Υποστηρικτικά και ασφαλή σχολεία: Πολυεπίπεδη ανάλυση δεδομένων της διεθνούς έρευνας PISA 2022


Σταύρος Αϊβαλιώτης
https://orcid.org/0009-0007-2807-9055
Αναστάσιος Εμβαλωτής
Abstract

Το σχολικό κλίμα αναγνωρίζεται ως κρίσιμος παράγοντας που επηρεάζει τη μαθητική επίδοση. Η παρούσα έρευνα εξετάζει το βαθμό στον οποίο το αίσθημα ασφάλειας και ο εκφοβισμός στο σχολείο, καθώς και το «αίσθημα του ανήκειν» των μαθητών/ητριών επηρεάζουν τις επιδόσεις των μαθητών και μαθητριών στα μαθηματικά βάσει των ερευνητικών δεδομένων του πρόσφατου κύκλου της διεθνούς έρευνας Programme for International Student Assessment (PISA-2022) στην Ελλάδα. Αξιοποιώντας τις δυνατότητες που παρέχουν τα πολυεπίπεδα μοντέλα ανάλυσης δεδομένων, απομονώθηκαν και αναλύθηκαν μεταβλητές∙δείκτες της έρευνας που αφορούν το σχολικό κλίμα και εξετάστηκε η σχέση τους με τις ακαδημαϊκές επιδόσεις στα μαθηματικά. Τα αποτελέσματα έδειξαν ότι ένα θετικό σχολικό κλίμα – με υψηλότερη αίσθηση του ανήκειν και ασφάλειας και χαμηλότερα επίπεδα εκφοβισμού – συνδέεται με υψηλότερες επιδόσεις στα μαθηματικά, ακόμη και μετά την συμπερίληψη κοινωνικοδημογραφικών μεταβλητών στην ανάλυση. Η έρευνα τονίζει τη σημασία ενός υποστηρικτικού και ασφαλούς σχολικού περιβάλλοντος, μέσα από αναθεώρηση πολιτικών και παρεμβάσεων, για την ενίσχυση των επιδόσεων των μαθητών/ητριών.

Article Details
  • Sezione
  • Επιστημονική Αρθρογραφία
Downloads
I dati di download non sono ancora disponibili.
Riferimenti bibliografici
Adams, R., & Wu, M. (eds.) (2003). Programme for International Student Assessment (PISA): PISA 2000 Technical Report, PISA, OECD Publishing, Paris, https://doi.org/10.1787/9789264199521-en.
Alcock, L., Attridge, N., Kenny, S., & Inglis, M. (2014). Achievement and behaviour in undergraduate mathematics: personality is a better predictor than gender. Research in Mathematics Education, 16(1), 1-17. https://www.doi.org/10.1080/14794802.2013.874094.
Allen, K.-A., Kern, M. L., Rozek, C. S., McInerney, D. M., & Slavich, G. M. (2021). Belonging: A review of conceptual issues, an integrative framework, and directions for future research. Australian Journal of Psychology, 73(1), 87–102. https://doi.org/10.1080/00049530.2021.1883409.
Armor, D. J., Marks, G. N., & Malatinszky, A. (2018). The impact of school SES on student achievement: Evidence from U.S. statewide achievement data. Educational Evaluation and Policy Analysis, 40(4), 613–630. https://doi.org/10.3102/0162373718787917.
Avvisati, F. (2020). The measure of socio-economic status in PISA: A review and some suggested improvements. Large-Scale Assessments in Education, 8(1), 8. https://doi.org/10.1186/s40536-020-00086-x.
Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychological Bulletin, 117(3), 497–529. https://doi.org/10.1037/0033-2909.117.3.497
Barbieri, C. A., & Miller-Cotto, D. (2021). The importance of adolescents’ sense of belonging to mathematics for algebra learning. Learning and Individual Differences, 87, 101993. https://doi.org/10.1016/j.lindif.2021.101993.
Bradley, R. H., & Corwyn, R. F. (2002). Socioeconomic status and child development. Annual Review of Psychology, 53, 371–399. https://doi.org/10.1146/annurev.psych.53.100901.135233.
Cascella, C. (2020). Intersectional effects of socioeconomic status, phase and gender on mathematics achievement. Educational Studies, 46(4), 476-496. https://www.doi.org/10.1080/03055698.2019.1614432.
Chen, G., & Weikart, L. A. (2008). Student background, school climate, school disorder, and student achievement: An empirical study of New York City’s middle schools. Journal of School Violence, 7(4), 3–20. https://doi.org/10.1080/15388220801973813.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). NY: Lawrence Erlbaum Associates. https://doi.org/10.4324/9780203771587.
Cohen, J., Mccabe, E. M., Michelli, N. M., & Pickeral, T. (2009). School climate: Research, policy, practice, and teacher education. Teachers College Record: The Voice of Scholarship in Education, 111(1), 180–213. https://doi.org/10.1177/016146810911100108.
Coleman, J. S. (1966). Equality of educational opportunity. National Center for Educational Statistics.
Contini, D., Tommaso, M. L. D., & Mendolia, S. (2017). The gender gap in mathematics achievement: Evidence from Italian data. Economics of Education Review. 58, 32–42. https://doi.org/10.1016/J.ECONEDUREV.2017.03.001.
Cowan, C. D., Hauser, R. M., Levin, H. M., Beale Spencer, M., & Chapman, C. (2012). Improving the measurement of socioeconomic status for the National Assessment of Educational Progress: A theoretical foundation. https://nces.ed.gov/nationsreportcard/pdf/researchcenter/Socioeconomic_Factors.pdf.
Crawford, J., Allen, K.-A., Sanders, T., Baumeister, R., Parker, P., Saunders, C., & Tice, D. (2024). Sense of belonging in higher education students: An Australian longitudinal study from 2013 to 2019. Studies in Higher Education, 49(3), 395–409. https://doi.org/10.1080/03075079.2023.2238006.
Daily, S. M., Mann, M. J., Kristjansson, A. L., Smith, M. L., & Zullig, K. J. (2019). School climate and academic achievement in middle and high school students. Journal of School Health, 89(3), 173–180. https://doi.org/10.1111/josh.12726.
Dulay, S., & Karadağ, E. (2017). The effect of school climate on student achievement. In E. Karadağ (Eds.), The factors effecting student achievement: Meta-analysis of empirical studies (pp. 196-213). Springer Cham. https://doi.org/10.1007/978-3-319-56083-0.
Durán-Narucki, V. (2008). School building condition, school attendance, and academic achievement in New York City public schools: A mediation model. Journal of Environmental Psychology, 28(3), 278–286. https://doi.org/10.1016/j.jenvp.2008.02.008
Else-Quest, N. M., Hyde, J. S., & Linn, M. C. (2010). Cross-national patterns of gender differences in mathematics: A meta-analysis. Psychological Bulletin, 136(1), 103-127. https://www.doi.org/10.1037/A0018053.
Erola, J., Jalonen, S., & Lehti, H. (2016). Parental education, class and income over early life course and children’s achievement. Research in Social Stratification and Mobility, 44, 33–43. https://doi.org/10.1016/j.rssm.2016.01.003.
Gentzis, E. A., & Dixson, D. D. (2024). How school climate relates to other psychosocial perceptions and academic achievement across the school year. School Psychology Review, 53(5), 441–458. https://doi.org/10.1080/2372966X.2023.2261835.
Hagerty, B. M. K., Lynch-Sauer, J., Patusky, K. L., Bouwsema, M., & Collier, P. (1992). Sense of belonging: A vital mental health concept. Archives of Psychiatric Nursing, 6(3), 172–177. https://doi.org/10.1016/0883-9417(92)90028-H.
Hox, J., Moerbeek, M., & van de Schoot, R. (2010). Multilevel analysis: Techniques and applications, Second Edition (2nd ed.). Routledge. https://doi.org/10.4324/9780203852279.
Karakolidis, A., Pitsia, V., & Emvalotis, A. (2016). Examining students’ achievement in mathematics: A multilevel analysis of the Programme for International Student Assessment (PISA) 2012 data for Greece. International Journal of Educational Research, 79, 106-115. https://www.doi.org/10.1016/j.ijer.2016.05.013.
Karakolidis, A., Pitsia, V., & Cosgrove, J. (2022). Multilevel modelling of International Large-Scale Assessment data. In Khine, M. S. (Eds.), Methodology for multilevel modeling in educational research: Concepts and Applications (pp. 141-159). Springer Singapore. https://doi.org/10.1007/978-981-16-9142-3.
Lee, J., Zhang, Y., & Stankov, L. (2019). Predictive validity of SES measures for student achievement. Educational Assessment, 24(4), 305–326. https://doi.org/10.1080/10627197.2019.1645590.
Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370–396. https://doi.org/10.1037/h0054346.
Mourshed, M., Farrell, D., & Barton, D. (2013). Education to employment: Designing a system that works. McKinsey & Company.
Muthén, L. K., & Muthén, B. O. (1998-2017). Mplus User’s Guide (8th ed.). Muthén & Muthén.
OECD. (2009). PISA data analysis manual: SPSS, Second Edition. PISA. OECD Publishing. https://doi.org/10.1787/9789264056275-en.
OECD. (2016). PISA 2015 results (Volume I): Excellence and equity in education, PISA, OECD Publishing. http://dx.doi.org/10.1787/9789264266490-en.
OECD. (2023a). PISA 2022 assessment and analytical framework. PISA, OECD Publishing. https://doi.org/10.1787/dfe0bf9c-en.
OECD. (2023b). PISA 2022 results (Volume I): The state of learning and equity in education, PISA, OECD Publishing. https://doi.org/10.1787/53f23881-en.
OECD. (2023c). PISA 2022 results (Volume II): Learning during – and from - disruption, PISA, OECD Publishing. https://doi.org/10.1787/a97db61c-en.
Papanastasiou, C. (2002). Effects of background and school factors on the mathematics achievement. Educational Research and Evaluation, 8(1), 55–70. https://doi.org/10.1076/edre.8.1.55.6916.
Pitsia, V., Biggart, A., & Karakolidis, A. (2017). The role of students' self-beliefs, motivation and attitudes in predicting mathematics achievement: A multilevel analysis of the Programme for International Student Assessment data. Learning and Individual Differences. 55, 163-173. https://www.doi.org/10.1016/j.lindif.2017.03.014.
Reilly, D., Neumann, D. L., & Andrews, G. (2015). Sex differences in mathematics and science achievement: A meta-analysis of national assessment of educational progress assessments. Journal of Educational Psychology, 107(3), 645-662. https://www.doi.org/10.1037/EDU0000012.
Reilly, D., Neumann, D. L., & Andrews, G. (2019). Investigating gender differences in mathematics and science: Results from the 2011 Trends in Mathematics and Science Survey. Research in Science Education, 49(1), 25-50. https://www.doi.org/10.1007/S11165-017-9630-6.
Rocher, T., & Hastedt, D. (2020). International large-scale assessments in education: a brief guide. IEA Compass: Briefs in Education No. 10. IEA.
Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. Wiley. https://doi.org/10.1002/9780470316696.
Samuelsson, M., & Samuelsson, J. (2016). Gender differences in boys’ and girls’ perception of teaching and learning mathematics. Open Review of Educational Research, 3(1), 18-34. https://doi.org/10.1080/23265507.2015.1127770.
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. A. B., & Bosker, R.J. (2002). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). Sage Publications.
Steele, C. M., & Aronson, J. (1995). Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology, 69(5), 797-811. https://www.doi.org/10.1037/0022-3514.69.5.797.
Twemlow, S. W., Fonagy, P., & Sacco, F. C. (2002). Feeling safe in school. Smith College Studies in Social Work, 72(2), 303–326. https://doi.org/10.1080/00377310209517660.
Walton, G. M., & Cohen, G. L. (2007). A question of belonging: Race, social fit, and achievement. Journal of Personality and Social Psychology, 92(1), 82–96. https://doi.org/10.1037/0022-3514.92.1.82.
Walton, G. M., & Cohen, G. L. (2011). A brief social-belonging intervention improves academic and health outcomes of minority students. Science, 331(6023), 1447–1451. https://doi.org/10.1126/science.1198364.
Wang, M.-T., & Degol, J. L. (2016). School climate: A review of the construct, measurement, and impact on student outcomes. Educational Psychology Review, 28(2), 315–352. https://doi.org/10.1007/s10648-015-9319-1.
White, K. R. (1982). The relation between socioeconomic status and academic achievement. Psychological Bulletin, 91(3), 461-481. https://doi.org/10.2307/1129211.
Woltman, H., Feldstain, A., MacKay, C. J., & Rocchi, M. (2012). An introduction to hierarchical linear modeling. Tutorials in Quantitative Methods for Psychology, 8(1), 52-69. https://doi.org/10.20982/tqmp.08.1.p052.
Wu, M. (2005). The role of plausible values in large-scale surveys. Studies in Educational Evaluation, 31(2–3), 114–128. https://doi.org/10.1016/j.stueduc.2005.05.005.