Factors that influence students’ decisions to pursue engineering as a major for students with different mathematics abilities
Abstract
This multimethod study explores the factors that affect college student motivation for selecting an engineering major, especially for students who have different levels of mathematics abilities and preparation in high school. Data from 560 students were collected at a U.S. public university, focusing on the personal high school academic experiences of those students during high school. This information was analyzed using inferential statistics where students were separated in two groups based on mathematics abilities (high and low) and compared in terms of their decision to pursue an engineering major. Being a male student was found to be the most influential factor for choosing an engineering major, followed by having a strong mathematics preparation in high school; these results were more pronounced for the low mathematics ability group. Findings also showed that participants in the high mathematics ability group were more motivated by interest in engineering and its applications, while participants in the low mathematics ability group were more motivated for the money and social status that getting an engineer career could bring them.
Downloads
References
Aronson, J., Fried, C. B. and Good, C. (2002). Reducing the effects of stereotype threat on African American college students by shaping theories of intelligence. Journal of Experimental Social Psychology, 38(2), 113-125.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. New Jersey, United States: Prentice Hall.
Betz, N. E. and Hackett, G. (1983). The relationship of mathematics self-efficacy expectations to the selection of science-based college majors. Journal of Vocational Behavior, 23(3), 329-345.
Brown, S. and Burnham, J. (2012). Engineering student’s mathematics self-efficacy development in a freshmen engineering mathematics course. International Journal of Engineering Education, 28(1), 113-129.
Centro Nacional de Evaluación para la Educación Superior [Ceneval]. (2017). Exámenes Diagnósticos. México: Centro Nacional de Evaluación para la Educación Superior. Recuperado de http://www.ceneval.edu.mx/examenes-de-diagnostico.
Collett, D. (1991). Modelling binary data. London, England: Chapman and Hall.
Committee on STEM Education. (2013). Federal Science, Technology, Engineering, and Mathematics (STEM) Education. 5-Year Strategic Plan. D.C., United States: National Science and Technology Council. Retrieved from http://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/stem_stratplan_2013.pdf.
Consejo Nacional de Ciencia y Tecnología [Conacyt]. (2014). Programa Especial de Ciencia, Tecnología e Innovación 2014-2018. México: Consejo Nacional de Ciencia y Tecnología. Recuperado de http://www.conacyt.gob.mx/images/conacyt/PECiTI_2014-2018.pdf.
Creswell, J. W. (2009). Research design: Qualitative, quantitative and mixed methods research. California, United States: Sage Publications.
Creswell, J. W. and Clark, V. (2011). Designing and conducting mixed methods research. California, United States: Sage Publications.
Cunningham, C. and Knight, M. (2004). Draw an engineer test: Development of a tool to investigate students’ ideas about engineers and engineering. Paper presented at the 2004 American Society for Engineering Education Annual Conference and Exposition. Salt Lake City, Utah, June 20-23, 2004.
Dillman, D. A. (2007). Mail and internet surveys: The tailored design method. New York, United States: Wiley.
Erdmann, V. and Schumann, T. (2010). European Engineering Report. Koln, Alemania: Institut der deutschen wirtschaft. Retrieved from https://www.vdi.de/uploads/media/2010-04_IW_European_Engineering_Report_02.pdf.
Eris, O., Chachra, D., Chen, H. L., Sheppard, S., Ludlow, L. and Rosca, C. (2010). Outcomes of a longitudinal administration of the persistence in engineering survey. Journal of Engineering Education, 99(4), 371-395.
Falk, R. and Well, A. V. (1997). Many faces of the correlation coefficient. Journal of Statistics Education, 5. Retrieved from http://www.amstat.org/publications/jse/v5n3/falk.html.
Gobierno de la República. (2013). Plan Nacional de Desarrollo 2013-2018. México: Gobierno de la República. Recuperado de http://pnd.gob.mx/wp-content/uploads/2013/05/PND.pdf.
Goetz, T., Bieg, M., Lüdtke, O., Pekrun, R. and Hall, N. C. (2013). Do girls really experience more anxiety in mathematics? Psychological Science, 24(10), 2079-2087.
Hackett, G. (1985). Role of mathematics self-efficacy in the choice of math-related majors of college women and men: A path analysis. Journal of Counseling Psychology, 32(1), 47-56.
Hackett, G. and Betz, N. E. (1989). An exploration of the mathematics self-efficacy/mathematics performance correspondence. Journal for Research in Mathematics Education, 20(3), 261–273.
Hayes, A. F. (2013). Methodology in the social sciences. Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York, United States: Guilford Press.
Kokkelenberg, E. C. and Sinha, E. (2010). Economics of education review who succeeds in STEM studies. An analysis of Binghamton University undergraduate students. Economics of Education Review, 29(6), 935-946.
Lent, R., Lopez, F. and Bieschke, K. (1991). Mathematics self- efficacy sources and relation to science-based career choice. Journal of Counseling Psychology, 38(4), 424-430.
Lent, R. W., Brown, S. D. and Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79-122.
Levin, J. and Wyckoff, J. (1988). Effective advising: Identifying students most likely to persist and succeed in engineering. Engineering Education, 78(11), 178–182.
National Academy of Engineering. (2015). Introduction to the grand challenges for engineering. Washington, D.C. Recuperado de http://www.engineeringchallenges.org/challenges/16091.aspx
Nicholls, G. M., Wolfe, H., Besterfield, M., Shuman, L. J. and Larpkiattaworn, S. (2007). A method for identifying variables for predicting STEM enrollment. Journal of Engineering Education, 96(1), 33–44.
Ohland, M. W., Brawner, C. E., Camacho, M. M., Layton, R. A., Long, R. A., Lord, S. M. and Wasburn, M. H. (2011). Race, gender, and measures of success in engineering education. Journal of Engineering Education, 100(2), 225–252.
Organización para la Cooperación y el Desarrollo Económicos [OCDE]. (2014). PISA 2012 Results: What students know and can do – student performance in mathematics, reading and science. Paris, France: OECD Publishing.
Paderewski, P., García-Arenas, M. I., Gil, R. M., González, C. S., Ortigosa, E. M. and Padilla, N. (2017). Initiatives and strategies to encourage women into engineering. IEEE Revista Iberoamericana de Tecnologías del Aprendizaje, 12(2), 106–114.
Porter, C. H. (2011). An examination of variables which influence high school students to enroll in an undergraduate engineering or physical science major. (doctoral thesis). Clemson University, Clemson.
R Development Core Team. (2012). R: A language and environment for statistical computing. Vienna, Germany: R Foundation for Statistical Computing. Retrieved from https://www.gbif.org/tool/81287/r-a-language-and-environment-for-statistical-computing.
Rovine, M. L. and Von Eye, A. (1997). A 14th way to look at a correlation coefficient: Correlation as the proportion of matches. American Statistician, 51, 42-46.
Sadler, P. M., Sonnert, G., Hazari, Z. and Tai, R. (2012). Stability and volatility of STEM career interest in high school: A gender study. Science Education, 96, 411-427.
Saldaña, J. (2009). The Coding Manual for Qualitative Researchers. London, England: SAGE.
Sandelowski, M., Voils, C. I. and Knafl, G. (2009). On quantitizing. Journal of Mixed Methods Research, 3(3), 208-222.
Secretaría de Educación Pública [Sep]. (2007). Programa sectorial de educación 2007-2012. México: Secretaría de Educación Pública.
Seymour, E. and Hewitt, N. M. (1997). Talking about leaving: Why undergraduates leave the sciences. Colorado, United States: Westview Press.
In order to promote the development and dissemination of research in education in Latin America, the Ibero-American Journal for Educational Research and Development (RIDE) adhered to the Budapest Open Access Initiative, which is why it is identified as a Open access publication. This means that any user can read the complete text of the articles, print them, download them, copy them, link them, distribute them and use the contents for other purposes. Creative Cummons licenses allow users to specify the rights to use an open access journal available on the Internet in such a way that users know the rules of publication. Authors who publish in this journal accept the following conditions: Authors they keep the author's rights and give the magazine the right of the first publication, with the work registered with the attribution license of Creative Commons, which allows third parties to use the published material whenever they mention the authorship of the work and the first publication in this The authors can make other independent and additional contractual agreements for the non-exclusive distribution of the version of the article published in this journal (eg, include it in an institutional repository or publish it in a book) as long as they clearly indicate that The work was published for the first time in this magazine. Authors are allowed and recommended to publish their work. low on the Internet (for example on institutional or personal pages) before and during the review and publication process, as it can lead to productive exchanges and to a greater and faster dissemination of the published work
