Matematiikan osaamistaso ja matemaattisen minäkäsityksen kehitys alakoulusta toiselle asteelle
DOI:
https://doi.org/10.31129/LUMAT.10.1.1732Keywords:
Matematiikka, Osaaminen, minäkäsitys, toinen aste, CLPM, BFLPEAbstract
Matematiikan osaamisen ja matemaattisen minäkäsityksen välillä on vahva positiivinen yhteys. Matematiikkaan liittyvän minäkäsityksen ja osaamistason pitkittäiset muutokset ja näiden vaikutukset auttavat ymmärtämään erilaisten oppijoiden valintojen taustoja suomalaisen koulu-uran aikana aina toisen asteen loppuun asti. Kartoitimme suomalaisten oppijoiden minäkäsityksen ja osaamistason yhteyttä Kansallisen koulutuksen arviointikeskuksen (KARVIn) vuosina 2008–2015 keräämän matematiikan arviointiaineiston pohjalta. Tarkasteluun käytimme ristiviiveyhteyksien paneelimallia (cross-lagged panel model, CLPM) sekä KARVIn pitkittäistutkimuksessa tunnistettua lukiolaisten luokittelua heidän suorittamiensa matematiikan kurssien määrän perusteella. Havaitsimme opiskelijoiden minäkäsityksen heikkenevän ja eri koulupolkujen osaamistasojen välisten erojen kasvavan. Ammatillisella puolella minäkäsitys vakiintuu peruskoulun lopun tasolle, kun taas lukiossa paljon kursseja suorittaneiden keskuudessa peruskoulun aikainen korkea minäkäsitys laskee voimakkaasti. Näillä ryhmillä peruskoulun osaamistaso on voimakkaammin yhteydessä toisen asteen lopun minäkäsitykseen kuin peruskoulun lopun minäkäsitys toisen asteen lopun osaamistasoon. Muissa luokittelun ryhmissä vastaavissa yhteyksissä ainoastaan peruskoulun lopun minäkäsityksellä on merkitsevä yhteys toisen asteen lopun osaamistasoon. Tutkimuksemme mukaan oppilaan vertaisryhmän tason vaikutus (ns. ”Big Fish, Little Pond” -vaikutus) selittää minäkäsityksen muutoksia toisella asteella.
Development of self-concept and proficiency in mathematics from primary school to upper secondary school
The positive correlations between mathematics achievement, enjoyment in mathematics, and self-efficacy beliefs in mathematics are well established. In this study, examining the longitudinal changes in mathematics attitudes and their effects help us to understand the reasons behind different choices the Finnish students make in their school path until the end of secondary grade. We have examined the relation between self-concept and proficiency in mathematics using the national longitudinal mathematics learning outcomes evaluation data collected by the Finnish Education Evaluation Centre (FINEEC) during 2008–2015. The relation between the variables is analysed using a cross-lagged panel model (CLPM) and FINEEC’s classification of mathematics course completed (2017) in the upper secondary education. Proficiency level gap increased over time between students and self-concept decreased. For students who chose the vocational track, there was no decrease in self-concept after lower secondary school. In the academic track, self-efficacy decrease strongest in high achievers group. In transition to upper secondary education, among vocational school and high achievers strongest cross-effect was from proficiency to self-concept. For others, only significant cross-effect was from previous self-concept to profession. Based on our research, “Big Fish Little Pond Effect” is related to changes in self-concept.
References
Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall.
Bandura, A. (1993). Perceived Self-Efficacy in Cognitive Development and Functioning. Educational Psychologist, 28(2), 117–148. https://doi.org/10.1207/s15326985ep2802_3
Bandura, A. (1997). Self-efficacy: The exercise of control. W H Freeman/Times Books/ Henry Holt & Co.
Bandura, A. (2000). Exercise of human agency through collective efficacy. Current Directions in Psychological Science, 9, 75–78. https://doi.org/10.1111/1467-8721.00064
Bandura, A. (2001). Social cognitive theory: An agentic perspective. Annual Review of Psychology, 52(1), 1–26. https://doi.org/10.1146/annurev.psych.52.1.1
Bandura, A. (2006). Toward a Psychology of Human Agency. Perspectives on Psychological Science, 1(2), 164–180. https://doi.org/10.1111/j.1745-6916.2006.00011.x
Bandura, A. (2012). On the Functional Properties of Perceived Self-Efficacy Revisited. Journal of Management, 38(1), 9–44. https://doi.org/10.1177/0149206311410606
Bandura, A. (toim.). (1995). Self-efficacy in changing societies. Cambridge, UK: Cambridge University Press.
Fang, J., Huang, X., Zhang, M., Huang, F., Li, Z. & Yuan, Q. (2018). The Big-Fish-Little-Pond Effect on Academic Self-Concept: A Meta-Analysis. Frontiers in psychology, 9, 15–69. https://doi.org/10.3389/fpsyg.2018.01569
Fennema, E., & Sherman, J. (1976). Fennema-Sherman Mathematics Attitudes Scales: Instruments Designed to Measure Attitudes toward the Learning of Mathematics by Females and Males. Journal for Research in Mathematics Education, 7(5), 324–326. https://doi.org/10.2307/748467
Froiland, J. M. & Davison, M. L. (2016) The longitudinal influences of peers, parents, motivation, and mathematics course-taking on high school math achievement. Learning and Individual Differences, Volume 50, 252–259. https://doi.org/10.1016/j.lindif.2016.07.012
Grigg, S., Perera, H. N., McIlveen, P., & Svetleff, Z. (2018). Relations among math self efficacy, interest, intentions, and achievement: A social cognitive perspective. Contemporary Educational Psychology, 53, 73–86. https://doi.org/10.1016/j.cedpsych.2018.01.007
Halme N., Kanste O., Nummi T., Perälä M-L. (2014). Rakenneyhtälömallin kehittäminen ja arviointi – tutkimuksen kohteena avun antaminen lasten ja perheiden palveluissa. Sosiaalilääketieteellinen aikakauslehti 51, 272–288. http://ojs.tsv.fi/index.php/SA/article/view/48474/14148
Hamaker, E. L., Kuiper, R. M., Grasman, R. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20(1), 102–116. https://doi.org/10.1037/a0038889
Hannula, M. S., Bofah, E., Tuohilampi, L., & Metsämuuronen, J. (2014). A longitudinal analysis of the relationship between mathematics-related affect and achievement in Finland. Teoksessa S. Oesterle, P. Liljedahl, C. Nicol & D. Allan (toim.), Proceedings of the 38th conference of the IGPME and the 36th conference of the PME-NA (Vol. 3, s. 249–256). Vancouver, Canada: PME.
Hannula, M.S. (2002). Attitude towards mathematics: emotions, expectations and values. Educational Studies in Mathematics 49, 25–46. https://doi.org/10.1023/A:1016048823497
Hill, A. B. (1965). “The Environment and Disease: Association or Causation?,” Proceedings of the Royal Society of Medicine, 58, 295–300. https://doi.org/10.1177/0141076814562718
Holm, M. E., Korhonen, J., Laine, A., Björn, P. M. & Hannula, M. S. (2020). Big-fish-little-pond effect on achievement emotions in relation to mathematics performance and gender. International Journal of Educational Research, 104, 101692. https://doi.org/10.1016/j.ijer.2020.101692
Hooper D., Coughlan J. & Mullen M. R. (2008). Structural equation modelling. Guidelines for determining model fit. Electronic J Bus Res Meth 2008, 6, 53−60. https://academic-publishing.org/index.php/ejbrm/article/view/1224
Huisman, T. (2006). Luen, kirjoitan ja ratkaisen. Peruskoulun kolmasluokkalaisten oppimistulokset äidinkielessä ja kirjallisuudessa sekä matematiikassa. Oppimistulosten arviointi 7/2006. Opetushallitus. Helsinki: Yliopistopaino.
IBM Corp. (2017). IBM SPSS Statistics for Windows, Version 25.0. Armonk, NY: IBM Corp.
Kaleva, S., Pursiainen, J., Hakola, M. (2019). Students’ reasons for STEM choices and the relationship of mathematics choice to university admission. IJ STEM Ed 6, 43. https://doi.org/10.1186/s40594-019-0196-x
Kozak, M. (2019), What is Strong Correlation?. Teaching Statistics, 31, 85–86. https://doi.org/10.1111/j.1467-9639.2009.00387.x
Kupiainen, S., Marjanen, J. & Ouakrim-Soivio, N. (2018). Ylioppilas valintojen pyörteissä: Lukio-opinnot, ylioppilastutkinto ja korkeakoulujen opiskelijavalinta. Ainedidaktisia tutkimuksia. Suomen ainedidaktisen tutkimusseuran julkaisuja, Nro 14. Suomen ainedidaktinen tutkimusseura ry. http://hdl.handle.net/10138/231687
Lee, J. (2009). Universals and specifics of math self-concept, math self-efficacy, and math anxiety across 41 PISA 2003 participating countries. Learning and Individual Differences, 19(3), 355–365. https://doi.org/10.1016/j.lindif.2008.10.009
Liou, P. Y. (2014). Investigation of the Big-Fish-Little-Pond Effect on Students’ Self-Concept of Learning Mathematics and Science in Taiwan: Results from TIMSS 2011. Asia-Pacific Edu Res 23, 769–778. https://doi.org/10.1007/s40299-013-0152-3
Ma, X. & Kishor, N. (1997). Assessing the Relationship between Attitude toward Mathematics and Achievement in Mathematics: A Meta-Analysis. Journal for Research in Mathematics Education, 28(1), 26–47. https://doi.org/10.2307/749662
Marsh, H. W. & Craven, R. G. (2006). Reciprocal effects of self-concept and performance from a multidimensional perspective: Beyond seductive pleasure and unidimensional perspectives. Perspect Psychol Sci, 1(2), 133–163. https://doi.org/10.1111/j.1745-6916.2006.00010.x
Marsh, H. W. & Hau, K. T. (2004). Explaining paradoxical relations between academic self-concepts and achievements: Cross-cultural generalizability of the internal/external frame of reference predictions across 26 countries. Journal of Educational Psychology, 96(1), 56–67. https://doi.org/10.1037/0022-0663.96.1.56
Marsh, H. W. (1984). Relations among dimensions of self-attribution, dimensions of self-concept, and academic achievements. Journal of Educational Psychology, 76(6), 1291–1308. https://doi.org/10.1037/0022-0663.76.6.1291
Marsh, H. W. (1987). The big-fish-little-pond effect on academic self-concept. Journal of Educational Psychology, 79(3), 280–295. https://doi.org/10.1037/0022-0663.79.3.280
Marsh, H. W. (1990). Influences of internal and external frames of reference on the formation of math and english self-concepts. Journal of Educational Psychology, 82(1), 107–116. https://doi.org/10.1037/0022-0663.82.1.107
Marsh, H. W., Byrne, B. M. & Shavelson, R. J. (1988). A multifaceted academic self-concept: Its hierarchical structure and its relation to academic achievement. Journal of Educational Psychology, 80(3), 366–380. https://doi.org/10.1037/0022-0663.80.3.366
Marsh, H. W., Parker, P. D. & Pekrun, R. (2019). Three paradoxical effects on academic self-concept across countries, schools, and students. European Psychologist, 24(3), 231–242. http://doi.org/10.1027/1016-9040/a000332
Marsh, H. W., Seaton, M., Trautwein, U., Lüdtke, O., Hau, K. T., O’Mara, A. J. & Craven, R. G. (2008). The big-fish–little-pond-effect stands up to critical scrutiny: Implications for theory, methodology, and future research. Educational psychology review, 20(3), 319–350. https://doi.org/10.1007/s10648-008-9075-6
Marsh, H. W., Smith, I. D. & Barnes, J. (1985). Multidimensional self-concepts. Relations with sex and academic achievement. Journal of Educational Psychology, 77(5), 581–596. https://doi.org/10.1037/0022-0663.77.5.581
Marsh, H. W., Trautwein, U., Lüdtke, O. & Köller, O. (2008b). Social comparison and big-fish-little-pond effects on self-concept and other self-belief constructs: Role of generalized and specific others. Journal of Educational Psychology, 100(3), 510. https://doi.org/10.1037/0022-0663.100.3.510
Martin, A. J. & Marsh, H. W. (2008). Academic buoyancy: Towards an understanding of students' everyday academic resilience. Journal of School Psychology, 46(1), 53–83. https://doi.org/10.1016/j.jsp.2007.01.002
Metsämuuronen, J. & Salonen, V. (2017). Matemaattisen osaamisen piirteitä ammatillisen kou-lutuksen lopussa 2015 ja pitkän ajan muutoksia. Kansallinen koulutuksen arviointikeskus. Julkaisut 2:2017. Tampere: Juvenes Print – Suomen Yliopistopaino Oy
Metsämuuronen, J. & Tuohilampi, L. (2017). Matemaattinen osaaminen lukiokoulutuksen lopulla 2015. Kansallinen koulutuksen arviointikeskus. Julkaisut 3:2017. Tampere: Juvenes Print – Suomen Yliopistopaino Oy
Metsämuuronen, J. (2012). Challenges of the Fennema-Sherman test in the international comparisons. International Journal of Psychological Studies, 4(3), 1–22. https://doi.org/10.5539/ijps.v4n3p1
Metsämuuronen, J. (2013). Perusopetuksen matematiikan oppimistulosten pitkittäisarviointi vuosina 2005–2012. [Longitudinal analysis of the Mathematical Achievement in the Compulsory Education in 2005–2012.] Koulutuksen seurantaraportit 2013:4. Opetushallitus. Tampere: Juvenes Print – Suomen Yliopistopaino Oy. s. 65–172.
Metsämuuronen, J. (2017). Oppia ikä kaikki–Matemaattinen osaaminen toisen asteen koulutuksen lopussa 2015. Helsinki: Kansallinen koulutuksen arviointikeskus. Julkaisut, 1:2017.
Möller, J., Zitzmann, S., Helm, F., Machts, N. & Wolff, F. (2020). A meta-analysis of relations between achievement and self-concept. Review of Educational Research, 90(3), 376–419. https://doi.org/10.3102/0034654320919354
Mund, M., Johnson, M. D. & Nestler, S. (2021). Changes in size and interpretation of parameter estimates in within-person models in the presence of time-invariant and time-varying covariates. Frontiers in psychology, 3663. https://doi.org/10.3389/fpsyg.2021.666928
Muthén, L. K. & Muthén, B. O. (1998–2017). Mplus User’s Guide. Eighth Edition. Los Angeles, CA: Muthén & Muthén
Niemi E. K. & Metsämuuronen J. (toim.). (2010). Miten matematiikan taidot kehittyvät. Matematiikan oppimistulokset peruskoulun viidennen vuosiluokan jälkeen vuonna 2008. Koulutuksen seurantaraportti 2010:2. Opetushallitus. Helsinki: Edita Prima Oy.
Niepel, C., Brunner, M. & Preckel, F. (2014). The longitudinal interplay of students' academic self‐concepts and achievements within and across domains: Replicating and extending the reciprocal internal/external frame of reference model. Journal of Educational Psychology, 106, 1170–1191. https://doi.org/10.1037/a0036307
Nikulainen, K. (2019). Oulun ammattikorkeakouluun valittujen opiskelijoiden lukiotausta: ylioppilaskirjoitusten arvosanat ja ainevalinnat. Maantieteen pro gradu -tutkielma. Oulun yliopisto.
OECD (2019), PISA 2018 Results (Volume I): What Students Know and Can Do, PISA, OECD Publishing, Paris, https://doi.org/10.1787/5f07c754-en
Pajares, F. & Graham, L. (1999). Self-efficacy, motivation constructs, and mathematics performance of entering middle school students. Contemporary Educational Psychology, 24(2), 124–139. https://doi.org/10.1006/ceps.1998.0991
Pajares, F. & Miller, M. D. (1995). Mathematics self-efficacy and mathematics performances: The need for specificity of assessment. Journal of Counseling Psychology, 42(2), 190–198. https://doi.org/10.1037/0022-0167.42.2.190
Pajares, F. (2005). Gender Differences in Mathematics Self-Efficacy Beliefs. Teoksessa A. M. Gallagher & J. C. Kaufman (toim.), Gender differences in mathematics: An integrative psychological approach (s. 294–315). Cambridge University Press.
Pearl, J. (2009). Causal inference in statistics: An overview Statistics Surveys, Vol 3, 96–146. https://doi.org/10.1214/09-SS057
Peixoto, F., Sanches, C., Mata, L. & Monteiro, V. (2017). “How do you feel about math?”: relationships between competence and value appraisals, achievement emotions and academic achievement. Eur J Psychol Educ 32, 385–405. https://doi.org/10.1007/s10212-016-0299-4
Portaankorva-Koivisto, P.M., Eronen, L., Hannula, M. & Kupiainen, S. (2018). Lukion ensimmäinen yhteinen matematiikan kurssi – mielekästä ja merkityksellistä. FMSERA Journal. 2, 1, 57–65. Retrieved from https://journal.fi/fmsera/article/view/69899
Preckel, F., Goetz, T., Pekrun, R. & Kleine, M. (2008). Gender differences in gifted and average-ability students: Comparing girls' and boys' achievement, self-concept, interest, and motivation in mathematics. Gifted Child Quarterly, 52(2), 146–159. https://doi.org/10.1177/0016986208315834
Pursiainen, J. (2016). Valintaperusteiden kertomaa. Solmu 2, 1–6.
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Danmarks Pædagogishe Institut. Studies in Mathematic Psychology I. Copenhagen: Nielsen & Lydiche.
Schreiber J. B., Nora A., Stage F. G., Barlow E. A. & King J. (2006). Reporting structural equation modelling and confirmatory factor analysis results: a review. J Educ Res 2006, 99, 323−37. https://doi.org/10.3200/JOER.99.6.323-338
Siegel, A. F. (2016). Multiple regression: predicting one variable from several others. Practical Business Statistics. Academic Press: United States of America. https://doi.org/10.1016/B978-0-12-804250-2.00012-2
Sung, Y., Huang, L., Tseng, F. & Chang, K. (2014). The aspects and ability groups in which little fish perform worse than big fish: Examining the big-fish-little-pond effect in the context of school tracking. Contemporary Educational Psychology, 39(3), 220–232. https://doi.org/10.1016/j.cedpsych.2014.05.002
Timmerman, H. L., Toll, S. W. M., Van Luit, J. E. H. (2017) The relation between math self-concept, test and math anxiety, achievement motivation and math achievement in 12 to 14-year-old typically developing adolescents. Psychology, Society, & Education, 9(1), 89–103. https://doi.org/10.21071/psye.v9i1.13854
Verhelst, N. G., Glas, C. A. W. & Verstralen H. H. F. M. (1995). One-Parameter Logistic Model OPLM. Arnhem: Cito.Justi, R., & Gilbert, J. (2003). Models and modelling in chemical education. Teoksessa J. Gilbert, O. de Jong, R. Justi, D. F. Treagust, & J. H. Driel (toim.), Chemical Education: Towards Research-based Practice (s. 47–68). Springer Netherlands. https://doi.org/10.1007/978-1-4612-4230-7_12
Zyphur, M. J., Allison, P. D., Tay, L., Voelkle, M. C., Preacher, K. J., Zhang, Z., Hamaker, E. L., Shamsollahi, A., Pierides, D. C., Koval, P. & Diener, E. (2020). From Data to Causes I: Building A General Cross-Lagged Panel Model (GCLM). Organizational Research Methods, 23(4), 651–687. https://doi.org/10.1177/1094428119847278
Downloads
Published
How to Cite
License
Copyright (c) 2022 Reito Visajaani Salonen, Markku S. Hannula
This work is licensed under a Creative Commons Attribution 4.0 International License.