Search Results (29)

This study examined the processes linking cognitively activating mathematics instruction to self-efficacy via metacognitive self-regulation. A sequential mediation model was tested whereby cognitive-activating instruction operationalised as mathematical argumentation was specified as being associated with metacognitive self-regulation, which, in turn, was estimated to be associated with mathematics performance and mathematics self-efficacy. Data from 6403 adolescents (49.76% females) from Greece’s PISA 2022 dataset were utilised. Latent variables were constructed from the student questionnaire items to capture cognitive activation, metacognitive self-regulation, and self-efficacy. Structural equation modelling showed that cognitive activation was positively associated with metacognitive self-regulation, which, in turn, was positively associated with mathematics self-efficacy. Sequential mediation analysis indicated that cognitive-activating instruction was also directly linked to mathematics self-efficacy and indirectly through mathematics performance, supporting the role of performance as a source of mastery experiences. In brief, the findings imply that engaging students in cognitively activating activities is associated with better metacognitive self-regulation skills and higher mathematics self-efficacy, partly through mathematics performance, which is consistent with the mastery-experiences account. Full article

Aligning information technology (IT) workforce demands with educational assessments is essential for bridging skills gaps; yet, no prior corpus maps IT task reasoning to Programme for International Student Assessment (PISA) proficiency levels. This paper introduces a large language model (LLM)-powered framework aligning IT competencies with PISA 2022 and the OECD (Organisation for Economic Co-operation and Development) Learning Compass 2030, drawing on O*NET v30.2 (Occupational Information Network), ESCO (European Skills, Competences, Qualifications, and Occupations) v1.2.1, PISA descriptors and OECD definitions. The framework operates in two stages: Stage 1 aligns 562 IT task statements with minimum PISA 2022 proficiency levels via LLM annotation and cross-model validation; and Stage 2 extends this mapping to the OECD Learning Compass 2030 through the semantic clustering of task embeddings and a bidirectional gap analysis of 95 ESCO transversal skills. Using Gemini 2.5 Flash, 562 tasks are annotated with minimum PISA levels across Mathematical, Reading, and Science literacy (first stage). Annotation reliability is assessed through a five-model cross-validation against a blind human domain expert (treated as a reference benchmark, not a gold standard) on a stratified 100-task sample (17.8% of the corpus), with agreement ranging from fair (Gemini 2.5 Flash, κ = 0.29) to moderate (Claude Haiku 4.5, κ = 0.50; LLaMA 3.3 70B, κ = 0.44). A bias-correction sensitivity analysis confirms that distributional findings remain stable after accounting for the primary annotator’s systematic overestimation, and OLS-calibrated alignment against O*NET ability ratings provides directional plausibility support. Validated tasks are embedded and clustered into 25 technical profiles via K-Means, each classified against OECD dimensions. The framework is extended to 95 ESCO transversal skills in 24 clusters. Bidirectional analysis reveals that, while every PISA proficiency level is engaged by at least one transversal cluster, 33% of these clusters, covering creative, ethical, social–emotional, and dispositional competencies, fall entirely outside PISA’s cognitive scope. This boundary mapping identifies where the PISA-based alignment is valid and where complementary tools are required for a full readiness assessment. Full article

The phenomenon of sustainability, which has been identified in the context of prominence over the past century, has the potential to significantly influence education and its components, as it has been successfully established in other areas of human activity. One of these components is the managerial aspect of education, which directly impacts the quality of education. In this respect, sustainable school management will probably be seen as one of the critical factors associated with the performance of the school of the twenty-first century. Sustainable school management does not simply imply the school’s capacity to meet its immediate needs but also its capacity to function in an uninterrupted and effective manner over the long term to achieve its goals. Some of the aspects included in this regard are planned use of resources, the continued effectiveness of decision processes, and the development of a school climate that promotes cooperation among teachers for school improvement. The main goal of this study is to develop the Sustainable School Management Index (SSMI), a measure of schools’ long-term organizational sustainability management capacity, using PISA 2022 school principal survey data. To support this goal, the study pursues two specific objectives: (1) to identify the main managerial factors associated with the SSMI, and (2) to examine how these factors relate to sustainable school management across countries. Using a quantitative correlational survey design, the study relies on the PISA 2022 school questionnaire data collected from 80 countries and economies. After data cleaning and missing data management, the analysis was conducted on a sample of 21,629 schools and 431 variables. To explore the factors of the SSMI, an ensemble learning approach based on decision trees was developed. The model performance was evaluated through cross-validation, and the variable importance was measured through a permutation test. Moreover, to describe the sustainable management school profiles, a cluster analysis was carried out based on the index factors, and a four-cluster classification of schools was identified. To validate the machine learning findings and to understand the direction of the relationships, a linear regression analysis technique was also used. The SSMI is a multidimensional composite index, which is based on six dimensions, informed by theory: positive school climate, institutional structure and support, resource adequacy, planning and technology preparation, management independence, and teacher collaboration. In the first predictive model, school leadership and institutional pressure have been considered as independent variables, explaining the variance in SSMI. According to the results, the institutional pressure factor shows the most pronounced negative correlation with the SSMI; meanwhile, the school leadership variable shows a smaller but still positive correlation with the same index. Moreover, according to PCA outcomes, the structure of the index as a multidimensional composite measure seems to be consistent. Therefore, the SSMI created during this research can be seen as a metric for the evaluation of schools concerning their sustainable management ability. Full article

Background/Objectives: Bullying victimization is a significant threat to adolescents’ psychological well-being and academic functioning. However, most prior research has relied on variable-centered approaches that may obscure meaningful heterogeneity in students’ victimization experiences. The present study aimed to identify latent subtypes of bullying victimization among adolescents in Saudi Arabia using nationally representative PISA 2022 data and to examine whether the structure and prevalence of these subtypes differed across gender. Methods: Data were drawn from the Saudi Arabian sample of the Programme for International Student Assessment (PISA) 2022 and included 6709 adolescents. Bullying victimization was assessed using 11 categorical indicators representing different forms of victimization. Weighted descriptive analyses were first conducted to estimate the prevalence of specific bullying behaviors. Multigroup latent class analysis (LCA) was then applied separately across gender to identify victimization profiles and evaluate measurement and structural invariance. Sequential invariance testing was used to determine whether the latent classes had equivalent meaning and prevalence across males and females. This study involved secondary analysis of an existing large-scale educational dataset and did not require trial registration. Results: Weighted descriptive estimates showed that the prevalence of specific bullying victimization experiences ranged from 7.5% to 24.3%, with boys reporting greater exposure than girls on most overt and coercive forms. Class enumeration supported a parsimonious three-class solution for both genders, reflecting low, moderate, and high victimization severity. Approximately 71–79% of students were classified in the low-risk group, 14–18% in the moderate-risk group, and 3–14% in the high-risk group. Measurement invariance testing supported full invariance of item-response probabilities across gender, indicating that the latent classes represented substantively comparable victimization patterns for males and females. In contrast, structural invariance was not supported, as males were more likely to belong to the high-victimization class, whereas females were more likely to be classified in the low-risk group. Conclusions: The findings indicate that gender differences in bullying victimization are attributable to differences in the level of exposure rather than differences in the underlying structure of victimization experiences. Bullying victimization appears to be concentrated within a relatively small but highly vulnerable subgroup of adolescents. These results support the importance of universal school-based anti-bullying policies and prevention initiatives, while also highlighting the need for targeted psychosocial support and protective interventions for students experiencing chronic or multiple forms of victimization. Full article

This study examines the increasing concern regarding teacher job satisfaction, which has a direct impact on retention, instructional quality, and student outcomes. Traditionally, teacher satisfaction has been evaluated through questionnaires, which present limitations in terms of data efficiency and analyses. In this study, machine learning techniques were applied to data from the PISA 2022 teacher questionnaire in Morocco (N = 2998 lower-secondary teachers). Two multiclass classification targets were defined: overall job satisfaction (SATJOB_class) and satisfaction with the teaching profession (SATTEACH_class), each categorised into three balanced classes: low (<−0.5), medium (−0.5 to 0.5), and high (>0.5) classes. The methodology comprised four key stages. Initially, comprehensive pre-processing was conducted to address missing values, retaining features with fewer than 300 missing entries and applying mode imputation. Subsequently, nine classifiers, including logistic regression, K-nearest neighbours, multinomial naïve Bayes, support vector machine, decision tree, random forest, XGBoost, AdaBoost, and a feed-forward Artificial Neural Network, were evaluated using identical train/test splits and hyperparameter tuning. Third, the model performance was assessed using accuracy, precision, recall, and F1-score. Finally, the feature importance was derived from tree-based and permutation methods. The results indicated that XGBoost outperformed the other models for SATJOB_class with an accuracy (0.61), precision (0.62), recall (0.61), and F1-score (0.61), followed by Random Forest (accuracy = 0.59), Logistic Regression (accuracy = 0.59), and AdaBoost (accuracy = 0.59). For SATTEACH_class, Random Forest led with accuracy (0.59), followed closely by XGBoost (0.58), ANN (0.57), and AdaBoost (0.56). Key predictors of teacher job satisfaction included workload-related variables and school-environment factors, which consistently emerged as the most important features across the best-performing models. The methodology and open-source pipeline provide a reproducible framework for evidence-based interventions to improve teacher retention and instructional quality, offering valuable insights for policymakers and educational administrators. Full article

The rapid integration of artificial intelligence (AI) into educational systems is transforming how student performance is analysed and how educational policies are informed by large-scale data. Within this context, machine learning techniques are increasingly used to identify patterns associated with academic success and educational inequality. However, the use of predictive algorithms in education also raises important questions regarding transparency, fairness, and potential algorithmic bias. This study examines the predictive performance and fairness implications of machine learning models used to identify academically resilient students using data from the Programme for International Student Assessment (PISA) 2022. The analysis is based on a dataset containing more than 600,000 student observations across multiple national education systems. Academic resilience is operationalised following the OECD framework, identifying students who belong to the lowest quartile of the socioeconomic status index (ESCS) within their country while simultaneously achieving mathematics performance in the top quartile (PV1MATH). A predictive framework incorporating six supervised learning algorithms—Logistic Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost—was implemented. The modelling pipeline includes data preprocessing, missing value imputation, class imbalance correction using SMOTE, and model evaluation through multiple classification metrics, including accuracy, F1-score, and the area under the ROC curve (AUC). In addition, fairness diagnostics are conducted to examine potential disparities in prediction outcomes across gender groups, while feature importance analysis and SHAP-based explanations are used to interpret the contribution of key predictors. The results indicate that ensemble-based models achieve the highest predictive performance, particularly those based on gradient boosting techniques. At the same time, the analysis reveals that socioeconomic status, migration background, and school repetition constitute the most influential predictors of academic resilience. Although gender displays relatively low predictive importance, measurable differences in positive prediction rates across gender groups suggest the presence of potential algorithmic disparities. These findings highlight the importance of integrating fairness evaluation, transparency, and interpretability into educational data science workflows. The study contributes to ongoing discussions on the responsible use of artificial intelligence in education by emphasising the need for governance frameworks capable of ensuring that algorithmic systems support equity-oriented educational policies. Full article

This study develops and validates a three-level model of digital learning conditions that reflects the progression from ICT accessibility (“access”) to pedagogical use (“use”) and their influence on student learning outcomes (“impact”). Drawing on secondary analysis of the PISA 2022 ICT Familiarity Questionnaire and applying complex-sample regression together with the logic of structural equation modelling (SEM), the study examines how ICT resources, usage practices, and digital feedback (ICTFEED) interact and how they are associated with Lithuanian fifteen-year-olds’ achievement in mathematics, reading, and science. The three-level model includes: (1) ICT infrastructure—access to technology at home and at school and students’ perceived quality of technological resources; (2) ICT learning practices—use of digital tools in subject lessons, inquiry-based activities, and school-related work outside the classroom; and (3) digital feedback and its relationship with academic achievement. Results show that neither home nor school ICT availability predicts students’ experience of receiving digital feedback. The only significant infrastructure-level predictor is the perceived quality of school ICT resources (ICTQUAL). Digital feedback is most strongly predicted by ICT use in inquiry-based learning and by ICT-supported schoolwork outside the classroom, whereas ICT use in subject lessons has only a minimal effect. Across all domains, digital feedback is negatively associated with student achievement, even when ICT access, resource quality, learning-use variables, and digital leisure are controlled for. This pattern suggests that ICTFEED functions primarily as a compensatory mechanism, being more frequently used with lower-achieving students rather than serving as a direct enhancer of academic performance. The proposed three-level model offers a structured framework for interpreting students’ digital learning experiences and highlights the key components of school ICT ecosystems that shape digital assessment practices and learning outcomes. Full article

While the international influence of the Programme for International Student Assessment (PISA) on education policy debates is well recognized, the degree to which PISA findings drive actual policy reforms and classroom practices remain debated. Using PISA as a case, this article examines how educational research is translated into policy responses and practices in German federalism, focusing specifically on immigrant students—a key group within German education reform discourse. It analyzes the reflection of PISA findings from the 2000, 2018, and 2022 assessments on immigrant student performance in the resolutions of the Standing Conference of Ministers of Education and Cultural Affairs, the process of implementation by the federal states (Länder), and the effect on school-level practice. Framed by research knowledge mobilization theory, the article investigates the relationships among research production, mediation, and usage, clarifying the interplay between educational research, policy, and practice in Germany’s federal system. Historical analysis exposes consistent gaps between research-derived recommendations and binding, actionable change at both policy and practice levels, often due to challenges in developing evidence-based and consistently applied policy measures across the Länder. The article concludes with practical recommendations for improving the impact of interdisciplinary, policy-oriented research on policy and practice, considering the complexities of Germany’s federal governance. Full article

Prior studies have examined associations between early childhood education and care (ECEC) and cognitive performance in later adolescence. However, little is known about the role of non-cognitive skills development and truancy in this link. To address this gap, the current study investigates how non-cognitive skills and truancy mediate the link between ECEC and cognitive performance among 15-year-old students (N = 550,818), leveraging the Programme for International Student Assessment (PISA) 2022 dataset. Findings indicate that ECEC directly and positively influences non-cognitive skills development and cognitive performance. Non-cognitive skills development is negatively associated with truancy and positively influences cognitive performance. An inverse relationship was found between truancy and cognitive performance. Analyzing this relationship based on gender, it was observed that female students benefited more from ECEC compared to their male counterparts. These results imply that the provision of ECEC may reap substantial social equity benefits. Full article

Background: Engaging in physical activity (PA) is especially significant for adolescents, as this is a key developmental stage for establishing lifelong habits. While the physical, mental, and cognitive health benefits of PA are well-documented, less is known about its relationship with adolescents’ life satisfaction (LS). Most existing evidence often involves small sample sizes, focusing particularly on developed regions, and few studies use large-scale comparative data. Methods: This study examines the association between adolescents’ LS and PA using data from the 2022 Programme for International Student Assessment (PISA), the world’s largest comparative education survey of adolescents. Our analysis included 399,794 adolescents from 64 high- and middle-income countries and economies. We used three-level multilevel regressions. Results: We found that, after controlling for individual, family, and school factors, PA is positively and significantly associated with LS. This finding holds for the pooled sample and across the 64 countries analysed. For most countries, we did not find a significant gender moderator effect, suggesting that the positive association between PA and LS did not vary by gender. Conclusions: The findings suggest a global health promotion strategy to promote PA amongst adolescents as a normative developmental process necessary for their well-being and mental health. Full article

High-quality playing surfaces enhance player experience and safety while serving as an appealing setting for spectators. Natural turfgrass provides optimal conditions at the beginning of the playing season but faces challenges under increasing field usage. Turfgrasses with high wear tolerance and quick recovery capacity are crucial for maintaining surface quality under intensive wear. Bermudagrass is the most used species in warm climates but needs winter overseeding in the transition zone. In Mediterranean climates, tall fescue (Schedonorus arundinaceus (Schreb.) Dumort, formerly Festuca arundinacea) has emerged as a promising species due to its tolerance to heat, drought, and salinity, alongside traits like deep rooting, shade adaptation, and wear resistance. The trial was conducted at the CeRTES experimental station in Rottaia, Pisa, Italy. Twenty-seven tall fescue cultivars and three cultivars of perennial ryegrass (Lolium perenne L.) were hand-seeded on 3 November 2022, at a rate of 43 g m⁻². The experimental design consisted of plots measuring 4.5 m² arranged in a randomized complete block design with three replications. The objective of the study is to evaluate the performance of twenty-seven cultivars of tall fescue with the aim of using the species in soccer fields with a permanent stand approach, with no need to manage spring and fall transitions. The field study encompasses determinations referring to the establishment stage, the maintenance at low cutting height stage (20 mm) and the subsequent stage of soccer use under different seasonal conditions (autumn, winter, and spring). Results showed that certain fescue cultivars, notably ‘Essential’, ‘Eyecandy’, and ‘FAG3/19-20208B’, exhibited quick establishment and adaptation to low cutting height (20 mm), and performed similarly to the reference ryegrasses ‘Gianna’ and ‘Mercitwo’ in terms of wear tolerance and recovery capacity across the three seasons. Moreover, most of the tested tall fescue cultivars performed well at a 20 mm mowing height, maintaining satisfactory quality and density. Among these, ‘Eyecandy’ and ‘Foxhound’ displayed finer leaf textures, comparable to those of the reference ryegrass. Full article

Academic performance has become a consolidated indicator of a nation’s educational and social equity. Consequently, increasing attention has been paid to determining the factors associated with school performance, particularly in the case of students with extreme academic outcomes. The aim of this study is to identify and compare the factors related to the level of mathematical competence of Spanish students with low and high levels of achievement, based on data from the Spanish sample of PISA 2022 (n = 30,800). The results of the multilevel quantile regression analysis reveal that the social, economic, and cultural status of the students have a significant and positive effect on both groups. Other variables, such as gender, grade repetition, and length of pre-primary education, show differentiated effects depending on the level of competence. Moreover, school-related factors, such as school location and competition among centres, exhibit opposite effects. Finally, aspects such as school ownership, average class size, and the degree of curricular autonomy only have a significant impact on the mathematical competence of low-achieving students. These findings highlight the need for differentiated educational policies that address the specific needs of each group of students. Full article

Purpose: This study adopted a quantitative design to evaluate a new latent construct, “Parental Academic Commitment (PAC)”, that was composed of parental involvement (PARINVOL) and parents’ expectations of their children’s academic success (PAREXPT). Furthermore, we also explored how different perceptions of school leadership would impact parental academic commitment and student learning. More specifically, we compared how principal-perceived school leadership and teacher-perceived leadership would influence student achievement (SA) directly and indirectly through mediating parental academic commitment (PAC). Methods: To find answers, we utilized two Structural Equations Models—Multiple Indicators and Multiple Causes Analysis (SEM-MIMIC) to first confirm the psychometric properties of PAC, and then compared the two SEM models. Data from 202 principals, 4251 teachers, 10,291 parents, and 10,291 students in Hong Kong and Macao from PISA 2022 were utilized; both individual-level and school-level analyses were conducted. Results: Principal-rated and teacher-rated school leadership functioned differently in the 2 SEM models. Both ESCS (Economic, Social, and Cultural Status) and PAC were confirmed to be significant contributors to positive student outcomes. Full article

Enhancing mathematical achievement has been identified as a pivotal issue in school education, extending beyond mathematics education alone. However, research comprehensively examining the relationship between multiple affective variables and learning and mathematics achievement is limited. The present study examines the relationship between self-efficacy, mathematics anxiety, behavioural engagement, and mathematics achievement among students in Japan. Moreover, this study examines whether there are any differences in this relationship according to gender and socio-economic status (SES). A path analysis using the data from students in Japan (n = 5760) in the PISA 2022 dataset revealed that (1) self-efficacy for formal and applied mathematics was significantly negatively related to mathematics anxiety and significantly positively related to behavioural engagement and mathematics achievement, (2) self-efficacy for mathematical reasoning and 21st-century mathematics was found to be significantly negatively associated with mathematics anxiety and positively associated with behavioural engagement, and (3) while a negative correlation was observed between mathematics anxiety and behavioural engagement, a significant relationship was not identified between the two and mathematics achievement. Furthermore, the multiple-group structural equation modelling, with gender and SES as the grouping variable, demonstrated no differences in gender and SES in the relationship between self-efficacy, mathematics anxiety, behavioural engagement, and math achievement. Full article

School leadership plays a critical role in shaping student academic performance. Despite the UAE’s recognition as one of the leading nations globally for quality education, research on the impact of leadership practices on performance in international assessments like PISA remains scarce. This study explores the influence of school leadership on students’ performance in the UAE’s schools. The PISA 2022 UAE database containing data on 24,600 15-year-old students across 840 schools was used to assess mathematical literacy based on their ability to apply math concepts and their attitudes toward the subject. Insights into leadership practices were utilized using responses from school principals in the PISA 2022 school leaders’ questionnaire. The results demonstrate that leadership practices significantly influence student outcomes. Schools where leaders emphasize teacher accountability and professional development show improved mathematics performance, lower anxiety levels, and enhanced self-efficacy among students. Conversely, excessive focus on disciplinary measures or teaching skill improvements is associated with reduced student self-efficacy. These findings highlight the importance of adaptive leadership approaches that consider local educational contexts, balancing accountability and support to optimize both student performance and well-being. By refining leadership practices, schools can drive meaningful improvements in student success and better equip learners to thrive in global educational benchmarks. Full article