An explanatory model of the intention to continue studying among non-traditional university students

  1. Rosário, Pedro
  2. Pereira, Armanda
  3. Núñez Pérez, José Carlos 1
  4. Cunha, Jennifer
  5. Fuentes, Sonia
  6. Polydoro, Soely
  7. Gaeta González, Martha Leticia
  8. Fernández Alba, María Estrella 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Revista:
Psicothema

ISSN: 0214-9915

Año de publicación: 2014

Volumen: 26

Número: 1

Páginas: 84-90

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Psicothema

Resumen

Antecedentes: a pesar de la importancia que se atribuye al rendimiento académico en la toma de decisión de los alumnos mayores de 25 años sobre si continuar o no sus estudios en la Universidad, la investigación sobre este tópico es limitada. Método: se analizó la relación entre el rendimiento académico y la intención de 327 alumnos no-tradicionales de continuar sus estudios en la Universidad mediante el ajuste de un modelo de relaciones causales. Resultados: la hipótesis central del estudio fue confirmada en la medida en que la intención de continuar con los estudios resultó determinada por los resultados académicos previos, aunque la cantidad de varianza explicada fue relativamente escasa (un 13%). Conclusiones: de los resultados obtenidos en este estudio se concluyó que la intención de continuar en la Universidad depende menos de lo que se cree del rendimiento logrado, por lo que las universidades deberán dirigir su mirada también a otras variables como, por ejemplo, la calidad del apoyo que estos estudiantes reciben para continuar sus estudios.

Referencias bibliográficas

  • Adams, J., & Corbett, A. (2010). Experiences of traditional and nontraditional College Student: A quantitative study of experiences, motivations and expectations among undergraduate Student. Methods of Social Research. Retrived from http://www.unh.edu/sociology/ index.cfm?id=ADF0DA25-E4F5-8F19-0AECEFEB8F59549F.
  • Arbuckle, J.L. (2009). Amos 18.0 User's Guide. Crawfordville: Amos Development Corporation.
  • Atwood, R. (2010, 10 June). The Cinderella students. Times Higher Education, pp. 31-46.
  • Bandalos, D.L. (1993). Factors influencing cross-validation of confirmatory factor analysis models. Multivariate Behavioral Research, 28, 351-374.
  • Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238-246.
  • Bentler, P.M. (1995). EQS: Structural equations program manual. Encino, CA: Multivariate Software.
  • Browne, M.W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. Bollen & J. Long (Eds.), Testing Structural Equation Models (pp. 136-162). Newbury Park, CA: Sage.
  • Cantwell, R., Archer, J., & Bourke, S. (2001). A comparison of the Academic Experiences and achievement of University student entering by traditional and nontraditional means. Assessment & Evaluation in Higher Education, 26(3), 221-234.
  • Chartrand, J.M. (1990). A causal analysis to predict the personal and academic adjustment os nontraditional student. Journal of Counseling Psychology 37(1), 65-73.
  • Chartrand, J.M. (1992). An empirical test of a model of nontraditional student adjustment. Journal of Counseling Psychology, 39(2), 193-202.
  • Cruce, T., & Williams, N. (2012). Preparing for the Silver Tsunami: The demand for Higher Education among older adults. Research in Higher Education, 53(6), 593-613.
  • Dornan, M. (2001). Metacognitive differences between traditional-age and nontraditional-age college student. Adult Education Quarterly, 51(236), 236-249.
  • Finney, S.J., & DiStefano, C. (2006). Non-normal and categorical data in structural equation modeling. In G.R. Hancock & R.O. Mueller (Eds.), Structural equation modeling: A second course (pp. 269-314). Greenwich, CT: Information Age Publishing.
  • Gilardi, S., & Chiara G. (2011). University life of non-traditional students: Engagement styles and impact on attrition. Journal of Higher Education, 82(1), 33-53.
  • Hay, D., Tan, P., & Whaites, E. (2010). Non-traditional learners in higher education: Comparison of a traditional MCQ examination with concept mapping to assess learning in a dental radiological science course. Assessment & Evaluation in Higher Education, 5, 577-595.
  • Hoskins, S., Newstead, S., & Dennis, I. (1997). Degree performance as a function of age, gender, prior qualifications and discipline studied. Assessment and Evaluation in Higher Education, 22(3), 317-328.
  • Johnston, V. (2001). The progression of Napier students through the first year of the modular course. Edinburgh, Napier University, Student Retention Project.
  • Jöreskog, K.G., & Sörbom, D. (1983). LISREL-6 User's Reference Guide. Mooresville, IN: Scientific Software.
  • Kim, K. (2007). ERIC Review: Exploring the meaning of "Nontraditional" at the community college. Community College Review, 30(1), 74-88.
  • Kimbrough, D., & Weaver, G. (1999). Improving the background knowledge of non-traditional students. Innovative Higher Education, 23(3), 197-219.
  • Laing, C., & Robinson, A. (2003). 'The Withdrawal of non-traditional students: Developing an explanatory model'. Journal of Further & Higher Education, 27(2), 175-185.
  • Metzner, B.S., & Bean, J.P. (1987). The estimulation of a conceptual model of nontraditional student. Research in Higher Education, 27(1), 15-37.
  • Munro, L. (2011). 'Go boldly, dream large!': The challenges confronting non-traditional students at university. Australian Journal of Education, 55(2), 115-131.
  • Núñez, J.C., Cerezo, R., González-Pienda, J.A., Rosário, P., Valle, A.,., & Suárez, N. (2011). Implementation of training programs in selfregulated learning strategies in Moodle format: Results of a experience in higher education. Psicothema, 23, 274-281.
  • Ogren, C.A. (2003). Rethinking the nontraditional student form a historical perspective. The Journal of Higher Education, 74(6), 640-664.
  • Pascarela, E. (2006). How college affects students: Ten directions for future research. Journal of College Student Development, 47(5), 508-520.
  • Pascarella, E.T., Duby, P.B., & Iverson, B.K. (1982). A test and reconceptualization of a theoretical model of college withdrawal in a commuter institution setting. Sociological Education, 56, 88-100.
  • Rosário, P., Núñez, J.C., Valle, A., Paiva, O., & Polydoro, S. (2013). Approaches to teaching in High School when considering contextual variables and teacher variables. Revista de Psicodidáctica, 28, 25-45.
  • Russell, M. (2008). The age of uncertainty. Times Higher Education, March 20-26, p. 24.
  • Sandler, M.E. (2000). Career decision-making self-efficacy, perceived stress, and an integrated model of student persistence: A structural model of finances, attitudes, behavior, and career development. Research in Higher Education, 41(5), 537-579.
  • Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45, 89-125.
  • Tinto, V. (2002, June). Promoting student retention: Lessons learned from the United States. Paper presented at the 11th Annual Conference of the European Access Network, Prato, Italy. U.S. Department of Education, National Center for Education Statistics (2002). Nontraditional undergraduates. Retrived from http://nces. ed.gov/programs/ coe/2002/analyses/nontraditional.
  • West, S.G., Finch, J.F., & Curran, P.J. (1995). Structural equation models with non-normal variables: Problems and remedies. In R. Hoyle (Ed.), Structural equation modeling: Concepts, issues and applications (pp. 55-75). Newbury Park, CA: Sage.
  • Yorke, M., & Longden, B. (2004). Retention and Student Success in Higher Education. Maidenhead, UK: Society for Research into Higher Education and Open University.
Fuente de los datos: Dialnet