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A path model of factors affecting secondary school students’ technological literacy

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Abstract

Technological literacy defines a competitive vision for technology education. Working together with competitive supremacy, technological literacy shapes the actions of technology educators. Rationalised by the dictates of industry, technological literacy was constructed as a product of the marketplace. There are many models that visualise different dimensions of technological literacy, but clear empirical evidence on how these interact is still lacking. A measurement method that comprehensively evaluates technological literacy is missing. Insights into the stem structure and interaction of technological literacy dimensions could be useful for technology education curriculum design and its implementation. In this study, the multifaceted nature of technological literacy was measured using a new assessment method, and dimensions of secondary school students’ technological literacy were empirically investigated. A total of 403 students participated in the quasi-experimental research design. The treatment group consisted of 121 students taught optional subjects relating to technology education. The control group consisted of 282 students. Results from variance analysis showed that optional technology subjects enhance technological literacy, especially students’ technological capacity where a large effect size (η 2 = 0.14) was noted. Results from a path analysis revealed critical thinking and decision-making as the most important dimensions of technological literacy while the predictor of active participation in out-of-school technical activities and technology homework was a key independent influencing factor. A large effect size (R 2 = 0.4) for career path orientation predictors was detected. Technological capacity was revealed as a decisive predictor for a career path in vocational education and technical high school.

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References

  • Arciszewski, T. (2016). Inventive engineering: Knowledge and skills for creative engineers. Boca Raton, London, New York, NY: CRC Press, Taylor & Francis Group.

    Google Scholar 

  • Asunda, P. A., & Hill, B. R. (2007). Critical features of engineering design in technology education. Journal of Industrial Technology Education, 44(1), 25–48.

    Google Scholar 

  • Avsec, S. (2012). Metoda merjenja tehnološke pismenosti učencev 9. razreda osnovne šole. Ljubljana: Univerza v Ljubljani. Retrived from http://pefprints.pef.uni-lj.si/663/.

  • Avsec, S., & Jamšek, J. (2016). Technological literacy for students aged 6–18: A new method for holistic measuring of knowledge, capabilities, critical thinking and decision-making. International Journal of Technology and Design Education, 26(1), 43–60. doi:10.1007/s10798-015-9299-y.

    Article  Google Scholar 

  • Avsec, S., & Szewczyk-Zakrzewska, A. (2015). Predicting academic success and technological literacy in secondary education: a learning styles perspective. International Journal of Technology and Design Education,. doi:10.1007/s10798-015-9344-x.

    Google Scholar 

  • Benson, C. (2012). The developemnt of quality design and technlogy in english primary schools: issues and solutions. In T. Ginner, J. Hallström, & M. Hulten (Eds.), Technology education in the 21st century, the PATT26 conference. Stockholm, Linkoping: Linkoping Universty, CETIS.

    Google Scholar 

  • Blest, D. C. (2003). A new measure of kurtosis adjusted for skewness. Australian and New Zealand Journal of Statistics, 45, 175–179.

    Article  Google Scholar 

  • Blunch, N. (2013). Introduction to structural equation modeling using SPSS and Amos. London: Sage Publications Ltd.

    Book  Google Scholar 

  • Bordens, K. S., & Abbott, B. B. (2011). Research design and methods: A process approach. New York, NY: McGraw-Hill.

    Google Scholar 

  • Bryman, A., & Cramer, D. (2011). Quantitative data analysis with SPSS 17, 18 and 19. A guide for social scientists. East Sussex: Routledge.

    Google Scholar 

  • Castillo, M. (2010). Technological literacy: Designing and testing an instrument to measure eighth-grade achievement in technology education. Paper presented at the meeting of the American Society for Engineering Education, Louisville, KY: Chapman and Hall/CRC.

  • Childress, V. W., & Rhodes, C. (2008). Engineering outcomes for grades 9–12. The Technology Teacher, 67(7), 5–12.

    Google Scholar 

  • Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: L. Erlbaum.

    Google Scholar 

  • Cox, N. J. (2010). Speaking stata: The limits of sample skewness and kurtosis. The Stata Journal, 10(3), 482–495.

    Google Scholar 

  • David, H. A., & Nagaraja, H. N. (2003). Order statistics. Hoboken, NJ: Wiley.

    Book  Google Scholar 

  • De Miranda, M. A. (2004). The grounding of a discipline: Cognition and instruction in technology education. International Journal of Technology and Design Education, 14, 61–77.

    Article  Google Scholar 

  • de Vries, M. J. (2006). Technological knowledge and artefacts: An analytical view. In J. R. Dakers (Ed.), Defining technological literacy: Towards an epistemological framework (pp. 17–30). New York, NY: Palgrave Macmillan.

    Chapter  Google Scholar 

  • Eisenkraft, A. (2010). Retrospective analysis of technological literacy of K-12 students in the USA. International Journal of Technology and Design Education, 20, 277–303.

    Article  Google Scholar 

  • Eng, J. (2003). Sample size estimation: How many individuals should be studied? Radiology, 227, 309–313.

    Article  Google Scholar 

  • Field, A. P. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). London: Sage.

    Google Scholar 

  • Frank, M. (2005). A systems approach for developing technological literacy. Journal of Technology Education, 17(1), 19–34.

    Article  Google Scholar 

  • Gagel, W. C. (2004). Technology profile: An assessment strategy for technological literacy. The Journal of Technology Studies, 30(4), 38–44.

    Google Scholar 

  • Garmire, E., & Pearson, G. (Eds.). (2006). Tech tally: Approaches to assessing technological literacy. Washington, DC: National Academies Press.

    Google Scholar 

  • Gilchrist, W. G. (2000). Statistical modelling with quantile functions. Boca Raton, FL: Chapman and Hall/CRC Press.

    Book  Google Scholar 

  • Gliner, J. A., & Morgan, G. A. (2000). Research methods in applied settings: An integrated approach to design and analysis. Mahwah, NJ: Erlbaum.

    Google Scholar 

  • Hayes, S. C. (2002). Acceptance, mindfulness, and science. American Psychological Association, D12, 101–105.

    Google Scholar 

  • Heppner, P. P., Heppner, M. J., Lee, D., Wang, Y., Park, H., & Wang, L. (2006). Development and validation of a collectivist coping style inventory. Journal of Counselling Psychology, 53(1), 107–125.

    Article  Google Scholar 

  • Hilton, J. K. (2006). The effect of technology on student science achievement. In E. Alkhalifa (Ed.), Cognitively informed systems: Utilizing practical approaches to enrich information presentation and transfer, pp. 312–333.

  • Holland, S. (2004). Attitudes toward technology and development of technological literacy of gifted and talented elementary school students. (Electronic Thesis or Dissertation). Accessed 20 June 2016 from https://etd.ohiolink.edu/.

  • Holmes-Smith, P., Coote, L., & Cunningham, E. (2005). Structural equation modeling: From the fundamentals to advanced topics. Melbourne: School Research, Evaluation and Measurement Services.

    Google Scholar 

  • Hosking, J. R. M. (2006). On the characterization of distributions by their L-moments. Journal of Statistical Planning and Inference, 136, 193–198.

    Article  Google Scholar 

  • Ingerman, A., & Collier-Reed, B. (2011). Technological literacy reconsidered: A model for enactment. International Journal for Technology and Design Education, 21, 137–148.

    Article  Google Scholar 

  • International Technology Education Association/International Technology and Engineering Educators Association. (2007). Standards for technological literacy: Content for the study of technology (3rd ed.). Reston, VA: Author.

    Google Scholar 

  • Kaiser, H. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36.

    Article  Google Scholar 

  • Kelley, T. R. (2008). Cognitive processes of students participating in engineering. Journal of Technology Education, 19, 50–64.

    Google Scholar 

  • Kelley, T. R., & Wicklein, R. C. (2009). Examination of assessment practices for engineering design projects in secondary education. Journal of Industrial Teacher Education, 46(2), 6–25.

    Google Scholar 

  • Klapwijk, R., & Rommes, E. (2009). Career orientation of secondary school students (m/f) in the Netherlands. International Journal of Technology and Design Education, 19, 403–418.

    Article  Google Scholar 

  • Krathwohl, D. R. (2002). A revision of Bloom’s taxonomy: An overview. Theory into Practice, 41, 213–217.

    Article  Google Scholar 

  • Kubiszyn, T., & Borich, G. D. (2013). Educational testing and measurement: Classroom application and practise. Hoboken, NJ: Willey.

    Google Scholar 

  • Lewis, T. (2005). Coming to terms with engineering design as content. Journal of Technology Education, 16(2), 37–54.

    Article  Google Scholar 

  • Luckay, M. B., & Collier-Reed, B. I. (2014). An instrument to determine the technological literacy levels of upper secondary school students. International Journal of Technology and Design Education, 24(3), 261–273.

    Article  Google Scholar 

  • Lutz, S., & Huitt, W. (2004). Connecting cognitive development and constructivism: Implications from theory for instruction and assessment. Constructivism in the Human Sciences, 9(1), 67–90.

    Google Scholar 

  • Mawson, B. (2006). Factors affecting learning in technology in the early years at school. International Journal of Technolgy and Design Education, 17, 253–269.

    Article  Google Scholar 

  • McMillan, J. H., & Schumacher, S. (2001). Research in education: A conceptual introduction. Boston, MA: Allyn and Bacon.

    Google Scholar 

  • Moore, G., Raucent, B., Hernandez, A., Bourret, B., & Marre, D. (2005). What can teachers learn from what students say about PBL? In E. de Graaff, G. Saunders-Smits, & M. Nieweg (Eds.), Research and practice of active learning in engineering education (pp. 19–26). Amsterdam, NL: Amsterdam University Press.

    Google Scholar 

  • Odom, L. R., & Morrow, J. R. (2006). What’s this r? A correlational approach to explaining validity, reliability and objectivity coefficients. Measurement in Physical Education and Exercise Science, 10(2), 137–145.

    Article  Google Scholar 

  • Petrina, S. (2007). Advanced teaching methods for the technology classroom. Hershey: Information Science Publishing.

    Book  Google Scholar 

  • Petrina, S., Feng, F., & Kim, J. (2007). Researching cognition and technology: How we learn across the lifespan. International Journal of Technology and Design Education, 18, 375–396.

    Article  Google Scholar 

  • Rohaan, E. J., Taconis, R., & Jochems, W. M. G. (2010). Analysing teacher knowledge for technology education in primary schools. International Journal of Technology and Design Education,. doi:10.1007/s10798-010-9147-z.

    Google Scholar 

  • Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23–74.

    Google Scholar 

  • Schunk, D. H. (2009). Learning theories: An educational perspective. New Jersey: Pearson Int. Press.

    Google Scholar 

  • Schunn, C. D., & Silk, E. M. (2011). Learning theories for engineering and technology education. Fostering Human Development Through Engineering and Technology Education, International Technology Education Studies, 6, 3–18.

    Article  Google Scholar 

  • Shadish, W. S., Cook, T. C., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston, MA: Houghton Mifflin Co.

    Google Scholar 

  • Shumway, S. L., Saunders, W., Stewardson, G., & Reeve, E. (2001). A comparison of cooperative–cooperative and cooperative-competitive goal structures and their effect on group problem-solving performance and student attitudes toward their learning environment. Journal of Industrial Teacher Education, 38(3), 6–24.

    Google Scholar 

  • Stevens, J. (2009). Applied multivariate statistics for the social sciences (5th ed.). New York, NJ: Routledge, Taylor and Francis Group.

    Google Scholar 

  • Szewczyk-Zakrzewska, A., & Avsec, S. (2016). Predicting academic success and creative ability in freshman chemical engineering students: a learning styles perspective. International Journal of Engineering Education, 32(2A), 682–694.

    Google Scholar 

  • Taylor, J. S. (2006). Student perceptions of selected technology student association activities. Journal of Technology Education, 17(2), 56–71.

    Article  Google Scholar 

  • Thorsteinsson, G., & Olafsson, B. (2015). Piloting technological understanding and reasoning in Icelandic schools. International Journal of Technology and Design Education,. doi:10.1007/s10798-015-9301-8.

    Google Scholar 

  • Virtanen, S., Raikkonen, E., & Ikonen, P. (2015). Gender-based motivational differences in technology education. International Journal of Technology and Design Education, 25, 197–211. doi:10.1007/s10798-014-9278-8.

    Article  Google Scholar 

  • Weir, J. P. (2005). Quantifying test–retest reliability using the intraclass correlation coefficient. Journal of Strength and Conditioning Research, 19(1), 231–240.

    Google Scholar 

  • Wells, J. G. (2016). Efficacy of the technological/engineering design approach: Imposed cognitive demands within design-based biotechnology instruction. Journal of Technology Education, 27(2), 4–20.

    Google Scholar 

  • Wells, J. G., Lammi, M., Gero, J., Grubbs, M. E., Paretti, M., & Williams, C. (2016). Characterizing design cognition of high school students: Initial analyses comparing those with and without pre-engineering experiences. Journal of Technology Education, 27(2), 78–91.

    Google Scholar 

  • Whitley, E., & Ball, J. (2002). Statistics review 4: Sample size calculations. Critical Care, 6(4), 335–341.

    Article  Google Scholar 

  • Wicklein, R. C. (2006). Five good reasons for engineering as the focus for technology education. The Technology Teacher, 65(7), 25–29.

    Google Scholar 

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Avsec, S., Jamšek, J. A path model of factors affecting secondary school students’ technological literacy. Int J Technol Des Educ 28, 145–168 (2018). https://doi.org/10.1007/s10798-016-9382-z

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