Student characteristics and mathematics achievement in Timss

Titolo Rivista: CADMO
Autori/Curatori: Elisa Caponera, Paolo Maria Russo
Anno di pubblicazione: 2014 Fascicolo: 2 Lingua: Italiano
Numero pagine: 13 P. 93-105 Dimensione file: 76 KB
DOI: 10.3280/CAD2014-002008
Il DOI è il codice a barre della proprietà intellettuale: per saperne di più clicca qui

Qui sotto puoi vedere in anteprima la prima pagina di questo articolo.

Se questo articolo ti interessa, lo puoi acquistare (e scaricare in formato pdf) seguendo le facili indicazioni per acquistare il download credit.
Acquista Download Credits per scaricare questo Articolo in formato PDF

anteprima articolo

FrancoAngeli è membro della Publishers International Linking Association, Inc (PILA)associazione indipendente e non profit per facilitare (attraverso i servizi tecnologici implementati da CrossRef.org) l’accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche

In the present study, the relationship between student characteristics and mathematics performance was evaluated using a structural equation modeling approach. Italian students participated at Timss 2011 field test (N = 1264; 52% female, mean age: 13 years and 10 months ± 6 months) completed a questionnaire including measures of socio-economic and cultural background, general reasoning ability and self-concept in mathematics and the Timss mathematics achievement test. A mediation structural equation model assessed the direct and indirect effects of the general reasoning ability test and socio-economic and cultural background through the mediation of selfconcept in mathematics. The results showed that all measures were significantly associated with mathematics achievement test, furthermore selfconcept partially mediated the effects of socio-economic status and general reasoning ability.

  1. Baron, R.M., Kenny, D.A. (1986), “The Moderator-mediator Variable Distinction in Social Psycholog complex interaction between cognitive, motivational and affective variables ical Research: Conceptual, Strategic, and Statistical Considerations”, Journal of Personality and Social Psychology, 51 (6), pp. 1173-1182.
  2. Bentler, P.M. (1990), “Comparative fit Indexes in Structural Models”, Psychological Bulletin, 107 (2), pp. 238-246.
  3. Boncori, L. (2001), KM – Test di ragionamento, Roma, Crisp.
  4. Boncori, L. (1993), Teoria e tecniche dei test. Torino: Bollati Boringhieri.
  5. Browne, M.W., Cudeck, R. (1993), “Alternative Ways of Assessing Model Fit”, in K.A. Bollen, J.S. Long (eds), Testing Structural Equation Models, Sage Focus Editions, Newbury Park, Sage Publication, vol. 154, pp. 136-162.
  6. Byrne, B.M. (2010), Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. New York: Routledge/Taylor & Francis Group, 2nd ed.
  7. Chiu, M.M., Xihua, Z. (2008), “Family and Motivation Effects on Mathematics Achievement: Analyses of Students in 41 Countries”, Learning and Instruction, 18 (4), pp. 321-336.
  8. Deary, I.J., Strand, S., AIth, P., Fernandes, C. (2007), “Intelligence and Educational Achievement”, Intelligence, 35, pp. 13-21.
  9. Eccles, J.S., Wigfield, A. (1995), “In the Mind of the Actor: The Structure of Adolescents’ Achievement Task Values and Expectancy-related Beliefs”, Personality and Social Psychology Bullettin, 21, pp. 215-225.
  10. European Commission (2008), “Improving Competences for the 21st Century: An Agenda for European Cooperation on Schools, available on http://eurlex.europa.eu/LexUriServ/ LexUriServ.do?uri=COM:2008:0425:FIN:EN:PDF.
  11. European Parliament, Council of the European Union (2006), Recommendation of the European Parliament and of the Council of 18 December 2006 on Key Competences for Lifelong Learning, available on http://eur-lex.europa.eu/legalcontent/EN/NOT/?uri= CELEX:32006H0962.
  12. Fin, L.S., Ishak, Z. (2013), “Direct and Indirect Effects of Self-concept and Socioeconomic Status on Students’Academic Achievement”, Educational Research International, 1 (2), pp. 40-49.
  13. Gomes, C.M.A., Golino, H.F., Menezes, I.G. (2014), “Predicting School Achievement Rather than Intelligence: Does Metacognition Matter?”, Psychology, 5 (9), pp. 1095-1110.
  14. Hemmings, B., Grootenboer, P., Kay, R. (2011), “Predicting Mathematics Achievement: The Influence of Prior Achievement and Attitudes”, International Journal of Science and Mathematics Education, 9 (3), pp. 691-705. House, J.D. (2005), “Mathematics Beliefs and Achievement of Adolescent Students in Japan: Results from the Timss 1999 Assessment”, Psychological reports, 97(3), pp. 717-720.
  15. House, J.D. (2009), “Mathematics Beliefs and Achievement of a National Sample of Native American Students: Results from the Trends in International Mathematics and Science Study (Timss) 2003 United States assessment”, Journal Information, 104 (2), pp. 439-446.
  16. Iacobucci, D., Saldanha, N, Deng, X. (2007), “A Meditation on Mediation: Evidence
  17. that Structural Equations Models Perform better than Regressions”, Journal of Consumer Psychology, 17 (2), pp. 139-153.
  18. Lee, J.W. (1999), “The Comparisons of Statistical Reasoning between Science-gifted Students and Average Students: The Application of the Law of Large Numbers”, Chinese Journal of Psychology, 41(1), pp. 1-18.
  19. Levpušček, M.P., Zupančič, M., Sočan, G. (2013), “Predicting Achievement in Mathematics in Adolescent Students The Role of Individual and Social Factors”, The Journal of Early Adolescence, 33 (4), pp. 523-551.
  20. Mackintosh, N.J. (1998), IQ and Human Intelligence. Oxford: Oxford University Press.
  21. Marsh, H., Trautwein, U., Lüdtke, O., Köller, O., Baumert, J. (2005), “Academic Self-Concept, Interest, Grades, and Standardized Test Scores: Reciprocal Effects Models of Causal Ordering”, Child Development, 76 (2), pp. 397-416.
  22. Mullis, I.V.S., Martin, M.O., Ruddock, G.J., O’Sullivan, C.Y., Preuschoff, C. (2009), Timss 2011 Assessment Frameworks. Chestnut Hill: Timss & PIRLS International Study Center Lynch School of Education, Boston College; Amsterdam, the Netherlands: International Association for the Evaluation of Educational Achievement (Iea).
  23. Mullis, I.V.S., Martin, M.O., Foy, P., Arora, A. (2012), Timss 2011 International Results in Mathematics. Chestnut Hill: Timss & PIRLS International Study Center, Lynch School of Education, Boston College; Amsterdam, the Netherlands: International Association for the Evaluation of Educational Achievement (Iea).
  24. Oecd (2014), Education at a Glance 2014: Oecd Indicators, Oecd Publishing, available on http://www.oecd.org/edu/Education-at-a-Glance-2014.pdf.
  25. Pajares, F., Kranzler, J. (1995), “Self-efficacy Beliefs and General Mental Ability in Mathematical Problem-solving”, Contemporary Educational Psychology, 20 (4), pp. 426-443.
  26. Plomin, R. (1999), “Genetics and General Cognitive Ability”, Nature, 402 (supp. 6761), pp. C25-C29.
  27. Poropat, A. (2009), “A Meta-analysis of the Five-factor Model of Personality and Academic Performance”, Psychological Bulletin, 135 (2), pp. 322-338.
  28. Robitaille, D.F., Taylor, A.R. (2002), “From Sims to Timss: Trends in Students’ Achievement in Mathematics”, in D.F. Robitaille, A.E, Beaton (eds), Secondary Analysis of the Timss Data. Dordrecht: Kluwer Academic Publishers, pp. 47-62.
  29. Rohde, T.E., Thompson, L.A. (2007), “Predicting Academic Achievement with Cognitive Ability”, Intelligence, 35 (1), pp. 83-92. Russo, P.M. (2001), “Il fattore G di Charles E. Spearman”, Cadmo, 11 (27), pp. 86-91.
  30. Šimelionienė, A., Gintilienė, G. (2010), “Factors Affecting the High Achievements of 17-18 Years Old Students”, Special Education, 2 (23), pp. 149-157.
  31. Singh, K., Granville, M., Dika, S. (2002), “Mathematics and Science Achievement: Effects of Motivation, Interest, and Academic Engagement”, Journal of Educational Research, 95 (6), pp. 323-332.
  32. Sirin, R. (2005), “Socioeconomic Status and Academic Achievement: A Meta-analytic Review of Research”, Review of Educational Research, 75 (3), pp. 417-453.
  33. Spinath, B., Spinath, F., Harlaar, N., Plomin, R. (2006), “Predicting School Achievement from General Cognitive Ability, Self-perceived Ability, and Intrinsic Value”, Intelligence, 34 (4), pp. 363-374.
  34. Steiger, J.H. (1990), “Structural Model Evaluation and Modification: An Interval Estimation Approach”, Multivariate Behavioral Research, 25 (2), pp. 173-180.
  35. Suárez-Álvarez, J., Fernández-Alonso, R., Muñiz, J. (2014), “Self-concept, Motivation, Expectations, and Socioeconomic Level as Predictors of Academic Performance in Mathematics”, Learning and Individual Differences, 30, pp.118-123.
  36. Trautwein, U., Lüdtke, O., Köller, O., Baumert, J. (2006), “Self-esteem, Academic Selfconcept, and Achievement: How the Learning Environment Moderates the Dynamics of Self-concept”, Journal of Personality and Social Psychology, 90 (2), p. 334.
  37. Van der Stel, M., Veenman, M. (2008), “Relation between Intellectual Ability and Metacognitive Skillfulness as Predictors of Learning Performance of Young Students Performing Tasks in Different Domains”, Learning and Individual Differences, 18, pp. 128-134.
  38. Veenman, M.V.J., Spaans, M. (2005), “Relation between Intellectual and Metacognitive Skills: Age and Task Differences”, Learning and Individual Differences, 15, pp. 159-176.
  39. Volet, S.E. (1997), “Cognitive and Affective Variables in Academic Learning: The Significance of Direction and Effort in Students’ Goals”, Learning and
  40. Instruction, 7 (3), pp. 235-254.
  41. Wang, Z., Osterlind, S.J., Bergin, D.A. (2012), “Building Mathematics Achievement Models in Four Countries using Timss 2003”, International Journal of Science and Mathematics education, 10, pp. 1215-1242.
  42. Whimbey, A., Lochhead, J. (1999), Problem Solving and Comprehension. Mahwah: Lawrence Erlbaum Associates, 6th ed.
  43. Yang, Y. (2003), “Dimension of Socio-economic Status and Their Relationship to Mathematics and Science Achievement at Individual and Collective Levels”, Scandinavian Journal of Education Research, 47 (1), pp. 21-41.
  44. Yoshino, A. (2012), “The Relationship between Self-concept and Achievement in Timss 2007: A Comparison between American and Japanese Students”, International Review of Education, 58 (2), pp. 199-219.
  45. Zhao, X, Lynch, J.G., Chen, Q. (2010), “Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis”, Journal of Consumer Psychology, 37, pp. 197-206.

Elisa Caponera, Paolo Maria Russo, Student characteristics and mathematics achievement in Timss in "CADMO" 2/2014, pp 93-105, DOI: 10.3280/CAD2014-002008