Factors that Influence Students in Choosing Physics Programmes at University Level: the Case of Greece

  • Kalliopi Meli
  • Konstantinos Lavidas
  • Dimitrios Koliopoulos
Article
  • 11 Downloads

Abstract

Low enrolment in undergraduate level physics programmes has drawn the attention of the relevant disciplines, education policy-makers, and researchers worldwide. Many reports released during the previous decades attempt to identify the factors that attract young people to study science, but only few of them focus explicitly on physics. In Greece, in contrast to many other countries, physics departments are overflowing with young students. However, there are two categories of students: those for whom physics was the optimal choice of a programme (“choosers”) and those for whom physics was an alternative choice that they had to settle for. We suggest that the latter category be called “nearly-choosers,” in order to be differentiated from choosers as well as from “non-choosers,” namely those candidates that did not apply to a physics programme at all. We are interested in the factors that attract high school students to study physics and the differences (if any) between choosers and nearly-choosers. A newly formed questionnaire was distributed within a Greek physics department (University of Patras), and the students’ responses (n = 105) were analysed with exploratory factor analysis and specifically principal component analysis so as to extract broad factors. Three broad factors have arisen: school-based, career, and informal learning. The first two factors proved to be motivating for pursuing a degree in physics, while the third factor appeared to have a rather indifferent association. t tests and Pearson correlations indicated mild differentiations between choosers and nearly-choosers that pertain to school-based influences and informal learning.

Keywords

Physics education Higher education Motivation Inspiring factors 

Notes

Acknowledgements

We would like to thank our colleagues from the Physics Department of University of Patras, Evagelos Vitoratos, and Ekaterini Pomoni, for assisting us with the distribution of the questionnaire, as well as our partners from HOPE Network for inspiring us to work on this research field.

References

  1. Abraham, J., & Barker, K. (2015). An expectancy-value model for sustained enrolment intentions of senior secondary physics students. Research in Science Education, 45(4), 509–526.CrossRefGoogle Scholar
  2. Adamuti-Trache, M., & Andres, L. (2008). Embarking on and persisting in scientific fields of study: cultural capital, gender, and curriculum along the science pipeline. International Journal of Science Education, 30(12), 1557–1584.CrossRefGoogle Scholar
  3. Archer, L., Osborne, J., DeWitt, J., Dillon, J., Wong, B., & Willis, B. (2013). ASPIRES: young people’s science and career aspirations, age 10–14. London: King’s College.Google Scholar
  4. Caprile, M., Palmén, R., Sanzé, P., & Dente, G. (2015). Encouraging STEM studies: labour market situation and comparison of practices targeted at young people in different member states. Luxembourg: Publications Office of the European Union.Google Scholar
  5. Cleaves, A. (2005). The formation of science choices in secondary school. International Journal of Science Education, 27(February 2014), 471–486.CrossRefGoogle Scholar
  6. Cummins, R. A., & Gullone, E. (2000). Why we should not use 5-point Likert scales: the case for subjective quality of life measurement. In Proceedings Second International Conference on Quality of Life in Cities (pp. 74–93). Singapore: National University of Singapore. Retrieved from http://vhost47.hosted-sites.deakin.edu.au/iwbg/wellbeing-index/qol-in-cities-likert-scales-2000.docGoogle Scholar
  7. Dainton, S. F. (1968). Enquiry into the flow of candidates in science and technology into higher education. London.Google Scholar
  8. De Maesschalck, R., Jouan-Rimbaud, D., & Massart, D. L. (2000). The Mahalanobis distance. Chemometrics and Intelligent Laboratory Systems, 50(1), 1–18.CrossRefGoogle Scholar
  9. Field, A. (2009). Discovering statistics using SPSS (3rd ed.). Los Angeles: SAGE Publications, Inc..Google Scholar
  10. Fouad, N. A., Hackett, G., Smith, P. L., Kantamneni, N., Fitzpatrick, M., Haag, S., & Spencer, D. (2010). Barriers and supports for continuing in mathematics and science: gender and educational level differences. Journal of Vocational Behavior, 77(3), 361–373.CrossRefGoogle Scholar
  11. Franzen, M. D. (2013). Reliability and validity in neuropsychological assessment. New York: Springer Science+Business Media.Google Scholar
  12. Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-graph: tutorial and annotated example. Communications of the Association for Information Systems, 16, 91–109. Retrieved from https://pdfs.semanticscholar.org/a287/0e379cbff593811b8b918ba6323c12ac7d83.pdf
  13. Gill, T., & Bell, J. F. (2013). What factors determine the uptake of A-level physics? International Journal of Science Education, 35(5), 753–772.CrossRefGoogle Scholar
  14. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks: Sage.Google Scholar
  15. Harris, K., & Farrell, K. (2007). The science shortfall: an analysis of the shortage of suitably qualified science teachers in Australian schools and the policy implications for universities. Journal of Higher Education Policy and Management, 29(2), 159–171.CrossRefGoogle Scholar
  16. Hazari, Z., Sonnert, G., Sadler, P. M., & Shanahan, M.-C. (2010). Connecting high school physics experiences, outcome expectations, physics identity, and physics career choice: a gender study. Journal of Research in Science Teaching, 47(8), 978–1003.Google Scholar
  17. Hazari, Z., Potvin, G., Tai, R. H., & Almarode, J. T. (2012). Motivation toward a graduate career in the physical sciences: gender differences and the impact on science career productivity. Journal of College Science Teaching, 41, 90–98.Google Scholar
  18. Henriksen, E. K., Dillon, J., & Ryder, J. (Eds.). (2015). Understanding student participation and choice in science and technology education. Dordrecht: Springer.Google Scholar
  19. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.CrossRefGoogle Scholar
  20. Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2–20.CrossRefGoogle Scholar
  21. Holmegaard, H. T., Madsen, L. M., & Ulriksen, L. (2014). To choose or not to choose science: constructions of desirable identities among young people considering a STEM higher education programme. International Journal of Science Education, 36(December 2013), 186–215.CrossRefGoogle Scholar
  22. Jones, G., & Trippenbach, M. (2016). Inspiring the young to study physics. Paris. Retrieved from http://www.hopenetwork.eu/content/final-report-wg1
  23. Kerr, K., & Murphy, C. (2012). Children’s attitudes to primary science. In B. J. Fraser, K. Tobin, & C. J. McRobbie (Eds.), Second International Handbook of Science Education (pp. 627–649). Springer.Google Scholar
  24. Levrini, O., De Ambrosis, A., Hemmer, S., Laherto, A., Malgieri, M., Pantano, O., & Tasquier, G. (2017). Understanding first-year students’ curiosity and interest about physics—lessons learned from the HOPE project. European Journal of Physics, 38(2), 25701.Google Scholar
  25. Lyons, T. (2006). Different countries, same science classes: students’ experiences of school science in their own words. International Journal of Science Education, 28(6), 591–613.CrossRefGoogle Scholar
  26. Lyons, T., & Quinn, F. (2010). Understanding the declines in senior high school science enrolments. Retrieved from http://www.une.edu.au/simerr/pages/projects/131choosingscience.pdf
  27. Maltese, A. V., & Tai, R. H. (2010). Eyeballs in the fridge: sources of early interest in science. International Journal of Science Education, 32(5), 669–685.CrossRefGoogle Scholar
  28. Martin, M. O., Mullis, I. V., & Foy, P. (2008). TIMSS 2007 international science report findings from IEA’s trends in international mathematics and science study at the fourth and eighth grades. Chestnut Hill: TIMSS & PIRLS International Study Center, Boston College.Google Scholar
  29. Masnick, A. M., Valenti, S. S., Cox, B. D., & Osman, C. J. (2010). A multidimensional scaling analysis of students’ attitudes about science careers. International Journal of Science Education, 32(5), 653–667.CrossRefGoogle Scholar
  30. Ministry of Education Research and Religious Affairs. (2014). Preference statistics 2014. Retrieved from http://www.minedu.gov.gr/publications/docs2014/Statistics_Preferences_2014.zip
  31. National Science Board. (2008). Science and engineering indicators 2008. In National Science Foundation. (Vol 1). Arlington: National Science Foundation.Google Scholar
  32. OECD. (2016). PISA 2015 results (volume I): excellence and equity in education, PISA, (Vol. I). Paris: PISA, OECD Publishing.Google Scholar
  33. Office of the Chief Scientist. (2012). Health of Australian science. Canberra. Retrieved from https://docs.google.com/viewer?docex=1&url=http://www.chiefscientist.gov.au/wp-content/uploads/HASReport_Web-Update_200912.pdf
  34. Osborne, J., Simon, S., & Collins, S. (2003). Attitudes towards science: a review of the literature and its implications. International Journal of Science Education, 25(January 2014), 1049–1079.CrossRefGoogle Scholar
  35. Papas, G., & Psacharopoulos, G. (1987). The transition from school to the university under restricted entry: a Greek tracer study. Higher Education, 16(4), 481–501.CrossRefGoogle Scholar
  36. Papas, G., & Psacharopoulos, G. (1991). The determinants of educational achievement in Greece. Studies in Educational Evaluation, 17(2–3), 405–418.CrossRefGoogle Scholar
  37. Pett, M. A., Lackey, N. R., & Sullivan, J. J. (2003). Making sense of factor analysis: the use of factor analysis for instrument development in health care research. Thousand Oaks: SAGE Publications, Inc..CrossRefGoogle Scholar
  38. Preston, C. C., & Colman, A. M. (2000). Optimal number of response categories in rating scales: reliability, validity, discriminating power, and respondent preferences. Acta Psychologica, 104(1), 1–15.CrossRefGoogle Scholar
  39. Pronovost, M., Cormier, C., Potvin, P., & Riopel, M. (2016). Interest and disinterest from college students for higher education in sciences. In M. Riopel & Z. Smyrnaiou (Eds.), New developments in science and technology education (pp. 41–49). Cham: Springer International Publishing.CrossRefGoogle Scholar
  40. Reiss, M., Hoyles, C., Mujtaba, T., Riazi-Farzad, B., Rodd, M., Simon, S., & Stylianidou, F. (2011). Understanding participation rates in post-16 mathematics and physics: conceptualising and operationalising the UPMAP project. International Journal of Science and Mathematics Education, 9, 273–302.CrossRefGoogle Scholar
  41. Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS 3. Bönningstedt: SmartPLS. Retrieved June 10, 2017, from http://www.smartpls.com
  42. Rodd, M., Reiss, M., & Mujtaba, T. (2013). Undergraduates talk about their choice to study physics at university: what was key to their participation? Research in Science & Technological Education, 31(February 2015), 153–167.CrossRefGoogle Scholar
  43. Schreiner, C., & Sjøberg, S. (2010). The ROSE project: an overview and key findings, March, 1–31.Google Scholar
  44. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston: Pearson Education, Inc. Retrieved from http://content.apa.org/reviews/022267
  45. Tytler, R., & Osborne, J. (2012). Student attitudes and aspirations towards science. In B. J. Fraser, K. Tobin, & C. J. McRobbie (Eds.), Second international handbook of science education SE-41 (Vol. 24, pp. 597–625). Netherlands: Springer.CrossRefGoogle Scholar
  46. Venville, G., Rennie, L., Hanbury, C., & Longnecker, N. (2013). Scientists reflect on why they chose to study science. Research in Science Education, 43, 2207–2233.CrossRefGoogle Scholar
  47. Waltz, C. F., Strickland, O. L., & Lenz, E. R. (2010). Measurement in nursing and health research. New York: Springer Publishing Company.Google Scholar
  48. Wang, H.-H. (2004). Why teach science? Graduate science students’ perceived motivations for choosing teaching as a career in Taiwan. International Journal of Science Education, 26(August 2014), 113–128.CrossRefGoogle Scholar
  49. Woodrow, D. (1996). Cultural inclinations towards studying mathematics and sciences. New Community, 22, 23–38.Google Scholar
  50. Woolnough, B. E. (1990). In B. E. Woolnough (Ed.), Making choices: an enquiry into the attitudes of sixth-formers towards choice of science and technology courses in higher education. Oxford: Oxford University Department of Educational Studies.Google Scholar
  51. Woolnough, B. E. (1991). The making of engineers and scientists: factors affecting schools’ success in producing engineers and scientists. Oxford: Oxford University Department of Educational Studies.Google Scholar
  52. Woolnough, B. E. (1994a). Factors affecting students’ choice of science and engineering. International Journal of Science Education, 16(February 2015), 659–676.CrossRefGoogle Scholar
  53. Woolnough, B. E. (1994b). Why students choose physics, or reject it. Physics Education, 29, 368–374.CrossRefGoogle Scholar
  54. Zumbo, B. D. (2007). Validity: Foundational issues and statistical methodology. In C. R. Rao & S. Sinharay (Eds.), Handbook of statistics, Vol. 26: Psychometrics (pp. 45–79). Amsterdam: Elsevier Science.Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Educational Sciences and Early Childhood EducationUniversity of PatrasRioGreece

Personalised recommendations