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09 SES 02 C: Motivation, Family Background and Student Self-Perceptions
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09. Assessment, Evaluation, Testing and Measurement
Paper Relations between Personality, Socio-Emotional Skills, and Argumentation: An Ecological Interpretation University of Auckland, New Zealand Presenting Author:Title: Relations between Personality, Socio-Emotional Skills, and Argumentation: An Ecological Interpretation This study investigates how personality traits and socio-emotional (SE) skills relate to primary students’ written argumentation in a structured, digitally mediated classroom assessment context. Argumentation—particularly collaborative reasoning around socio-scientific dilemmas—is widely recognised as a form of 21st-century learning that is cognitively, socially, and emotionally demanding. While substantial research has examined cognitive and language demands in classroom argumentation, comparatively less attention has been given to learner characteristics (e.g., personality traits and specific SE skills) as explicit explanatory factors for observed variation in argumentation quality in K–12 classrooms. In parallel, argumentation research often describes tasks and topic resources in details, yet seldom examines features of topics as a source of variability in argumentation quality, despite evidence from reading and writing research that topic familiarity, background knowledge, agency, and identity relevance can shape performance. Context and purpose. The present study draws on baseline data from an intervention project in an experimental primary school in China where teachers integrated written argumentation into course design using an online Argumentation Tool (AT). Students produced written arguments within a shared online document, supported by curated texts and an evidence sheet, under the joint guidance of a subject specialist teacher and the class Banzhuren (homeroom teacher). The baseline analysis focuses on person–context relations: whether stable dispositions (Big Five personality traits) and SE skills predict argumentation outcomes, and whether contextual affordances linked to topic account for substantial differences in performance. Research questions. Theoretical framework. We interpret findings through an ecological perspective (Bronfenbrenner,1995) combined with concepts of affordances and a contextualised view of reading and writing performance. From this stance, personality traits and SE skills are treated as latent individual resources within classroom microsystems, whereas task structures, topic resources, peer visibility in the shared online document, and teacher orchestration provide activation conditions that may amplify or suppress the expression of these resources. The two topics were intentionally designed to be parallel in structure and use of AT but differed in experiential grounding and familiarity: a climate-change–related institutional dilemma (Green topic) and a culturally embedded topic linked to students’ everyday family and peer practices (Game topic). We hypothesise that such topic-linked affordances—particularly familiarity and accessibility of lived experiences—may differentially support multi-voiced argumentation even under comparable instructional structures. International dimension. Although the data come from a Chinese school, the study addresses internationally shared issues central to NW09: Relating Achievement Results to Variables on Individual Level. We examine how learner-level socio-emotional skills and personality traits relate to performance in an assessment of written argumentation quality, contributing to the interpretation of achievement results at the individual level. Many education systems—including European contexts—emphasise transversal competencies, dialogic teaching, and SE development; yet performance on complex writing assessments can vary widely depending on task or topic characteristics. By providing cross-cultural evidence and an ecological account of person–context relations, this study contributes to international debates on how to model contextual influences when interpreting assessment outcomes and designing classroom supports for argumentation. Methodology, Methods, Research Instruments or Sources Used Participants and design. Participants were 160 Grade Five students (49% girls; aged 10–12) from four parallel classes in an experimental primary school in China. The overall project was quasi-experimental; however, this paper reports a baseline phase (first three days) in which students engaged with AT-supported argumentation across two topics. Two classes worked with the Green topic and two with the Game topic, using the same three-day lesson structure (Day 1 topic introduction and questionnaires; Day 2 dialogic instruction and discussion; Day 3 AT writing task). Teachers were trained in advance and used shared materials to reduce variation in lesson structure. Measures. 1. Personality traits were measured on Day 1 with the Chinese Big Five Personality Inventory (CBF-PI-B; Dai, 2015), a 40-item scale assessing Neuroticism, Conscientiousness, Agreeableness, Openness, and Extraversion (6-point Likert). Internal consistency in this sample was acceptable (α = .681–.80 across subscales). 2. Socio-emotional (SE) skills were measured on Day 1 using a 40-item scale covering empathy, perspective-taking, sociability, and self-regulation (5-point Likert). The instrument was adapted from a New Zealand SE questionnaire, translated via forward–backward procedures and expert review. Internal consistency was satisfactory (α = .71–.81). CFA indices supported construct validity (CFI = .95, TLI = .93, RMSEA = [.009, .066], SRMR = .052). 3. Argumentation outcomes were collected on Day 3 through students’ written arguments in AT. Texts were exported and coded on three indicators: (a) argumentation profile (integrated vs single/dual, based on a dialogic profile framework), (b) evidence use (any vs none), and (c) length of writing (word count). Coding and reliability. Coding schemes for profile and evidence were adapted from prior classroom argumentation research. Copilot was used only during development to refine code descriptions; all categories and decision rules were set by researchers. Two human coders independently coded all scripts; agreement was 85% (Cohen’s κ = .72), with disagreements resolved by discussion. Analysis. One-way ANOVAs checked between-class differences in personality and SE skills (ns), allowing pooled analyses. Pearson correlations examined relations among personality and SE skills and among argumentation indicators. Hierarchical logistic regressions tested predictors of profile and evidence use: Model 1 (Big Five), Model 2 (Big Five + SE), Model 3 (+ Topic). Multiple linear regression tested predictors of word count. Conclusions, Expected Outcomes or Findings This baseline study produced three main findings. First, personality traits were strongly and coherently associated with SE skills, suggesting an underlying pattern of “responsible sociability” linking dispositional orientations and self-reported socio-emotional competencies. Second, contrary to common assumptions that SE skills directly support argumentation quality, neither personality traits nor SE skills robustly predicted students’ written argumentation outcomes (profile, evidence use, or word count) in pooled analyses. Third, topic emerged as a substantial contextual predictor of argumentation profiles: students writing about the culturally familiar Game topic were more likely to produce dual or integrated profiles than those writing about the Green topic, despite comparable instructional structures and curated resources. In regression models, Extraversion showed a negative association with advanced profiles (and with evidence use), while Openness showed a small positive association with evidence use; however, these effects were modest relative to topic effects. Interpreted ecologically, the findings suggest that contextual affordances linked to topic—including familiarity, experiential grounding, and the ease of mobilising shared everyday experiences—may account for more variance in dialogic quality than stable individual dispositions within structured writing tasks. The results highlight the importance of modelling person–context relations when interpreting achievement results in complex competencies such as written argumentation, especially in digitally mediated settings where task demands (e.g., typing skills, visibility of peers’ texts) may interact with learner resources. For assessment and instructional design, the study implies that topic selection and contextual scaffolds may be critical levers for enabling multi-voiced argumentation, and that generic SE measures may be insufficiently sensitive to domain-specific socio-emotional functioning required for written argumentation. References Alexander, R. (2017). Towards dialogic teaching: Rethinking classroom talk (5th ed.). Dialogos. Bronfenbrenner, U. (1995). Developmental ecology through space and time: A future perspective. In P. Moen, G. H. Elder, Jr., & K. Lüscher (Eds.), Examining lives in context: Perspectives on the ecology of human development (pp. 619–647). American Psychological Association. Brown, A. L. (1997). Transforming schools into communities of thinking and learning about serious matters. American Psychologist, 52(4), 399–413. https://doi.org/10.1037/0003-066X.52.4.399 Connor, C. M. D. (2016). A lattice model of the development of reading comprehension. Child Development Perspectives, 10(2), 92–96. PMC Costa, P. T., Jr., & McCrae, R. R. (1992). Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO-FFI) professional manual. Psychological Assessment Resources. Gu, L. M., Chen, J., & Li, J. C. (2015). The leadership of “banzhuren” in Chinese school: Based on the sample survey in Changzhou city of China. Journal of Education and Human Development, 4(4), 102–114. http://dx.doi.org/10.15640/jehd.v4n4a13 Hadley, G., & Boon, A. (2022). Critical Thinking (1st ed.). Routledge. https://doi.org/10.4324/9780429059865 Hodkinson, H., & Hodkinson, P. (2004). Rethinking the concept of community of practice in relation to schoolteachers’ workplace learning. International Journal of Training and Development. https://doi.org/10.1111/j.1360-3736.2004.00193. John, O. P., & Srivastava, S. (1999). The Big Five trait taxonomy: History, measurement, and theoretical perspectives. In L. A. Pervin & O. P. John (Eds.), Handbook of personality: Theory and research (2nd ed., pp. 102–138). Guilford Press. Li, J., & Chen, J. (2013). Banzhuren and classrooming: Democracy in the Chinese classroom. International Journal of Progressive Education, 9(3), 91–106. Kuhn, D., & Crowell, A. (2011). Dialogic argumentation as a vehicle for developing young adolescents’ thinking. Psychological Science, 22, 545–552. https://doi.org/10.1177/0956797611402512. McNaughton, S., Rosedale, N., Jesson, R. N., Hoda, R., & Teng, L. S. (2018). How digital environments in schools might be used to boost social skills: Developing a conditional augmentation hypothesis. Computers & Education, 126, 311–323. https://doi.org/10.1016/j.compedu.2018.07.018 McNaughton, S., Rosedale, N., Zhu, T., Teng, L. S., Jesson, R., Oldehaver, J., Hoda, R., & Williamson, R. (2023). A school-wide digital programme has context specific impacts on self-regulation but not social skills. E-Learning and Digital Media. https://doi.org/10.1177/204275302311562 82 McNaughton, S., Zhu, T., Rosedale, N., Oldehaver, J., Jesson, R., & Greenleaf, C. (2019). Critical Perspective Taking: Promoting and Assessing Online Written Argumentation for Dialogic Focus. Studia Paedagogica, 24(4), 119. https://doi.org/10.5817/SP2019-4-6 Wenger, E. (1998) Communities of Practice-A breif introduction. Communities of Practice. 09. Assessment, Evaluation, Testing and Measurement
Paper Set up for Success? The Influence of Parent-Student Discussions of Education on Cognitive and Non-Cognitive Outcomes among Swedish Youth University of Gothenburg, Sweden Presenting Author:Parents are the first influences on their children, and as such their behaviour and communication with children has the power to potentially shape children’s attitudes across multiple domains. The aim of this paper is to investigate how parental support for education in middle school relates to student motivation and language arts achievement and socio-emotional wellbeing of Swedish youth. The importance of parental engagement in education has been well studied in the literature (e.g. Boonk et al., 2018) and it is thought to bolster social, emotional and academic growth in their students (e.g. Green et al., 2007). In a study of high school students in nine countries (China, Colombia, Italy, Jordan, Kenya, the Philippines, Sweden, Thailand, and the USA), Al-Hassan et al. (2024) found that parental support for education influenced students’ academic identity but not their academic performance. Plunkett et al. (2009) modelled the contribution of various parental support elements through student academic engagement and on to achievement amongst immigrant background youth in the USA. Schoolwork monitoring, academic advice, and schoolwork help, particularly from mothers were found to positively impact students’ academic achievement. Discussions between parents and children related to schooling has previously shown a positive association with academic outcomes (e.g. Gordon & Cui, 2012) and reduced problematic behaviour such as truancy (e.g. McNeal, 2012). The relationship between parental communication with children and achievement varies between both countries, and socioeconomic groups (Park, 2008). Understanding the role of parental communication in shaping children’s outcomes is not only a concern for educational scientists, as previous meta-analyses have shown that parental communication with their children has a significant relationship with outcomes across various health concerns (see Grey et al., 2022). As Park (2008) asserts, communication between parents and children can be viewed as a form of enacted social capital. One of the three forms of capital identified by Bourdieu (1986), social capital is best understood as the social positioning achieved by whom one knows and interacts with in the social space. Social capital begins with the immediate network, the family, and is gained and reproduced as an individual interacts with society around them. The function of the education system as an engine of social reproduction (as expanded upon by Bourdieu, 1998) is in this study questioned by examining the relationship between social capital in the form of parent-student behaviours and outcomes indicative of embodied cultural and social capital, that is academic achievement and contentment in the social space of the school. As such, this study investigates the following research questions:
Methodology, Methods, Research Instruments or Sources Used This study applies structural equation modelling to the analysis of data from the Evaluation Through Follow-up (UGU) study. UGU is a cohort-sequential, longitudinal database which combines survey-data with register-data to make the largest educational database in Sweden. UGU contains nationally representative panels for eleven birth-cohorts (1948, 1953, 1967, 1972, 1977, 1982, 1987, 1992, 1998, 2004, 2010), which sampled 10% of youth born in each cohort who were resident in Sweden during grade 3 (see Härnqvist, 2000), and tracks the educational careers of Swedish youth from early grades in compulsory school to adulthood. The 2004 UGU birth cohort is selected for this study, and the sample includes 9773 individuals. The sample was 51% male, 79% Swedish born, and 50% had 2 or more years of higher education. The sample was drawn in 2014 when the students were in Grade 3 (SCB, 2018). The data used in this analysis was gathered at two timepoints, Grade 6 and Grade 9. These Grades are the end of the middle and upper phase of compulsory education in Sweden respectively. The data used in the study comes from the parent questionnaire in Grade 6, the student questionnaires in Grades 6 and 9, and register and school administrative data. Latent variables for motivation, student engagement with parents, and satisfaction with school at the two timepoints are identified from items in the Grade 6 and Grade 9 student questionnaire, and a latent factor of parental-child educational discussions is identified from items in the parental questionnaire administered in Grade 6. Socio-demographic background is controlled for in the analysis using three variables which are gathered though register data: student sex, immigration background, and parental education level (used as a proxy for socioeconomic status). Finally, student achievement in Swedish in Grade 6 and 9 comes from school administrative data. There are three stages to the modelling procedure: Model A examines the predictive relationship between parental-child educational discussions, student engagement with parents, and motivation on the cognitive and non-cognitive outcomes in Grade 6; Model B examines student engagement with parents, and motivation on the cognitive and non-cognitive outcomes in Grade 9; and Model C combines Model A and B, with variables in grade 9 regressed on their counterparts in grade 6. Separate structural equation models are specified using the cognitive (Swedish language grades) and non-cognitive outcomes (satisfaction with school). Conclusions, Expected Outcomes or Findings Preliminary results suggest that parents can set a climate for school success in middle school aged Swedish youth without directly influencing outcomes. Parental-child educational discussions in grade 6 are positively associated with student engagement with parents at both Grade 6 and 9, but that such behaviour does not predict cognitive or non-cognitive outcomes. For both set of models, student engagement with their parents and student motivation were indicative predict cognitive or non-cognitive outcomes at Grade 6 and 9. When examining the sociodemographic control variables, sex, migration background and SES were all predictors of Swedish performance, whereas only sex predicted students’ contentment with school. References Al-Hassan, S. M., Duell, N., Lansford, J. E., Dodge, K. A., Gurdal, S., Liu, Q., Long, Q., Oburu, P., Pastorelli, C., Skinner, A. T., Sorbring, E., Steinberg, L., Tapanya, S., Tirado, L. M. U., Yotanyamaneewong, S., Alampay, L. P., Bacchini, D., Bornstein, M. H., Chang, L., . . . Di Giunta, L. (2024). Parents’ learning support and school attitudes in relation to adolescent academic identity and school performance in nine countries. European Journal of Psychology of Education, 39(4), 3841-3866. https://doi.org/https://doi.org/10.1007/s10212-024-00827-4 Boonk, L., Gijselaers, H. J. M., Ritzen, H., & Brand-Gruwel, S. (2018). A review of the relationship between parental involvement indicators and academic achievement. Educational Research Review, 24, 10-30. https://doi.org/https://doi.org/10.1016/j.edurev.2018.02.001 Bourdieu, P. (1986). The forms of capital. In J. E. Richardson (Ed.), Handbook of theory and research in the sociology of education (pp. 241-258). Greenwood Press. Bourdieu, P. (1998). Practical reason: on the theory of action. Oxford : Polity. Gordon, M. S., & Cui, M. (2012). The Effect of School-Specific Parenting Processes on Academic Achievement in Adolescence and Young Adulthood. Family Relations, 61(5), 728-741. https://doi.org/https://doi.org/10.1111/j.1741-3729.2012.00733.x Green, C. L., Walker, J. M. T., Hoover-Dempsey, K. V., & Sandler, H. M. (2007). Parents' Motivations for Involvement in Children's Education: An Empirical Test of a Theoretical Model of Parental Involvement. Journal of Educational Psychology, 99(3), 532-544. https://doi.org/10.1037/0022-0663.99.3.532 Grey, E. B., Atkinson, L., Chater, A., Gahagan, A., Tran, A., & Gillison, F. B. (2022). A systematic review of the evidence on the effect of parental communication about health and health behaviours on children's health and wellbeing. Preventive Medicine, 159, 107043. https://doi.org/https://doi.org/10.1016/j.ypmed.2022.107043 Härnqvist, K. (2000). Evaluation through follow-up. A longitudinal program for studying education and career development. In C.-G. Janson (Ed.), Seven Swedish longitudinal studies in behavioral science (pp. 76-114). Forskningsrådsnämnden. McNeal, R. B. (2012). Checking In or Checking Out? Investigating the Parent Involvement Reactive Hypothesis. The Journal of Educational Research, 105(2), 79-89. https://doi.org/10.1080/00220671.2010.519410 Park, H. (2008). The Varied Educational Effects of Parent‐Child Communication: A Comparative Study of Fourteen Countries. Comparative Education Review, 52(2), 219-243. https://doi.org/10.1086/528763 Plunkett, S. W., Behnke, A. O., Sands, T., & Choi, B. Y. (2009). Adolescents' Reports of Parental Engagement and Academic Achievement in Immigrant Families. Journal of Youth and Adolescence, 38(2), 257-268. https://doi.org/https://doi.org/10.1007/s10964-008-9325-4 Statistika Centralbyrån (SCB). (2018). Teknisk Rapport. En beskrivning av genomförande och metoder. Enkäter skolår 6 till UGU 2017 https://www4.gu.se/compeat/FUR/UGU/Rapporter/UGU2004_SCB_2018_Teknisk%20rapport_elever_ak6.pdf 09. Assessment, Evaluation, Testing and Measurement
Paper Skills to Adapt: Social and Emotional Drivers of Problem-Solving Across Ages 1: Bogazici University, Turkey (Türkiye); 2: Pontifical Comillas University, Spain; 3: GESIS - Leibniz-Institute for the Social Sciences, Germany Presenting Author:Due to the dynamic nature of modern life, enhancing adaptive problem-solving (APS) skills has become essential. APS involves using cognitive, metacognitive and non-cognitive strategies to define problems, gather relevant information, and apply solutions effectively by engaging digital, social, and physical tools in evolving contexts (Greiff et al., 2021). Since one of the most crucial skills for successful integration into society is APS, researchers are interested in the factors predicting APS. Previous research suggests that social and emotional skills (SES) — whose sub-dimensions include agreeableness, conscientiousness, emotional stability, extraversion, and openness to experience, corresponding to the Big Five framework (Kankaraš & Suarez-Alvarez, OECD, 2016; 2019) — are related to individuals’ adaptive problem-solving skills (OECD, 2021; OECD, 2024; Sullivan et al., 2010). For example, according to Greiff’s framework (2017), key elements for APS such as adaptability to uncertainty, intellectual curiosity, and stress management establish links with Social-Emotional Skills sub-dimensions like openness to experience, emotional stability, and conscientiousness. In addition to individual-level factors, country-level indicators such as Human Development Index (HDI), Government Expenditure on Education (GEE), and ICT Development Index (IDI) are important because they reflect structural conditions that support opportunities for developing APS. Investment in education, which is a key component of HDI and GEE, enhances cognitive skills and makes society more resourceful (problem solver) (Nunes 2003; Singh et al., 2025). Additionally, IDI, which measures digital technology (ICT) through its components access, use, and skills, represents an important structural part of modern society, holds a new perspective on the measurement of information development and enhances adaptive problem solving (International Telecommunication Union, 2011; Dobrota et al., 2012). High performance across these three indicators suggests the presence of the educational and technological infrastructure that enable individuals to develop strategies and adapt to complex problems. Furthermore, the factors related to relationships between SES and APS might differ across various age-cohorts groups and geographical regions. For the age-cohort groups, individuals at different stages of the life course do not solve problems same way (Feraco & Meneghetti, 2023). Problem-solving strategies and skills evolve as individuals accumulate life experience and cognitive maturity. With regard to geographical regions, individuals living in different geographical regions may approach problems and solution strategies differently due to differences in levels of development, income resources, educational attainment, and access to technology, all of which shape the contexts in which problem-solving takes place (World Bank, 2024). In particular, Europe may show distinct patterns compared to other regions However, there is no study in the literature investigating whether the relationship between APS and related factors (individual- and country-level) changes across age-cohorts groups and geographical regions. To address this gap, the current study aims to examine the extent to which individual-level (Social and Emotional Skills — SES) and country-level indicators (HDI, GEE, and IDI) predict APS, and whether these relationships vary across different age-cohorts groups and geographical regions in the second cycle of the Programme for the International Assessment of Adult Competencies (PIAAC). To address this aim, the present study formulates the following research question:
Methodology, Methods, Research Instruments or Sources Used Participants The study uses data from 2nd cycle of the PIAAC. Dataset includes 143564 participants aged 16 to 65, grouped into five age-cohorts groups (16-24, 25-34, 35-44, 45-54, and 55–65) based on OECD. The majority of participants belong to the 55–65 age-cohort group (23.9%), followed by 45–54 (21.2%), and 35–44 (21.1%). The 25–34 age-cohort group accounts for 19.0% of sample, whereas 16–24 age-cohort group represents smallest proportion of sample (14.9%). Also, the participants are from 25 countries and seven geographical regions based on The Geoscheme of the United Nations. The majority of respondents are from Europe (75.6%). Within Europe, Western Europe constitutes largest population (26.1%), followed by Northern Europe (21.2%), Eastern Europe (18.0%), and Southern Europe (10.3%). Remaining participants are from non-European regions, with respondents from Americas (12.2%), Asia (8.2%), and Oceania (4.0%). Instruments The study uses two main instruments of the PIAAC 2nd cycle dataset. In PIAAC Cycle 2, SES were measured through Background Questionnaire (BQ). The most widely used and best-validated model of personality traits is called ‘Big Five’ model, in which a range of traits is subsumed under five higher order dimensions: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Emotional Stability. For BQ, OECD working group on SES suggested implementing an established and internationally validated 30-item short version of Big Five Inventory (BFI-2-S) developed by Soto and John (2017) to measure SES. APS was measured using the Cognitive Module of Survey of Adult Skills and assessed as a new domain in the direct assessment component of PIAAC Cycle 2. Based on the framework by Greiff et al. (2017), unlike traditional tests, this module uses a computer-based and scenario-based approach where participants interact with a digital environment to solve non-routine problems. Instrument is designed to evaluate how well individuals adapt their strategies and goals when faced with unexpected changes or new information. Data Analysis In line with research question, using multilevel regression, analysis will proceed in two steps. First, main analysis will examine extent to which individual-level indicators (SES) and country-level indicators (HDI, GEE, and IDI) predict APS. Second, analysis will examine whether these relationships differ across age-cohort groups and geographical regions, focusing on differences in predictors of APS across these groups. Sample weights and plausible values will be handled using Mplus. Sample weights adjust the data to be population-representative, and plausible values provide reliable estimates of proficiency through multiple imputed scores. Conclusions, Expected Outcomes or Findings It is expected that both individual- and country-level indicators will be associated with adaptive problem-solving skills in the second cycle of PIAAC. At the individual level, social and emotional skill dimensions—particularly conscientiousness, openness to experience, and emotional stability—are expected to show positive relationships with APS. At the country level, indicators such as Human Development Index (HDI), Government Expenditure on Education (GEE), and ICT Development Index (IDI) are expected to be related to APS, as they reflect national educational and technological conditions. Moreover, these relationships are expected to vary across age-cohort groups. For age-cohort groups It is expected that the relationships between social and emotional skills and adaptive problem-solving will vary across age groups, three working-age populations (16-24, 25-34, 35-44, 45-54, and 55–65), based on OECD data. Specifically, SES sub-dimensions such as conscientiousness, openness to experience, and emotional stability may be differently related to APS across age groups, as individuals may have these social-emotional characteristics to different extents at different ages when solving complex problems. For geographical regions It is expected that strength and nature of the relationship between SES and APS will vary across geographical regions due to differences in socio-economic development, educational systems, and technological contexts. In European regions, especially Northern Europe, where educational structures, labor markets, and digital infrastructures tend to be more standardized and supportive of autonomous learning and problem-solving, SES may show a stronger association with APS. These contexts may provide individuals with more opportunities to translate social-emotional resources into effective adaptive strategies when facing complex and novel problems. In contrast, other regions, where educational and occupational systems may place relatively greater emphasis on structured instruction, cognitive performance, and conformity to established norms, the contribution of SES to APS may operate differently, with adaptive problem-solving being more strongly driven by cognitive skills or contextual constraints. References Ajayi, K., Das, S., Delavallade, C., Ketema, T., & Rouanet, L. (2022). Gender differences in socio-emotional skills and economic outcomes. World Bank Policy Research Working Paper, 10197. Dobrota, M., Jeremic, V., & Markovic, A. (2012). A new perspective on the ICT Development Index. Information Development, 28(4), 271-280. Feraco, T., & Meneghetti, C. (2023). Social, emotional, and behavioral skills: Age and gender differences at 12 to 19 years old. Journal of Intelligence, 11(6), 118. Greiff, S. et al. (2017), “Adaptive problem solving: Moving towards a new assessment domain in the second cycle of PIAAC”, OECD Education Working Papers, No. 156, OECD Publishing, Paris, https://doi.org/10.1787/90fde2f4-en. Greiff, S. et al. (2021), “PIAAC Cycle 2 assessment framework: Adaptive problem solving”, in The Assessment Frameworks for Cycle 2 of the Programme for the International Assessment of Adult Competencies, OECD Publishing, Paris, https://doi.org/10.1787/3a14db8b-en. Kankaraš, M. and J. Suarez-Alvarez (2019), “Assessment framework of the OECD Study on Social and Emotional Skills”, OECD Education Working Papers, No. 207, OECD Publishing, Paris, https://doi.org/10.1787/5007adef-en. Nunes, A. B. (2003). Government expenditure on education, economic growth and long waves: the case of Portugal. Paedagogica historica, 39(5), 559-581. OECD. (2016). Skills matter: Further results from the Survey of Adult Skills. Paris: OECD Publishing. https://doi.org/10.1787/9789264258051-en OECD (2021). Beyond academic learning: First results from the survey of social and emotional skills. OECD Publishing. https:// doi.org/10.1787/2ed1b046-enOECD. (2021). OECD survey on social and emotional skills technical report. OECD Publishing. https://doi. org/10.1787/3f50b556-en OECD (2024). Nurturing Social and Emotional Learning Across the Globe: Findings from the OECD Survey on Social and Emotional Skills 2023. OECD Publishing, Paris, https://doi. org/10.1787/32b647d0-en. Rammstedt, B., Vogel, V., Roth, M. et al. (2026). Unpacking the personality–cognitive ability link: a cross-national facet-level analysis of the Big Five. Large-scale Assess Educ 14, 6. https://doi.org/10.1186/s40536-026-00281-2 Singh, K., Cheemalapati, S., RamiReddy, S. R., Kurian, G., Muzumdar, P., & Muley, A. (2025). Determinants of Human Development Index (HDI): A Regression Analysis of Economic and Social Indicators. arXiv preprint arXiv:2502.00006. Soto, C. & John, O. (2017). Short and extra-short forms of the Big Five Inventory-2: The BFI-2-S and BFI-2-XS. http://dx.doi.org/10.1016/j.jrp.2017.02.004. Sullivan, K. T., Pasch, L. A., Johnson, M. D., & Bradbury, T. N. (2010). Social support, problem solving, and the longitudinal course of newlywed marriage. Journal of personality and social psychology, 98(4), 631 The World Bank. (2024). Understanding country income: World Bank Group income classifications for FY26 (July 1, 2025–June 30, 2026). https://datahelpdesk.worldbank.org/knowledgebase/articles/906519 | ||