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A Model of STEM Learning and Career Orientation Based on Social Cognitive Theory
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, , , University of Nebraska-Lincoln, United States

Society for Information Technology & Teacher Education International Conference, in Jacksonville, Florida, United States ISBN 978-1-939797-07-0 Publisher: Association for the Advancement of Computing in Education (AACE), Chesapeake, VA

Abstract

This research examined motivational and psychological processes underlying middle school youth science, technology, engineering, and mathematics (STEM) learning and career orientation. The study was conducted within the framework of summer robotics camps, which provided an ideal context because educational robotics is an integrative technology platform that draws upon all of the STEM disciplines. Structural equation modeling procedures were used to test the proposed path model. Consistent with social cognitive theory, youth STEM learning appears to be fostered by their self-efficacy and interest in these subjects. STEM career orientation is influenced by youth expected outcomes for such careers, with interest again playing an indirect, mediating role. The influence of the educator was more potent than that of peers or family in impacting youth STEM interest, showing that teachers' influence can extend beyond learning to promotion of interest in pursuing a STEM career.

Citation

Nugent, G., Barker, B. & Welch, G. (2014). A Model of STEM Learning and Career Orientation Based on Social Cognitive Theory. In M. Searson & M. Ochoa (Eds.), Proceedings of SITE 2014--Society for Information Technology & Teacher Education International Conference (pp. 1432-1440). Jacksonville, Florida, United States: Association for the Advancement of Computing in Education (AACE). Retrieved March 22, 2019 from .

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References

  1. AUTHORS (2009). Measuring the impact of robotics and geospatial technologies on youth science, technology, engineering, and mathematics attitudes. Proceedings of the World Conference on Educational Multimedia, Hypermedia, and Telecommunications, Honolulu, Hawaii.
  2. AUTHORS (2012). The impact of educational robotics on student STEM learning, attitudes and workplace skills. In Barker, Nugent, Grandgenett, & Adamchuk (Eds.), Robotics in K−12 education: A new technology for learning. Hershey, PA: IGI Global.
  3. Bandura, A. (1977). Self-efficacy. The exercise of control. New York: Freeman.
  4. Bandura, A. (1986) Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.
  5. Bandura, A., Barbaranelli, C., Caprara, G., & Postorelli, C. (2001). Self-efficacy beliefs as shapers of children’s aspirations and career trajectories. Child Development, 72, 187 – 206.
  6. Bell, P., Lewenstein, B., Shouse, A., & Feder, M. (2009). Learning science in informal environments. Washington DC: National Academies Press.
  7. Bong, M., & Skaalvik, E. (2003). Academic self-concept self-efficacy: how different are they really? Educational Psychology, 15, 1-40
  8. Cleaves, A. (2005). The formation of science choices in secondary school. International Journal of Science Education, 27, 471-486.
  9. DeBacker, T.K., & Nelson, R.M. (1999). Variations on an expectancy-value model of motivation in science. Contemporary Educational Psychology, 24, 71-94.
  10. Graham, J., Taylor, B., Olchowski, A., & Cumsille, P. (2006). Planned missing data design in psychological research. Psychological Methods, 11, 323 – 343.
  11. Hu, L. & Bentler, P.M. (1999). Cutoff criteria for fit Indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.
  12. Lent, R., Brown, S., Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45, 79-122.
  13. Liu, M., Hsieh, P.P., Cho, Y., & Schallert, D. (2006). Middle school students’ self-efficacy, attitudes, and achievement in a computer-enhanced problem-based learning environment. Journal of Interactive Learning Research, 17, 225-242.
  14. MacCallum, R.C., Browne, M.W., & Sugawara, H.M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1, 130-149.
  15. National Academy of Sciences. (2010). Rising above the gathering storm, revisited: rapidly approaching category 5. Washington, DC: The National Academies Press.
  16. National Research Council. (2010). Standards for K-12 engineering education? Washington, DC: The National Academies Press.
  17. Nauta, M., Kahn, J., Angell, J., & Cantarelli, E. (2002). Identifying the antecedent in the relation between career interests and self-efficacy: Is it one, the other, or both? Journal of Counseling Psychology, 49, 290 – 301.
  18. Olitsky, S., Loman, L.F., Gardner, J., & Billip, M. (2010). Coherence, contradiction, and the development of school science identities. Journal of Research in Science Teaching, 47, 1209 – 1228.
  19. Organisation for Economic Co-Operation and Development. (2007). Education at a glance 2007: OECD indicators. Paris, France: Author.
  20. Parker, P., Marsh, H., Ciarrochi, J., Marshall, S., & Abduljabbar, A.S. (2013). Juxtaposing math self-efficacy and self-concept as predictors of long-term achievement outcomes. Educational Psychology: An international journal of experimental educational psychology.
  21. Pintrich, P.R., & De Groot, E.V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82, 33 – 40.
  22. Pintrich, P., Smith, D., Garcia, T., & McKeachie, W. (1991). A manual for the use of the motivated strategies for learning questionnaire. Ann Arbor: University of Michigan.
  23. Rice, J.K. (2001). Explaining the negative impact of the transition from middle to high school on student performance in mathematics and science. Educational Administration Quarterly, 37, 372-400.
  24. Ryan, A.M. (2001). The peer group as a context for the development of young adolescent motivation and achievement. Child Development, 72, 261 – 266.
  25. Schafer, J.L. (1997). Analysis of incomplete multivariate data. New York: Chapman& Hall.
  26. Schunk, D.H. (1983). Reward contingencies and the development of children’s skills and self-efficacy. Journal of Educational Psychology, 75, 511-518.
  27. Sorge, C. (2007). What happens: relationship of age and gender with science attitudes from elementary to middle school. Science Educator, 16, 33 – 37.
  28. Tenenbaum, H., & Leaper, C. (2003). Parent-child conversations about science: The socialization of gender iniquities? Developmental Psychology, 39, 34 – 47.
  29. Usher, E., & Pajares, F. (2008). Sources of self-efficacy in school: critical review of the literature and future directions. Review of Educational Research, 78, 751 – 795.
  30. Vedder-Weiss, D., & Fortus, D. (2013) School, teacher, and parents’ goals emphases and adolescents’ motivation to learn science in and out of school. Journal of Research in Science Teaching, 50, 952 – 988.
  31. Wang, D. (2004). Family background factors and mathematics success: A comparison of Chinese and US students. International Journal of Education Research, 41, 40-54.

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