**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.12**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[.08, .14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[.09, .15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[.04, .10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[.01, .08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[-.00, .06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[.00, .06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[.06, .12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[.10, .16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[.09, .15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11. EXPOFA\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.19**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.06**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-.05, .02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-.03, .03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-.02, .04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[-.02, .04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[-.01, .05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-.03, .03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[-.02, .04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[.16, .22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[.18, .24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[.02, .09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12. COGACRCO\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.13**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.16**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.13**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.16**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[.00, .06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-.02, .04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-.01, .06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[-.02, .04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[-.05, .01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-.07, \u0026minus;\u0026thinsp;.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[-.04, .02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[.09, .16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[.13, .19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[.10, .16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[.13, .19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13. COGACMCO\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.04*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.11**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.15**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.15**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.20**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.17**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e.58**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-.06, \u0026minus;\u0026thinsp;.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-.09, \u0026minus;\u0026thinsp;.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-.07, \u0026minus;\u0026thinsp;.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[-.06, .00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[-.14, \u0026minus;\u0026thinsp;.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-.18, \u0026minus;\u0026thinsp;.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[-.02, .04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[.12, .18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[.17, .23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[.14, .20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[.19, .25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e[.56, .60]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14. ANXMAT\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.11**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.07**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.17**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.17**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.23**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.35**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.11**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.05**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.05**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.07**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[-.14, \u0026minus;\u0026thinsp;.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[-.25, \u0026minus;\u0026thinsp;.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[-.10, \u0026minus;\u0026thinsp;.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[-.21, \u0026minus;\u0026thinsp;.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[-.07, \u0026minus;\u0026thinsp;.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[-.04, .02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[-.20, \u0026minus;\u0026thinsp;.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e[-.26, \u0026minus;\u0026thinsp;.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[-.38, \u0026minus;\u0026thinsp;.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e[-.14, \u0026minus;\u0026thinsp;.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e[-.08, \u0026minus;\u0026thinsp;.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e[-.08, \u0026minus;\u0026thinsp;.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e[-.10, \u0026minus;\u0026thinsp;.03]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003e\u003cem\u003eNote.\u003c/em\u003e Values in square brackets indicate the 95% confidence interval for each correlation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003ePISA 2022 score, \u003csup\u003eb\u003c/sup\u003eECE 2016 score, \u003csup\u003ec\u003c/sup\u003eMeasured concurrently with PISA 2022. SES\u0026thinsp;=\u0026thinsp;Socioeconomic status, GROSAGR\u0026thinsp;=\u0026thinsp;Growth mindset, FAMCON\u0026thinsp;=\u0026thinsp;Subjective familiarity with mathematics concepts, MATHEFF\u0026thinsp;=\u0026thinsp;Mathematics self-efficacy: Formal and applied mathematics, DISCLIM\u0026thinsp;=\u0026thinsp;Disciplinary climate in mathematics, EXPOFA\u0026thinsp;=\u0026thinsp;Exposure to formal and applied mathematics tasks, COGACRCO\u0026thinsp;=\u0026thinsp;Cognitive activation in mathematics: Foster reasoning, COGACMCO\u0026thinsp;=\u0026thinsp;Cognitive activation in mathematics: Encourage mathematical thinking, ANXMAT\u0026thinsp;=\u0026thinsp;Mathematics anxiety.\u003c/p\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e \u0026lt; .05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; .01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultilevel mixed-effects logistic regression\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eModel specification and baseline variance\u003c/h2\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e report the results of the multilevel mixed-effects logistic regression models estimated for reading and mathematics, respectively. These models estimate the odds of reaching Proficiency Level 2 or above in PISA 2022 and were specified with students nested within schools, including a random intercept at the school level.\u003c/p\u003e \u003cp\u003eResults from the unconditional (null) models indicate statistically significant between-school variation in the odds of reaching baseline proficiency, thereby justifying the use of a multilevel modeling approach. The estimated variance of the school-level random intercept was σ\u0026sup2;\u003csub\u003eu\u003c/sub\u003e = 1.20 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.10) for reading and σ\u0026sup2;\u003csub\u003eu\u003c/sub\u003e = 1.38 (\u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.18) for mathematics. Using the latent-variable approach, this corresponds to an intraclass correlation coefficient (ICC) of approximately .27 for reading, indicating that 27% of the total variance in the probability of reaching proficiency Level 2 is attributable to differences between schools. For mathematics, the estimated ICC was .30, suggesting that 30% of the variance in baseline proficiency is explained by between-school differences.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eModel fit and model comparison\u003c/h3\u003e\n\u003cp\u003eAs shown in Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the successive inclusion of prior achievement (Model 2), socioeconomic status at both the individual and school levels (Model 3), attitudinal and instructional variables (Model 4), and gender (Model 5) resulted in progressive improvements in model fit, as evidenced by decreasing AIC and BIC values across model specifications. Given its stronger penalty for model complexity, the BIC consistently favored more parsimonious models. The final specification achieved the best balance between explanatory power and parsimony and was therefore retained for substantive interpretation.\u003c/p\u003e\n\u003ch3\u003eEffects of prior achievement (RQ1)\u003c/h3\u003e\n\u003cp\u003eAcross all model specifications, students\u0026rsquo; prior achievement in the national assessment (ECE 2016) emerged as a strong predictor of performance in PISA 2022. Relative to students classified at the pre-beginning level in ECE, those in higher achievement categories exhibited significantly higher odds of reaching proficiency Level 2 or above in PISA. In the fully adjusted models, prior achievement remained strongly associated with PISA performance in both domains. For reading, students who attained the satisfactory level in ECE 2016 had higher odds (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20.51, 95% CI [12.30, 34.21], \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) of reaching Level 2 in PISA 2022 compared to students in the pre-beginning category. A similarly strong association was observed in mathematics, where attainment of the satisfactory level in ECE 2016 was associated with higher odds (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;23.15, 95% CI [10.56, 50.77], \u003cem\u003ep\u003c/em\u003e \u0026lt; .01) of reaching baseline proficiency.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSocioeconomic background effects (RQ2)\u003c/h2\u003e \u003cp\u003eAlthough socioeconomic status was significantly associated with the odds of reaching baseline proficiency in reading and mathematics at both the individual and school levels (see Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), its effects were substantially smaller than those of prior achievement in ECE 2016, which consistently emerged as the dominant predictor of PISA 2022 performance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eControl variables (RQ3 \u0026 RQ4)\u003c/h3\u003e\n\u003cp\u003eIn the fully adjusted model, growth mindset (GROSAGR) was positively associated with higher odds of reaching Level (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) 2 in reading in PISA 2022 (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.56, 95% CI [1.43, 1.71], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). In mathematics (see Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), general- and domain-specific attitudinal variables exhibited a more heterogeneous pattern of associations. Specifically, positive associations were observed for GROSAGR (OR\u0026thinsp;=\u0026thinsp;1.30; 95% CI [1.17, 1.44], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) subjective familiarity with mathematics concepts (FAMCON; \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.17, 95% CI [1.11, 1.24], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and mathematics self-efficacy (MATHEFF; \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.38 95% CI [1.24, 1.53], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), whereas negative associations were found for exposure to formal and applied mathematics tasks (EXPOFA; \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.81, 95% CI [0.73, 0.91], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and mathematics anxiety (ANXMAT; \u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.89, 95% CI [0.80, 0.98], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). In contrast, none of the instructional variables\u0026mdash;disciplinary climate in mathematics (DISCLIM) and cognitive activation (COGACMCO, COGACRCO)\u0026mdash;showed statistically significant associations with the odds of reaching baseline proficiency.\u003c/p\u003e \u003cp\u003eControlling for prior achievement, socioeconomic background, and attitudinal and instructional variables, gender differences were observed in the odds of reaching proficiency Level 2. Male students exhibited lower odds (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.856, 95% CI [0.7, 1.0], \u003cem\u003ep\u003c/em\u003e \u0026lt; .05) of reaching baseline proficiency in reading compared to female students, indicating weak evidence of a gender difference in reading. In contrast, male students displayed significantly higher odds of reaching baseline proficiency in mathematics (\u003cem\u003eOR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.381, 95% CI [1.2, 1.7], \u003cem\u003ep\u003c/em\u003e \u0026lt; .01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eModels predicting the odds of reaching proficiency Level 2 or above in PISA 2022 (Reading)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeginning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.721***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.190***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.494***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.489***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.243)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.249)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.263)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.263)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.263***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.106***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.847***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.807***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.238)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.244)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.257)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfactory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.766***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.920***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.739***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.512***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.242)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.248)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.261)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.239***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.196***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.206***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.270***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.226***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.212***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.090)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.089)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGROSAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.567***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.560***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.046)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.856*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.078)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.108***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.139***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.166***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.181***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.073)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.239)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.252)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.255)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,871.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,160.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,868.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,382.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,981.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,884.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,192.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,913.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,433.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,438.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e schools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Coefficients are reported as odds ratios (OR); standard errors are shown in parentheses. \u003csup\u003ea\u003c/sup\u003eThe reference category for prior achievement corresponds to the pre-beginning level. SES\u0026thinsp;=\u0026thinsp;Socioeconomic status, GROSAGR\u0026thinsp;=\u0026thinsp;Growth mindset.\u003c/p\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e \u0026lt; .05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; .01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eModels predicting the odds of reaching proficiency Level 2 or above in PISA 2022 (Math)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeginning\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.050***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.385**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.338)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.414)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.416)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn process\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.805***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.196***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.516***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.714***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.323)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.395)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.396)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfactory\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.144***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.782***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.466***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.150***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.328)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.398)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.400)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.236***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.177**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.166**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.068)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchool SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.951***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.866***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.924***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.113)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGROSAGR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.303***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.300***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.052)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAMCON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.172***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.172***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.030)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDISCLIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOGACMCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.899*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.059)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMATHEFF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.401***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.377***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.052)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.052)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXPOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.815***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.813***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOGACRCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANXMAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.862**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.885**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.053)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.054)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.381***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.093)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.475***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.079***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.063***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.397)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.405)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,595.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,846.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,570.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,474.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,462.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,608.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,878.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,615.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,560.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,554.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICC (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e schools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Coefficients are reported as odds ratios (OR); standard errors are shown in parentheses. \u003csup\u003ea\u003c/sup\u003eThe reference category for prior achievement corresponds to the pre-beginning level. SES\u0026thinsp;=\u0026thinsp;Socioeconomic status, GROSAGR\u0026thinsp;=\u0026thinsp;Growth mindset, FAMCON\u0026thinsp;=\u0026thinsp;Subjective familiarity with mathematics concepts, ISCLIM\u0026thinsp;=\u0026thinsp;Disciplinary climate in mathematics, COGACMCO\u0026thinsp;=\u0026thinsp;Cognitive activation in mathematics: Encourage mathematical thinking, MATHEFF\u0026thinsp;=\u0026thinsp;Mathematics self-efficacy, EXPOFA\u0026thinsp;=\u0026thinsp;Exposure to formal and applied mathematics tasks, COGACRCO\u0026thinsp;=\u0026thinsp;Cognitive activation in mathematics: Foster reasoning,, ANXMAT\u0026thinsp;=\u0026thinsp;Mathematics anxiety.\u003c/p\u003e \u003cp\u003e*\u003cem\u003ep\u003c/em\u003e \u0026lt; .05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; .01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe objective of this study was to improve understanding of Peruvian students\u0026rsquo; achievement by examining the role of prior academic performance. In a context marked by persistent educational inequality\u0026mdash;where recent large-scale assessments, particularly those administered near the end of compulsory schooling such as PISA, have shown measurable improvements in average learning levels\u0026mdash;it is essential to consider students\u0026rsquo; learning trajectories over time. From this perspective, the linkage of data from a national assessment administered in fourth grade of primary education with PISA 2022 yielded promising results. This data integration enabled us to examine the relationship between early achievement and later performance (RQ1), as well as to assess the contribution of socioeconomic background at both the individual and school-aggregated levels after accounting for prior achievement (RQ2). Recognizing that educational outcomes are inherently multicausal, these relationships were further examined net of attitudinal and instructional factors (RQ3) and student gender (RQ4).\u003c/p\u003e \u003cp\u003eWith respect to RQ1, the analyses indicate that the development of reading and mathematics skills during the intermediate years of primary education\u0026mdash;specifically in fourth grade\u0026mdash;exerts a strong influence on students\u0026rsquo; subsequent performance in PISA. These findings reinforce existing evidence on the cumulative nature of learning (Kiss et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Getenet \u0026amp; Beswick, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), according to which early academic foundations play a decisive role in shaping later educational outcomes.\u003c/p\u003e \u003cp\u003eResults from the multilevel mixed-effects logistic regression models further show that the association between prior and current achievement takes on a clearly predictive character. In both reading and mathematics, students\u0026rsquo; performance in fourth grade strongly predicted their odds of reaching baseline proficiency (Level 2 or above) in PISA approximately six years later. Students who attained the satisfactory level in the national census assessment were substantially more likely to reach Level 2 in PISA than those who performed at lower achievement levels. Consistent with previous research (Kiss et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Getenet \u0026amp; Beswick, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), these results suggest that a solid base of prior knowledge facilitates the acquisition of more complex skills required in later stages of schooling. Similar patterns have been documented in national studies conducted by the Peruvian Ministry of Education, which report strong effects of prior performance on learning development during secondary education (Minedu, 2016, 2022).\u003c/p\u003e \u003cp\u003eTaken together, these findings provide further evidence that achievement gaps between high- and low-performing students persist throughout schooling. This persistence places the learning opportunities offered by the Peruvian education system under critical scrutiny. Considering students\u0026rsquo; performance in standardized assessments, the system appears to face difficulties in enabling most students to develop the competencies expected at each educational stage. Moreover, the results suggest that existing efforts to remediate early learning difficulties remain limited and insufficient to ensure that all students progress academically from their initial starting points.\u003c/p\u003e \u003cp\u003eWith respect to RQ2, and consistent with previous evidence on the salience of socioeconomic status in educational outcomes in Peru (Le\u0026oacute;n \u0026amp; Collahua, 2013), the findings indicate that socioeconomic background remained a significant factor influencing students\u0026rsquo; odds of reaching baseline proficiency (Level 2 or above) in PISA 2022 across model specifications. Notably, the effect of school-level socioeconomic status exceeded that of individual socioeconomic status. This result aligns with research documenting the high degree of socioeconomic segregation in the Peruvian education system, a structural feature that disproportionately affects students from low-SES backgrounds (Balarin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cueto et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As extensively documented in the literature, school socioeconomic composition shapes peer effects, instructional quality, access to educational resources, teacher expectations, and exposure to social and cultural capital\u0026mdash;all of which are closely related to the development of academic skills (Cherng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Chzhen \u0026amp; Leesch, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the aggregated SES effect was stronger in mathematics than in reading, a pattern that may reflect the greater degree of instructional specialization and institutional support required for the development of mathematical competencies, which may be more readily available in socioeconomically advantaged school environments.\u003c/p\u003e \u003cp\u003eDespite the relevance of socioeconomic status at both the individual and school levels, its effects were substantially smaller than those of prior achievement measured in fourth grade, which consistently emerged as the strongest predictor of performance in PISA 2022 across all analyses. This finding is in line with a growing body of evidence emphasizing the central role of prior achievement in shaping later academic outcomes. Prior achievement has been shown to be not only a robust predictor in multivariate models (Kiss et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Getenet \u0026amp; Beswick, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), but also to exert effects that are often markedly larger than those associated with background variables such as individual and school-level socioeconomic status (Marks, 2018; Marks \u0026amp; O\u0026rsquo;Connell, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marks, 2022, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Several explanations have been proposed to account for this pattern.\u003c/p\u003e \u003cp\u003eOne line of argument holds that socioeconomic status is conceptually and empirically insufficient to explain variation in students\u0026rsquo; academic achievement, and that many of the SES effects reported in the literature may largely reflect statistical artefacts (Marks, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Marks \u0026amp; O\u0026rsquo;Connell, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). From this perspective, prior achievement represents a more informative statistical control when estimating the contribution of contextual and instructional factors to academic outcomes (Marks, 2018; Marks \u0026amp; O\u0026rsquo;Connell, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marks, 2022). Nevertheless, prior achievement is frequently omitted from both empirical research and policy-oriented analyses. This omission has been attributed to two main factors. Methodologically, the limitations inherent in cross-sectional large-scale assessments\u0026mdash;which typically lack measures of earlier performance\u0026mdash;are often overlooked (Caro et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Marks, 2022). At a more ideological level, there is a widespread assumption that prior achievement, much like current achievement, merely acts as a proxy for students\u0026rsquo; socioeconomic background and other origin-related characteristics (Marks \u0026amp; O\u0026rsquo;Connell, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marks, 2022). Within this framework, some authors have argued that a cognitive ability or genetic transmission model offers a more appropriate explanation for a wide range of empirical findings in achievement research (Marks \u0026amp; O\u0026rsquo;Connell, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom an opposing standpoint, methodological critiques and alternative analytical approaches have been advanced, particularly in relation to evidence derived from fixed-effects models. Critics have argued that such models may be ill-suited to capturing the full complexity of socioeconomic composition effects and their relationship with academic achievement (Schiffer et al., 2020; Schiffer et al., 2021). In response, structural equation modeling approaches have been proposed as an alternative, as they allow for the explicit modeling of measurement error and may therefore be better suited to addressing potential overestimations of compositional SES effects (see Marks, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond these methodological debates, the interpretation of the empirical findings remains a central issue. Building on the consistently larger magnitude of prior achievement effects relative to those of socioeconomic status, Marks and O\u0026rsquo;Connell (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have argued that the traditional SES\u0026ndash;achievement model should be replaced by a cognitive ability or genetic transmission model. However, this position overlooks a fundamental question: to what extent do differences in prior achievement already exist before students enter formal schooling? Addressing this issue, Passaretta et al. (2022) examined the role of social origin in shaping achievement inequality during schooling in Germany, the Netherlands, and the United Kingdom by estimating the extent to which later educational inequalities originate prior to entry into primary education. Their findings showed that social inequalities in language achievement were already firmly established before school entry. Moreover, these gaps remained largely stable throughout primary education in the United Kingdom and Germany, while they widened in the Netherlands. Overall, between 50% and 80% of the language achievement gaps observed at the end of primary school could be traced back to disparities that existed prior to formal schooling, whereas no more than 20% to 50% of school-age gaps emerged during schooling itself.\u003c/p\u003e \u003cp\u003eTaking these findings into account, it becomes crucial to examine the role of prior achievement observed in the early years of primary education\u0026mdash;or even in early childhood education\u0026mdash;in shaping students\u0026rsquo; performance in later stages of schooling, such as PISA. Moreover, given that educational inequality in the Peruvian education system does not diminish but rather persists throughout students\u0026rsquo; school trajectories, an important question arises as to whether the prior achievement observed in fourth grade already reflects students\u0026rsquo; socioeconomic background and the learning opportunities to which they had access, both at the individual level and through the schools they attended.\u003c/p\u003e \u003cp\u003eWith respect to RQ3, the results indicate that prior performance in fourth grade of primary education, while highly predictive, is not the sole factor shaping students\u0026rsquo; PISA outcomes. In other words, low achievement in primary school does not mechanically translate into low performance at later stages of schooling. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, a non-negligible proportion of students classified in the lowest achievement levels of the national census assessment nonetheless managed to reach baseline proficiency (Level 2 or above) in PISA. Similarly, socioeconomic status did not fully account for the observed variation in achievement. Taken together, these findings point to the presence of additional factors that may enable some students to overcome initial conditions of academic disadvantage.\u003c/p\u003e \u003cp\u003eIn this study, several attitudinal factors\u0026mdash;such as growth mindset (GROSAGR), mathematics self-efficacy (MATHEFF), and mathematics anxiety (ANXMAT)\u0026mdash;emerged as statistically significant predictors, sustaining their effects even after controlling for both individual- and school-level socioeconomic status as well as prior achievement. These dispositions shape how students approach learning by influencing their engagement, persistence, and responses to academic challenges, thereby contributing to learning trajectories that extend beyond formal classroom instruction. In addition, the study examined instructional factors related to students\u0026rsquo; opportunities to learn. Subjective familiarity with mathematics concepts (FAMCON) and exposure to formal and applied mathematics tasks (EXPOFA) showed statistically significant associations with achievement. These results suggest that greater exposure to academic content and learning opportunities is associated with higher odds of reaching baseline proficiency in PISA.\u003c/p\u003e \u003cp\u003eFinally, with respect to RQ4, the findings revealed statistically significant gender differences in the odds of reaching Level 2 proficiency. After accounting for prior achievement, socioeconomic background, and attitudinal and instructional factors, male students exhibited lower odds of reaching baseline proficiency in reading compared to female students, whereas they showed higher odds of reaching the standard in mathematics. The persistence of these gender differences net of prior achievement and background characteristics suggests the influence of additional factors, potentially linked to gendered socialization processes, that differentially shape learning experiences and outcomes. Evidence from the Peruvian context indicates, for example, that girls report higher levels of mathematics anxiety and lower perceived teacher support in mathematics (Minedu, 2024c), while simultaneously exhibiting higher motivation and autonomy in reading activities compared to their male peers (Minedu, 2024d).\u003c/p\u003e \u003cp\u003eTaken together, these findings underscore the importance of addressing not only early learning gaps but also the attitudinal, instructional, and social factors that shape students\u0026rsquo; learning trajectories over time. They point to the need for educational interventions that move beyond remediation of early deficits and actively promote the dispositions, learning opportunities, and supportive environments that enable all students\u0026mdash;particularly those from disadvantaged backgrounds\u0026mdash;to fully develop their academic potential.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMethodological Considerations and Implications\u003c/h2\u003e \u003cp\u003eSeveral methodological considerations warrant discussion. First, the inclusion of prior achievement \u0026mdash;derived from a census-based national assessment (ECE) administered several years earlier\u0026mdash; allowed us to explicitly address the risk that the associations estimated in the models are confounded by unobserved differences in earlier performance (see Caro et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). By accounting for students\u0026rsquo; prior achievement, the analyses strengthen the internal validity of the estimated relationships and more adequately reflect the cumulative nature of learning processes.\u003c/p\u003e \u003cp\u003eSecond, although ECE scores are not directly comparable to those reported by PISA\u0026mdash;given that the former is a curriculum-based assessment and the latter focuses on competencies required for effective participation in adult life\u0026mdash;the results obtained in ECE 2016 proved to be highly predictive of the performance of the same students in PISA 2022. This finding supports the substantive validity of national assessment outcomes as indicators of prior achievement in models aimed at explaining performance in international large-scale assessments such as PISA.\u003c/p\u003e \u003cp\u003eThird, it is plausible that the analytical strategy adopted in this study influenced some of the estimated effects, particularly those related to school-level socioeconomic status. Previous research suggests that latent variable approaches, such as structural equation modeling, may be better suited to explicitly account for measurement error and to mitigate potential overestimation of compositional SES effects at the school level. Future research could therefore benefit from combining data linkage strategies with latent-variable modeling frameworks.\u003c/p\u003e \u003cp\u003eFinally, the data used in this study were derived from the linkage of two datasets: one from a national standardized assessment (ECE) and the other from an international assessment (PISA). Although the linkage procedure does not preserve PISA\u0026rsquo;s original sampling design, potential biases were explicitly examined across key strata in Peru\u0026rsquo;s sampling framework (gender, school sector, and school setting). The matched sample closely resembled the PISA target population, with rural areas constituting the most affected subgroup. Moreover, the distribution of students across achievement levels in ECE 2016 was broadly similar in the linked dataset and in the full population targeted by PISA 2022. Taken together, these results suggest that the representativeness of the findings was not seriously compromised.\u003c/p\u003e \u003cp\u003eMore broadly, these considerations underscore the value of research designs that integrate national and international large-scale assessment data. Such longitudinal and linked approaches offer a promising avenue for advancing understanding of learning trajectories, equity, and cumulative processes in education.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that Peruvian students\u0026rsquo; performance in PISA 2022 is strongly shaped by the learning they develop during primary education. Students who met the expected curricular standards in fourth grade of primary school were substantially more likely to reach the minimum level of proficiency established by the OECD, whereas those who lagged in primary school were considerably less likely to demonstrate the basic competencies assessed by PISA. Importantly, the effect of prior achievement exceeded that of socioeconomic status, a variable that traditionally explains a large share of variation in learning outcomes in the Peruvian context.\u003c/p\u003e \u003cp\u003eThe findings further show that learning gaps tend to persist over time. Although instances of academic resilience were identified\u0026mdash;and factors related to gender, student attitudes, and instructional practices contributed to students\u0026rsquo; likelihood of reaching baseline proficiency\u0026mdash;the overall pattern underscores the cumulative nature of learning trajectories. Taken together, these results highlight the need for the Peruvian education system to strengthen policies and interventions that ensure effective learning from the earliest stages of schooling, particularly for students who fall behind early in their educational trajectories.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003e2PL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etwo-parameter logistic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike information criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eANXMAT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMathematics anxiety\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eBIC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBayesian information criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCOGACRCO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCognitive activation in mathematics:Foster reasoning\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCOGACMCO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCognitive activation in mathematics:Encourage mathematical thinking\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDISCLIM\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDisciplinary climate in mathematics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eECE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStudent Census Evaluation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eESCS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePISA composite index of economic, social and cultural status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEXPOFA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExposure to formal and applied mathematics tasks\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eFAMCON\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSubjective familiarity with mathematics concepts\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eGROSAGR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGrowth mindset\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eICC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntraclass correlation coefficient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIRT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eItem response theory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMATHEFF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMathematics self-efficacy:Formal and applied mathematics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNAEP\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Assessment of Educational Progress\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eNAPLAN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Assessment Program \u0026ndash; Literacy and Numeracy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOECD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOrganisation for Economic Co-operation and Development\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOSF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOpen Science Framework\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePISA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProgramme for International Student Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRQ\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eResearch question\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eSES\u003c/h2\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAD: Conceptualization, Methodology, Project administration, Supervision, Validation, Writing\u0026mdash;original draft, Writing\u0026mdash;review and editing. GM: Conceptualization, Project administration, Supervision, Validation, Writing\u0026mdash;original draft, Writing\u0026mdash;review and editing. SR: Data curation, Methodology, Formal analysis, Statistical analysis, Software, Visualization. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eWe requested the Peruvian Ministry of Education to provide the already linked ECE 2016 and PISA 2022 datasets containing information on Peruvian students who participated in both assessments through a formal request sent to [[email protected]]. Data analysis materials can be found within the OSF Link in the manuscript: [https://osf.io/7caud/overview?view_only=6b059cfe045149beb516f3343a455c9f](https:/osf.io/7caud/overview?view_only=6b059cfe045149beb516f3343a455c9f)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgirdag, O., Houtte, M. V., \u0026amp; Avermaet, P. V. (2012). 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The role of prior achievement as an antecedent to student homework engagement. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(140). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2019.00140\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2019.00140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team. (2025). \u003cem\u003eR: A Language and Environment for Statistical Computing\u003c/em\u003e. R Foundation for Statistical Computing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSirin, S. R. (2005). Socioeconomic Status and Academic Achievement: A Meta-Analytic Review of Research. \u003cem\u003eReview of Educational Research\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e(3), 417\u0026ndash;453. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3102/00346543075003417\u003c/span\u003e\u003cspan address=\"10.3102/00346543075003417\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnijders, T., \u0026amp; Bosker, R. (2012). \u003cem\u003eMultilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling\u003c/em\u003e. Sage.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevens, T., To, Y. M., Stevenson, S. J., \u0026amp; Lochbaum, M. (2008). The importance of physical activity and physical education in the prediction of academic achievement. \u003cem\u003eJournal of sport behavior\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e, 368\u0026ndash;388.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite, K. R. (1982). The relation between socioeconomic status and academic achievement. \u003cem\u003ePsychological Bulletin\u003c/em\u003e, \u003cem\u003e91\u003c/em\u003e(3), 461\u0026ndash;481. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0033-2909.91.3.461\u003c/span\u003e\u003cspan address=\"10.1037/0033-2909.91.3.461\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiederkehr, V., Darnon, C., Chazal, S., Guimond, S., \u0026amp; Martinot, D. (2015). From social class to self-efficacy: Internalization of low social status pupils\u0026rsquo; school performance. \u003cem\u003eSocial Psychology of Education\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(4), 769\u0026ndash;784. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11218-015-9308-8\u003c/span\u003e\u003cspan address=\"10.1007/s11218-015-9308-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Organisation for Economic Co-operation and Development\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e These dichotomous variables were constructed using the proficiency cut-off points provided by PISA (see OECD, 2024). For each domain, the construction of the binary indicators relied on a single plausible value, which was randomly selected for analytical purposes (mathematics: PVMATH5; reading: PVREAD6).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e \u003cspan\u003e Variable names are reported in parentheses as they appear in the original PISA 2022 dataset.\u003c/span\u003e \u003c/li\u003e\u003cli\u003e\u003cspan\u003e Item codes are reported as they appear in the PISA 2022 dataset.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prior achievement, Socioeconomic status, PISA, Data linkage, Large-scale assessment","lastPublishedDoi":"10.21203/rs.3.rs-8744794/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8744794/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eThis study examines how students\u0026rsquo; prior learning trajectories shape the PISA 2022 outcomes of Peruvian students. Although recent results from large-scale assessments show improvements in average learning levels, a substantial proportion of students still fail to reach expected academic standards. Understanding the origins of this persistent learning lag requires examining students\u0026rsquo; earlier achievement and its relationship with later outcomes.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eNational and international large-scale assessment datasets were linked to construct measures of prior achievement (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4,666). Multilevel mixed-effects logistic regression models were estimated to examine the association between prior achievement and students\u0026rsquo; odds of reaching baseline proficiency (Level 2 or above) in PISA 2022. The additional contributions of socioeconomic background at both the individual and school levels, attitudinal and instructional factors, and student gender were examined after controlling for prior achievement.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eStudents who attained higher achievement levels in the national test were substantially more likely to reach baseline proficiency in PISA. The effects of socioeconomic background were markedly smaller than those of prior achievement. Growth mindset was positively associated with higher odds of reaching Level 2 in reading. In mathematics, domain-specific attitudinal variables showed a heterogeneous pattern of associations, while none of the instructional variables were statistically significant. Male students exhibited lower odds of reaching baseline proficiency in reading but higher odds in mathematics compared to female students.\u003c/p\u003e\u003ch2\u003eConclusions.\u003c/h2\u003e \u003cp\u003eOverall, the results highlight the cumulative nature of learning and reveal substantial learning gaps affecting students from disadvantaged contexts. They underscore the challenges faced by the Peruvian education system in ensuring that students who fall behind in the early stages of schooling develop the mathematical and reading skills required for full participation in society. The findings point to the importance of early, targeted educational policies aimed at supporting lagging students, particularly those from economically disadvantaged backgrounds.\u003c/p\u003e","manuscriptTitle":"Prior achievement, socioeconomic status, and baseline proficiency in PISA 2022: Evidence from linked national and international large-scale assessment data in Peru","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 09:22:05","doi":"10.21203/rs.3.rs-8744794/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a88b351d-d2bd-41b2-bd64-7418d30f27d0","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T12:55:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 09:22:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8744794","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8744794","identity":"rs-8744794","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}