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How do different background variables predict learning outcomes?

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Abstract

This article is a part of a research project aimed to find out how different background variables are related to learning outcomes in school subject Sloyd as found in the national evaluation of the Finnish National Board of Education. Results from this larger research project were previously published in this journal, where pupils’ readiness for Self-Regulated Learning were reported. Since then, pupils’ experiences of classroom techniques, attitudes towards the subject, leisure time interests and learning within the two domains of the subject (Technical Domain and Textile Domain, as the subject is usually divided in Finland) have been studied and results have been published in different journals. In this article, a new Structural Equation Model concludes the previous results. The new model highlights two paths of how the use of different learning orientations can predict successful learning outcomes. These paths can be followed using two pedagogical models: the Exploratory Production Approach and the Domain Specific Approach. Experiencing Learner-Centred Learning predicts positive attitudes and success in Exploratory Production activities. Experiencing Collaborative Learning predicts success in domain specific learning outcomes. Success in Exploratory Production predicts successful learning outcomes in both domains. The conclusion is that regulatory knowledge, “why”, is related to production activities and it is processed prior to domain specific knowledge, “what” and “how”. To develop the subject and pedagogy for schools and teacher training, it is not important to follow an approach defined by the domains (technical or textile). It is more important to teach pupils how to manage in the modern technological world and to understand why they need to be able to improve their life-world through Exploratory Production activities.

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References

  • Anderson, L. W., & Bourke, S. F. (2000). Assessing affective characteristics in schools. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.

    Google Scholar 

  • Ardies, J., de Maeyer, S., Gijbels, D., & van Keulen, H. (2014). Students’ attitudes towards technology. International Journal of Technology and Design Education,. doi:10.1007/s10798-014-9268-x.

    Google Scholar 

  • Ashcraft, M. H., & Kirk, E. P. (2001). The relationships among working memory, math anxiety and performance. Journal of Experimental Psychology, 120(2), 224–237.

    Article  Google Scholar 

  • Bentler, P. (1990). Comparative fit index in structural models. Psychological Bulletin, 107, 238–246.

    Article  Google Scholar 

  • Byrne, B. (2012). Structural equation modeling with Mplus. Basic concepts, applications, and programming. Routledge: Taylor & Francis.

    Google Scholar 

  • Cronbach, L. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.

    Article  Google Scholar 

  • de Vries, M. J. (2011). A concept-context framework for engineering and technology education. In M. Barak & M. Hacker (Eds.), Fostering Human Development Through Engineering and Technology Education (pp. 75–87). Boston: Sense Publishers.

    Chapter  Google Scholar 

  • Dewey, J. (2011) Democracy and education (1916). Milton Keynes: Simon & Brown.

  • Fennema, E., & Sherman, J. A. (1976). Fennema–Sherman mathematics attitudes scales: Instruments designed to measure attitudes toward the learning of mathematics by males and females. Journal for Research in Mathematics Education, 7, 324–326.

    Article  Google Scholar 

  • Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behaviour: An introduction to theory and research. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Gliner, J., Morgan, G., & Harmon, R. (2001). Measurement reliability. Journal of the American Academy of Child and Adolescent Psychiatry, 40(4), 486–488.

    Article  Google Scholar 

  • Gradwell, J. B. (1999). The immensity of technology… and the role of the individual. International Journal of Technology and Design Education, 9, 241–267.

    Article  Google Scholar 

  • Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis (6th ed.). Upper Saddle River: Pearson Education.

    Google Scholar 

  • Hamilton, J. W. (2007). Enhancing learning through collaborative inquiry and action. Design and Technology Education: An International Journal, 12(3), 33–46.

    Google Scholar 

  • Harinen, P., & Halme, J. (2012), Hyvä, paha koulu. Kouluhyvinvointia hakemassa. [Good, bad school. Searching school well being] Suomen UNICEF Nuorisotutkimusverkosto/Nuorisotutkimusseura, verkkojulkaisuja 56. http://www.unicef.fi/files/unicef/tutkimukset/hyva_paha_koulu.pdf. Accessed 25 November 2013.

  • Hoe, S. (2008). Issues and procedures in adapting structural equation modeling technique. Quantitative methods inquires. Journal of Applied Quantitative Methods, 3(1), 76–83.

    Google Scholar 

  • Hong, J.-C., Yu, K.-C., & Chen, M.-Y. (2011). Collaborative learning in technological project design. International Journal of Technology and Design Education, 21(3), 335–348.

    Article  Google Scholar 

  • Hoyle, R. (1995). The structural equation modeling approach: Basic concepts and fundamental issues. In R. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 1–15). Thousand Oaks: Sage.

    Google Scholar 

  • Hu, L., & Bentler, P. M. (1999). Cut-off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55.

    Article  Google Scholar 

  • Jöreskog, K. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34, 183–202.

    Article  Google Scholar 

  • Kupiainen, S., Marjanen, J., Vainikainen, M. P., & Hautamäki, J. (2011), Oppimaan oppiminen Vantaan peruskoulussa. Kolmas-, kuudes- ja yhdeksäsluokkalaiset oppijoina keväällä 2010 [Learning to Learn in Compulsory Education in Vantaa. The Learning of Third, Sixth and Ninth Graders in 2010]. [City of Vantaa and University of Helsinki].

  • Kallio, M. (2014). Risk-responsibility in safety education culture (Annales Universitatis Turkuensis C382. Painosalama: Turku.

    Google Scholar 

  • Kangas, K., Seitamaa-Hakkarainen, P., & Hakkarainen, K. (2013). Design thinking in elementary students’ collaborative lamp designing process. Design and Technology Education: An International Journal, 18(1), 30–43.

    Google Scholar 

  • Kimbell, R., Stables, K., & Green, R. (2001). The nature and purpose of design and technology. In G. Owen-Jackson (Ed.), Teaching design and technology in secondary schools (pp. 19–30). London, NY: Raoutledge/Falmer, Taylor & Francis Group.

    Google Scholar 

  • Klausmaier, H., & Goodwin, W. (1975). Learning and human abilities (4th ed.). New York: Harper & Row.

    Google Scholar 

  • Kline, R. (2011). Principles and practice of structural equation modeling (3rd ed.). New York & London: The Guilford Press.

    Google Scholar 

  • Koehler, M. J., & Mishra, P. (2008). Introducing TPCK. In the AACTE Committee on Innovation and Technology (Ed.), Handbook of technological pedagogical content knowledge (TPCK) for educators (pp. 3–29). London: Raoutledge/Falmer, Taylor & Francis Group.

    Google Scholar 

  • Koulutuksen tilastollinen vuosikirja [Educational Statistics]. (2012) Opetushallitus [Finnish National Board of Education] Reports 2012:5 (Tampere: Juvenes Print—Tampere University Press).

  • Lahdes, E. (1997). Peruskoulun uusi didaktiikka. [New Didactic of Basic School]. Helsinki: Kustannusosakeyhtiö Otava.

  • Laitinen, S., Hilmola A., & Juntunen, M.-L. (2011). Perusopetuksen musiikin, kuvataiteen ja käsityön oppimistulosten arviointi 9. vuosiluokalla [Assessment of Learning Results in Compulsory Education Music, Art and Craft in 9th Grade] (Finnish National Board of Education. Training Reports 2011:1).

  • Lim, S. Y., & Chapman, E. (2013). An investigation of the fennema–sherman mathematics anxiety subscale. Measurement and Assessment in Counseling and Development, 46(1), 26–37.

    Google Scholar 

  • Little, T. D., Lindenberger, U., & Nesselroade, J. R. (1999). On selecting indicators for multivariate measurement and modeling with latent variables: When “good” indicators are bad and “bad” indicators are good. Psychological Methods, 4(2), 192–211.

    Article  Google Scholar 

  • MacCallum, R., & Austin, J. (2000). Applications of structural equation modelling in psychological research. Annual Reviews of Psychology, 51, 201–226.

    Article  Google Scholar 

  • McDonald, R., & Ho, R. (2002). Principles and practises in reporting structural equation analyses. Psychological Methods, 7, 64–82.

    Article  Google Scholar 

  • Metsämuuronen, J. (2012). Challenges of the Fennema–Sherman test in the international comparisons. International Journal of Psychological Studies, 4(3), 1–22.

    Article  Google Scholar 

  • Metsärinne, M. (2004). ‘Projektikäsityöopetus. [Project Based Sloyd].’ Techne Series, Research in Sloyd Education and Craft Science A, 6,1–249.

  • Metsärinne, M. (2011). Käsityön prosessioppimisen tarkastelua koulukäsityön perus- ja otosmittariarvioinnin perusteella [Examining Process Learning of technology by Basic and Sample Assessment]. In S. Laitinen & A. Hilmola (eds) Taito- ja taideaineiden oppimistulokset—asiantuntijoiden arviointia [Learning Results in Craft—An Expert Analysis], (Finnish National Board of Education. Reports 2011:11, 194–206).

  • Metsärinne, M., & Kallio, M. (2011). Defining craft quality theory framework in Sloyd education. In: M. Johansson & M. Porko-Hudd (eds), Vetenskapliga perspektiv och metoder inom slöjdfältet [Scientific Perspective and Methods for Sloyd], Techne Series A, 18 (1).

  • Metsärinne, M., & Kallio, M. (2014a). Experiences of classroom techniques and technology learning outcomes. Design and Technology Education: An International Journal, 19(3), 9–22.

  • Metsärinne, M., & Kallio, M. (2014b). Craft interests during leisure time and craft learning outcomes in Finland. Craft Research, 5(1), 35–53.

    Article  Google Scholar 

  • Metsärinne, M., & Kallio, M. (2015). How are students’ attitudes related to learning outcomes? International Journal of Technology and Design Education, 1–19. doi:10.1007/s10798-015-9317-0.

  • Metsärinne, M., Kallio, M., & Virta, K. (2015). Pupils’ readiness for Self-Regulated Learning in the forethought phase of exploratory production. International Journal of Technology and Design Education, 25(1), 85–108.

    Article  Google Scholar 

  • Myerson, J. (1997). Tornadoes, t-squares and technology: Can computing be a craft? In P. Dormer (Ed.), The Culture of craft. Status and future (pp. 176–201). London: Manchester University Press.

    Google Scholar 

  • National Core Curriculum for Basic Education. (2014). Finnish National Board of Education. http://www.oph.fi/english/curricula_and_qualifications/basic_education. Accessed 16 April 2015.

  • Neo, M., & Neo, T.-K. (2013). Exploring students’ creativity and design skills through a multimedia project: A constructivist approach in a Malaysian classroom. Design and Technology Education: An International Journal, 18(3), 48–59.

    Google Scholar 

  • Peltonen, J. (2003a). The chain of rational theories as the directing means of productive activities in academic Sloyd education. Techne Series A, 5, 78–96.

    Google Scholar 

  • Peltonen, J. (2003b). Handicraft education and truth. Considerations of Truth as a basis for research-oriented teaching in handicraft education. In M. Itkonen & G. Backhaus (eds) Lived images. Mediations in experience, life-world and i-hood (pp. 412–429). University of Jyväskylä: Department of Teacher Education.

  • Peltonen, M., & Ruohotie, P. (1992). Oppimismotivaatiot. Teoriaa, tutkimuksia ja esimerkkejä oppimishalukkuudesta. [Theory, research and examples of motivation towards learning]. Keuruu: Otava.

    Google Scholar 

  • Ryle, G. (1949). Concept of mind. London: Hutchinson.

    Google Scholar 

  • Sachs, J., & Leung, S. O. (2007). Shortened versions of Fennema–Sherman mathematic attitude scales employing trace information. Psychologika, 50(3), 224–235.

    Article  Google Scholar 

  • Sennett, R. (2008). The craftsman. New Haven, London: Yale University Press.

    Google Scholar 

  • Sherman, H. J., & Christian, M. (1999). Mathematics attitudes and global self-concept: An investigation of the relationship. College Student Journal, 33, 1–6.

    Google Scholar 

  • Statistics Finland. (2011). Survey of Leisure Time. Statistics Finland. https://www.stat.fi/til/akay/. Accessed 3 October 2013.

  • Stefanou, C. R., Perencevich, K. C., DiCintio, M., & Turner, J. C. (2004). Supporting autonomy in the classroom: Ways teachers engourage student decision making and ownership. Educational Psychologist, 39(2), 97–110.

    Article  Google Scholar 

  • Steiger, J. (1990). Structural model assessment and modification: An interval estimation approach. Multivariate Behaviour Research, 25, 173–180.

    Article  Google Scholar 

  • Steiger, J. (2000). Point estimation, hypothesis testing, and interval estimation using the RMSEA: Some comments and a reply to Hayduk and Glaser. Structural Equation Modeling, 7(2), 149–162.

    Article  Google Scholar 

  • Tapia, M., & Marsh, G. E. (2004). An instrument to measure mathematics attitudes. Academic Exchange Quarterly, 8(2), 16–21.

    Google Scholar 

  • Tucker, L., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10.

    Article  Google Scholar 

  • Ullman, J. (2001). Structural equation modelling. In B. Tabachnik & L. Fidell (Eds.), Using multivariate statistics (4th ed., pp. 653–771). Needham Heights: Allyn & Bacon.

    Google Scholar 

  • West, S., Taylor, A., & Wu, W. (2012). Model fit and model selection in structural equation modeling. In R. Hoyle (Ed.), Handbook of structural equation modeling (pp. 209–231). Newyork: The Guilford Press.

    Google Scholar 

  • Wikoff, R. L., & Buchalter, B. D. (2006). Factor analysis of four Fennema–Sherman mathematics attitude scales. International Journal of Mathematical Education in Science and Technology, 17(6), 703–706.

    Article  Google Scholar 

  • Zimmerman, B. J. (1998). Developing self-fulfilling cycles of academic regulation: An analysis of exemplary instructional models. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-Regulated Learning from teaching to self-reflective practice (pp. 1–19). New York, London: The Guilford Press.

    Google Scholar 

  • Zimmerman, B. J. (2011). Motivational sources and outcomes of Self-Regulated Learning and performance. In: B. J. Zimmerman & D. H. Schunk (eds) Handbook of self-regulation of learning and performance (pp. 49–64). New York: Routledge.

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Authors and Affiliations

  1. Faculty of Behavioural Sciences, University of Helsinki, Helsinki, Finland

    Manne Kallio

  2. Faculty of Education, University of Turku, Turku, Finland

    Mika Metsärinne

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  1. Manne Kallio
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  2. Mika Metsärinne
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Correspondence to Manne Kallio.

Appendix

Appendix

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Table 1 The descriptive statistics and factor loadings for each item, factor alphas
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Kallio, M., Metsärinne, M. How do different background variables predict learning outcomes?. Int J Technol Des Educ 27, 31–50 (2017). https://doi.org/10.1007/s10798-015-9339-7

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