Evaluación del modelo cognitivo social de desarrollo de la carrera para la predicción de las metas en las materias tecnológicas de estudiantes de bachillerato

  1. Inda-Caro, Mª de las Mercedes 1
  2. Rodríguez-Menéndez, María del Carmen 1
  3. Torío-López, Susana 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Journal:
ESE: Estudios sobre educación.

ISSN: 1578-7001

Year of publication: 2017

Issue: 32

Pages: 49-71

Type: Article

DOI: 10.15581/004.32.49-71 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: ESE: Estudios sobre educación.

Sustainable development goals

Abstract

The authors of this paper have examined the relative contribution of personal (gender, emotional state, gender roles attitudes), contextual (perceived social supports and barriers) and cognitive (self-efficacy beliefs, interests, outcome expectations) variables to technological goals in a sample (N=1558) of high school Spanish students. The results of path analysis have provided confirmation for the extension of the Social-Cognitive Career Theory model, indicating that self-efficacy predicted interest, outcome expectations and goals. Additionally, outcome expectations predicted goals and interest. Perceived social support and perceived social barriers were related with self-efficacy, outcome expectations, goals and interest. Finally, there were gender differences in most variables.

Funding information

Subvención recibida: Investigación financiada por el MICINN (EDU-2010-17233) y FONDOS FEDER.

Funders

Bibliographic References

  • Anderson, N., Lankshear, C., Timms, C. y Courtney, L. (2008). “Because it’s boring, irrelevant and I don’t like computers”: why high school girls avoid professionally-oriented ICT subjects. Computers & Education, 50, 1304-1318. doi: 10.1016/j.compedu.2006.12.003
  • Barkatsas, A., Kasimatis, K. y Gialamas, V. (2009). Learning secondary mathematics with technology: exploring the complex interrelationship between student’s attitudes, engagement, gender and achievement. Computers & Education, 52, 562-570. doi: 10.1016/j.compedu.2008.11.001
  • Bovée, C., Voogt, J. y Meelissen, M. (2007). Computer attitudes of primary and secondary in South Africa. Computers in Human Behavior, 23, 1762-1776. doi: 10.1016/j.chb.2005.10.004
  • Britner, G. y Pajares, F. (2001). Self-effi cacy beliefs, motivation, race, and gender in middle school science. Journal of Women and Minorities in Science and Engineering, 7, 269-283.
  • Britner, S. y Pajares, F. (2006). Sources of science self-effi cacy beliefs of middle school students. Journal of Research in Science Teaching, 43, 485-499. doi: 10.1002/tea.20131
  • Cakir, O. (2012). Students’ self confi dence and attitude regarding computer: an international analysis based on computer availability and gender factor. Procedia Social and Behavioral Sciences, 47, 1017-1022. doi: 10.1016/j.sbspro.2012.06.772
  • Cupani, M., De Minzi, M. C., Pérez, E. R. y Pautassi, R. M. (2010). An assessment of a social cognitive career model of academic performance in mathematics in Argentinean middle school students. Learning and Individual Differences, 20, 659-663. doi: 10.1016/j.lindif.2010.03.006
  • Cupani, M. y Lorenzo, J. (2010). Evaluación de un modelo social cognitivo del rendimiento en matemática en una población de preadolescentes argentinos. Infancia y Aprendizaje, 33(1), 63-74.
  • Dhanjal, S. y Kwiatkowska, M. (2003). Women in computing: perceptions of computing science among female students in high schools and colleges. En VV. AA., Proceedings of Western Canadian Conference on Computing Education. Canada: Courtenay.
  • Ferrando, P. J. y Anguiano-Carrasco, C. (2010). El análisis factorial como técnica de investigación en psicología. Papeles del Psicólogo, 31(1), 18-33.
  • Flores, L., Navarro, R. y Dewitz, S. (2008). Mexican American high school students’ postsecondary educational goals. Applying social cognitive career theory. Journal of Career Assessment, 16, 489-501. doi: 10.1177/1069072708318905
  • Flores, L., Navarro, R., Smith, J. y Plosjaz, A. (2006). Testing a model of nontraditional career choice goals with Mexican American adolescent men. Journal of Career Assessment, 14, 214-234. doi: 10.1177/0894845308327739
  • Flores, L. y O’Brien, K. (2002). The career development of Mexican American adolescent women: a test of social cognitive career theory. Journal of Counselling Psychology, 49, 14-27. doi: 10.1037/0022-0167.49.1.14
  • Fouad, N. A. y Smith, P. L. (1996). A test of a social cognitive model for middle school students: math and science. Journal of Counselling Psychology, 43(3), 338-346.
  • Fouad, F., Smith, P. L. y Enochs, I. (1997). Reliability and validity evidence for the middle school self-effi cacy scale. Measurement and Evaluation in Counselling and Development, 30, 17-31.
  • Kadijevich, D. (2000). Gender differences in computer attitude among ninth-grade students. Journal of Educational Computing Research, 22, 145-154. doi: 10.2190/ K4U2-PWQG-RE8L-UV90
  • Lent, R. W. y Brown, S. (2006). On conceptualizing and assessing social cognitive constructs in careers research: a measurement guide. Journal of Career Assessment, 14, 12-35. doi: 10.1177/1069072705281364
  • Lent, R. W., Brown, S., Nota, L. y Soresi, S. (2003). Testing social cognitive interest and choice hypotheses across Holland types in Italian high school students. Journal of Vocational Behavior, 62, 101-118. doi: 10.1016/S00018791(02)00057-X
  • Lent, R. W., Brown, S. D., Sheu, H., Schmidt, J., Brenner, B. R., Gloster, C. S. et al. (2005). Social cognitive predictors of academic interests and goals in engineering: utility for women and students at historically black universities. Journal of Counselling Psychology, 52, 84-92. doi: 10.1037/0022-0167.52.1.84
  • Lent, R. W., Paixao, M. P., Da Silva, J. T. y Leitao, L. M. (2010). Predicting occupational interest and choice aspirations in Portuguese high school students: a test of social cognitive career theory. Journal of Vocational Behavior, 76, 244251. doi: 10.1016/j.jvb.2009.10.001
  • López-Sáez, M., Puertas, S. y Sáinz, M. (2011). Why don’t girls choose technological studies? Adolescents’ stereotypes and attitudes towards studies related to medicine or engineering. The Spanish Journal of Psychology, 14(1), 74-87.
  • Lorenzo-Seva, U. (1999). Promin: A method for oblique factor rotation. Multivariate Behavioral Research, 34, 347-365. doi: 10.1207/S15327906MBR3403_3
  • Lorenzo-Seva, U. y Ferrando, P. (2006). FACTOR: A computer program to fi t the exploratory factor analysis model. Behavior Research Methods, 38, 88-91. doi: 10.3758/BF03192753
  • Makrakis, V. y Sawada, T. (1996). Gender, computers and other school subjects among Japanese and Swedish students. Computers & Education, 26, 225-231. doi: 10.1016/0360-1315(95)00085-2
  • Muthén, L. y Muthén, B. (2010). Mplus. Statistical analysis with latent variables. User’s guide. Los A ngeles: Muthén & Muthén.
  • Nauta, M. y Epperson, D. L. (2003). A longitudinal examination of the socialcognitive model applied to high school girls’ choices of non-traditional college majors and aspirations. Journal of Counseling Psychology, 50, 448-457. doi: 10.1037/0022-0167.50.4.448
  • Navarro, R., Flores, L. y Worthington, R. (2007). Mexican American middle school students’ goal intentions in mathematics and science: a test of social cognitive career theory. Journal of Counseling Psychology, 54, 320-335. doi: 10.1037/0022-0167.54.3.320
  • O’Brien, V., Martínez-Pons, M. y Kopola, M. (1999). Mathematics self-effi cacy, ethnic identity, gender and career interests related to mathematics and science. The Journal of Educational Research, 92, 231-235. doi: 10.1080/00220679909597600
  • Papastergiou, M. (2008). Are computer science and information technology still masculine fi elds? High school student’s perceptions and career choices. Computers & Education, 51, 594-608. doi: 10.1016/j.compedu.2007.06.009
  • Turner, S., Stewart, J. y Lapan, R. (2004). Family factors associated with sixthgrade adolescents’ math and science career interests. The Career Development Quarterly, 53, 41-51. doi: 10.1002/j.2161-0045.2004.tb00654.x
  • Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41, 321-327.
  • Zarret, N. R. y Malanchuk, O. (2005). Who’s computing? Gender and race differences in young adult’s decisions to pursue an information technology career. New Directions for Child and Adolescent Development, 110, 65-84.