Éxito académico, compromiso y autoeficacia de los estudiantes universitarios de primer año: variables personales y desempeño del primer semestre.

Autores/as

  • Joana Casanova Centro de Investigação em Educação, Universidade do Minho https://orcid.org/0000-0003-0652-3438
  • Jorge Sinval National Institute of Education, Nanyang Technological University, Singapore (Singapore) - Business Research Unit (BRU-IUL), Instituto Universitário de Lisboa (ISCTE-IUL) - Faculty of Philosophy, Sciences and Languages of Ribeirão Preto, University of São Paulo, Ribeirão Preto, SP (Brazil) - Department of Evidence-Based Health, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, SP (Brazil) https://orcid.org/0000-0002-2855-1360
  • Leandro Almeida School of Psychology, University of Minho https://orcid.org/0000-0002-0651-7014
DOI: https://doi.org/10.6018/analesps.479151
Palabras clave: Educación superior, Estudiantes de primer año, Compromiso académico, Autoeficacia, Logro académico

Agencias de apoyo

  • This work was supported by the Portuguese Science and Technol-ogy Foundation (FCT), Research Center on Education (CIEd) [UIDB/01661/2020; UIDP/01661/2020]. Jorge Sinval: This work was produced with the support of INCD, and it was funded by FCT I.P. under the project Advanced Computing Project CPCA/A1/435377/2021, platform Cirrus. This work was sup-ported by the Portuguese Science and Technology Foundation, grant UIDB/00315/2020

Resumen

La educación superior puede ser extremadamente transformadora para los estudiantes y tiene un papel importante en la formación del capital humano, en la innovación y en el desarrollo social, cultural y ambiental de la sociedad. La expansión de la educación superior promovió el acceso de una mezcla de estudiantes más heterogénea, pero garantizar el acceso no garantiza el éxito académico. Este artículo tiene como objetivo analizar los predictores de desempeño académico en 447 estudiantes de primer año en el 1er y 2do semestre, considerando variables como sexo, edad, nivel educativo de los padres y calificaciones al ingresar a la educación superior, junto con los niveles de compromiso académico e autoeficacia de los estudiantes tras algunas semanas en la universidad. Los resultados muestran trayectorias estadísticamente significativas para sexo, edad y GPA hasta el desempeño del primer semestre, para los niveles educativos de los padres hasta la autoeficacia percibida, para la implicación académica de los estudiantes hasta el desempeño del primer semestre y el desempeño del primer semestre hasta el desempeño del segundo semestre La participación académica de los estudiantes también tuvo un efecto indirecto en el desempeño del segundo semestre. La correlación entre compromiso académica y autoeficacia fue positiva, fuerte y estadísticamente significativa. El modelo explicó el 35.2% de la varianza del rendimiento académico en el segundo semestre y el 15.0% de la varianza del rendimiento académico en el primer semestre. El conocimiento sobre los predictores del rendimiento académico y la importancia del compromiso y la autoeficacia respaldará las intervenciones oportunas, promoviendo el éxito y previniendo el fracaso y el abandono.

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Citas

Abreu Alves, S., Sinval, J., Lucas Neto, L., Marôco, J., Gonçalves Ferreira, A., & Oliveira, P. (2022). Burnout and dropout intention in medical students: The protective role of academic engagement. BMC Medical Education, 22(1), 83. https://doi.org/10.1186/s12909-021-03094-9

Adabaş, A., & Kaygin, H. (2016). Lifelong learning key competence levels of graduate students. Universal Journal of Educational Research, 4(12A), 31–38. https://doi.org/10.13189/ujer.2016.041305

Aina, C. (2013). Parental background and university dropout in Italy. Higher Education, 65(4), 437–456. https://doi.org/10.1007/s10734-012-9554-z

Aina, C., Baici, E., Casalone, G., & Pastore, F. (2019). Delayed graduation and university dropout: A review of theoretical approaches. 12601.

Almeida, L. S., Guisande, M. A., & Paisana, J. (2012). Extra-curricular involvement, academic adjustment and achievement in higher education: A study of Portuguese students. Anales de Psicología, 28(3), 860–865. http://dx.doi.org/10.6018/analesps.28.3.156231

Ambiel, R. A. M., Santos, A. A. A., & Dalbosco, S. N. P. (2016). Motivos para evasão, vivências acadêmicas e adaptabilidade de carreira em universitários. Psico, 47(4), 288. https://doi.org/10.15448/1980-8623.2016.4.23872

Araque, F., Roldán, C., & Salguero, A. (2009). Factors influencing university drop out rates. Computers & Education, 53(3), 563–574. https://doi.org/10.1016/j.compedu.2009.03.013

Azzi, R. G., & Polydoro, S. (2007). Auto-eficácia em diferentes contextos. Alínea.

Bailey, T. H., & Phillips, L. J. (2016). The influence of motivation and adaptation on students’ subjective well-being, meaning in life and academic performance. Higher Education Research and Development, 35(2), 201–216. https://doi.org/10.1080/07294360.2015.1087474

Bandura, A. (1996). Social cognitive theory of human development. In T. Husen & T. N. Postlethwaite (Eds.), International Encyclopedia of Education (2nd ed., pp. 5513–5518). Pergamin Press.

Bártolo-Ribeiro, R., Peixoto, F., Casanova, J. R., & Almeida, L. S. (2020). Regulation of cognition: Validation of a short scale for Portuguese first-year university students. Anales de Psicología, 36(2), 313–319. https://doi.org/10.6018/analesps.389361

Belloc, F., Maruotti, A., & Petrella, L. (2011). How individual characteristics affect university students drop-out: A semiparametric mixed-effects model for an Italian case study. Journal of Applied Statistics, 38(10), 2225–2239. https://doi.org/10.1080/02664763.2010.545373

Bernardo, A., Cervero, A., Esteban, M., Tuero, E., Casanova, J. R., & Almeida, L. S. (2017). Freshmen program withdrawal: Types and recommendations. Frontiers in Psychology, 8, 1–11. https://doi.org/10.3389/fpsyg.2017.01544

Boomsma, A. (2000). Reporting analyses of covariance structures. Structural Equation Modeling: A Multidisciplinary Journal, 7(3), 461–483. https://doi.org/10.1207/S15328007SEM0703_6

Byrne, B. M. (2012). Structural equation modeling with Mplus: Basic concepts, applications, and programming. Routledge. https://doi.org/10.4324/9780203807644

Casanova, J. R., Cervero, A., Núñez, J. C., Almeida, L. S., & Bernardo, A. (2018). Factors that determine the persistence and dropout of university students. Psicothema, 30(4), 408–414. https://doi.org/10.7334/psicothema2018.155

Casanova, J. R., Cervero, A., Nuñez, J. C., Bernardo, A. B., & Almeida, L. S. (2018). Abandono no Ensino Superior: Impacto da autoeficácia na intenção de abandono [Dropout in higher education: Impact of self-efficacy in dropout intention]. Revista Brasileira de Orientação Profissional, 19(1), 41–49. https://doi.org/1026707/1984-7270/2019v19n1p41

Casanova, J. R., Vasconcelos, R., Bernardo, A. B., & Almeida, L. S. (2021). University dropout in Engineering: Motives and student trajectories. Psicothema, 33(4), 595–601. https://doi.org/10.7334/psicothema2020.363

Casanova, J. R., Gomes, A., Moreira, M. A., & Almeida, L. S. (2022). Promoting success and persistence in pandemic times: An experience with first-year students. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.815584

Coetzee, M., & Oosthuizen, R. M. (2012). Students’ sense of coherence, study engagement and self-efficacy in relation to their study and employability satisfaction. Journal of Psychology in Africa, 22(3), 315–322. https://doi.org/10.1080/14330237.2012.10820536

Criollo, M., Romero, M., & Fontaines-Ruiz, T. (2017). Autoeficacia para el aprendizaje de la investigación en estudiantes universitarios. Psicología Educativa, 23(1), 63–72. https://doi.org/10.1016/j.pse.2016.09.002

De Clercq, D., Thongpapanl, N. T., & Dimov, D. (2011). A closer look at cross-functional collaboration and product innovativeness: Contingency effects of structural and relational context. Journal of Product Innovation Management, 28(5), 680-697. https://doi.org/10.1111/j.1540-5885.2011.00830.x

Denovan, A., Dagnall, N., Macaskill, A., & Papageorgiou, K. (2020). Future time perspective, positive emotions and student engagement: A longitudinal study. Studies in Higher Education, 45(7), 1533–1546. https://doi.org/10.1080/03075079.2019.1616168

Diniz, A. M., Alfonso, S., Araújo, A. M., Deaño, M. D., Costa, A. R., Conde, Â., & Almeida, L. S. (2018). Gender differences in first-year college students’ academic expectations. Studies in Higher Education, 1–13. https://doi.org/10.1080/03075079.2016.1196350

Dwyer, R. E., Hodson, R., & McCloud, L. (2013). Gender, debt, and dropping out of college. Gender & Society, 27(1), 30–55. https://doi.org/10.1177/0891243212464906

Fanelli, A. G., & Deane, C. A. (2015). Abandono de los estudios universitarios: Dimensión, factores asociados y desafíos para la politica pública [University dropout: Dimensions, determinants and challenges to public policy]. Revista Fuentes, 16, 85–106. https://doi.org/10.12795/revistafuentes.2015.i16.04 85

Ferrão, M. E., & Almeida, L. S. (2019) Differential effect of university entrance score on first-year students’ academic performance in Portugal. Assessment & Evaluation in Higher Education, 44(4), 610–622. https://doi.org/10.1080/02602938.2018.1525602

Figuera, P., Torrado, M., Dorio, I., & Freixa, M. (2015). Trayectorias de persistencia y abandono de estudiantes universitarios no convencionales: Implicaciones para la orientación [Non-traditional university students persistence and drop-out pathways: Implications for guidance]. Revista Electrónica Interuniversitaria de Formación Del Profesorado, 18(2), 107–123. https://doi.org/10.6018/reifop.18.2.220101

Finney, S. J., & DiStefano, C. (2013). Non-normal and categorical data in structural equation modeling. In G. R. Hancock & R. O. Mueller (Eds.), Structural equation modeling: A second course (2nd ed., pp. 439–492). Information Age Publishing.

Fredricks, J. A. (2011). Engagement in school and out-of-school contexts: A multidimensional view of engagement. Theory Into Practice, 50(4), 327–335. https://doi.org/10.1080/00405841.2011.607401

Fredricks, J. A., & McColskey, W. (2012). The measurement of student engagement: A comparative analysis of various methods and student self-report instruments. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 763–782). Springer. https://doi.org/10.1007/978-1-4614-2018-7_37

French, B. F., Immekus, J. C., & Oakes, W. C. (2005). An examination of indicators of engineering students’ success and persistence. Journal of Engineering Education, 94(4), 419–425. https://doi.org/10.1002/j.2168-9830.2005.tb00869.x

García-Ros, R., Pérez-González, F., Cavas-Martínez, F., & Tomás, J. M. (2018). Effects of pre-college variables and first-year engineering students’ experiences on academic achievement and retention: A structural model. International Journal of Technology and Design Education, 0123456789. https://doi.org/10.1007/s10798-018-9466-z

Gilardi, S., & Guglielmetti, C. (2011). University life of non-traditional students: Engagement styles and impact on attrition engagement styles and impact on attrition. The Journal of Higher Education, 82(1), 33–53. https://doi.org/10.1080/00221546.2011.11779084

González-Ramírez, T., & Pedraza-Navarro, I. (2017). Variables sociofamiliares asociadas al abandono de los estudios universitarios [Social and families variables associated with university drop-out]. Educatio Siglo XXI, 35(2), 365–388. https://doi.org/10.6018/j/298651

Harman, K. (2017). Democracy, emancipation and widening participation in the UK: Changing the “distribution of the sensible.” Studies in the Education of Adults, 49(1), 92–108. https://doi.org/10.1080/02660830.2017.1283757

Hoyle, R. H. (Ed.). (1995). Structural equation modeling: Concepts, issues and applications. SAGE Publications.

Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel, Y. (2021). semTools: Useful tools for structural equation modeling (R package version 0.5-4) [Computer software] (0.5-4).

Klem, A. M., & Connell, J. P. (2004). Relationships matter: Linking teacher support to student engagement and achievement. Journal of School Health, 74(7), 262–273. https://doi.org/10.1111/j.1746-1561.2004.tb08283.x

Kuh, G. D., Kinzie, J., Buckley, J. A., Bridges, B. K., & Hayek, J. C. (2006). What matters to student success: A review of the literature. Commissioned Report for the National Symposium on Postsecondary Student Success: Spearheading a Dialog on Student Success. July, 156. https://www.ue.ucsc.edu/sites/default/files/WhatMattersStudentSuccess(Kuh,July2006).pdf

Lassibille, G., & Gómez, M. L. N. (2009). Tracking students’ progress through the Spanish university school sector. Higher Education, 58(6), 821–839. https://doi.org/10.1007/s10734-009-9227-8

Lemon, J. (2006). Plotrix: a package in the red light district of R. R-News, 6(4), 8–12.

Lüdecke, D. (2019). sjstats: Statistical functions for regression models (R package version 0.17.3) [Computer software]. https://doi.org/10.5281/zenodo.1284472

Marôco, J. (2021). Análise de equações estruturais: Fundamentos teóricos, software & aplicações (3rd ed.). ReportNumber.

Marôco, J., Marôco, A. L., Campos, J. A. D. B., & Fredricks, J. A. (2016). University student’s engagement: Development of the University Student Engagement Inventory (USEI). Psicologia: Reflexão e Crítica, 29(21), 1–12. https://doi.org/10.1186/s41155-016-0042-8

McDonald, R. P., & Ho, M.-H. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64–82. https://doi.org/10.1037/1082-989X.7.1.64

McNabb, R., Pal, S., & Sloane, P. (2002). Gender differences in educational attainment: The case of university students in England and Wales. Economica, 69, 481–503. https://doi.org/10.1111/1468-0335.00295

McNamara, A., Arino de la Rubia, E., Zhu, H., Ellis, S., & Quinn, M. (2018). skimr: Compact and flexible summaries of data (R package version 1.0.3) [Computer software] (1.0.3).

Merritt, D. L., & Buboltz, W. (2015). Academic success in college: Socioeconomic status and parental influence as predictors of outcome. Open Journal of Social Sciences, 03(05), 127–135. https://doi.org/10.4236/jss.2015.35018

Muthén, B. O. (1983). Latent variable structural equation modeling with categorical data. Journal of Econometrics, 22(1–2), 43–65. https://doi.org/10.1016/0304-4076(83)90093-3

Naylor, R., Baik, C., & Arkoudis, S. (2017). Identifying attrition risk based on the first year experience. Higher Education Research & Development, 1–15. https://doi.org/10.1080/07294360.2017.1370438

OECD. (2018). Review of the Tertiary Education, Research and Innovation System in Portugal. https://doi.org/10.1787/9789264308138-en

Palardy, G. J. (2013). High school socioeconomic segregation and student attainment. American Educational Research Journal, 50(4), 714–754. https://doi.org/10.3102/0002831213481240

Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research (Vol. 2). Jossey-Bass.

Polydoro, S. A., & Guerreiro-Casanova, D. C. (2010). Escala de Autoeficácia na Formação Superior: Construção e estudo de validação [Self-Efficacy Scale in Higher Education: Construction and validation study]. Avaliação Psicológica, 9(2), 267–278.

R Core Team. (2021). R: A language and environment for statistical computing (version 4.0.4) [Computer software] (4.0.4). R Foundation for Statistical Computing.

Raykov, T. (2001). Estimation of congeneric scale reliability using covariance structure analysis with nonlinear constraints. The British Journal of Mathematical and Statistical Psychology, 54, 315–323. https://doi.org/10.1348/000711001159582

Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’ academic performance: A systematic review and meta-analysis. Psychological Bulletin, 138(2), 353–387. https://doi.org/10.1037/a0026838

Rodríguez-Muñiz, L. J., Bernardo, A. B., Esteban, M., & Díaz, I. (2019). Dropout and transfer paths: What are the risky profiles when analyzing university persistence with machine learning techniques? PLoS ONE, 14(6), 1–20. https://doi.org/10.1371/journal.pone.0218796

Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–21. http://www.jstatsoft.org/v48/i02/paper

Schneider, M., & Preckel, F. (2017). Variables associated with achievement in higher education: A systematic review of meta-analyses. Psychological Bulletin, 143(6), 565–600. https://doi.org/10.1037/bul0000098

Severiens, S., & ten Dam, G. (2012). Leaving college: A gender comparison in male and female-dominated programs. Research in Higher Education, 53(4), 453–470. https://doi.org/10.1007/s11162-011-9237-0

Signorell, A., Aho, K., Alfons, A., Anderegg, N., Aragon, T., Arppe, A., Baddeley, A., Barton, K., Bolker, B., Borchers, H. W., Caeiro, F., Champely, S., Chessel, D., Chhay, L., Cummins, C., Dewey, M., Doran, H. C., Dray, S., Dupont, C., … Zeileis, A. (2019). DescTools: Tools for descriptive statistics (R package version 0.99.28) [Computer software] (0.99.28).

Sinval, J., Casanova, J. R., Marôco, J., & Almeida, L. S. (2021). University student engagement inventory (USEI): Psychometric properties. Current Psychology, 40(4), 1608–1620. https://doi.org/10.1007/s12144-018-0082-6

Soares, A. M., Pinheiro, M. R., Manuel, J., & Canavarro, J. M. (2015). Transição e adaptação ao ensino superior e a demanda pelo sucesso nas instituições portuguesas [Transition and adaptation to higher education and the demand for success in Portuguese institutions]. Psychologica, 58(2), 97–116. https://doi.org/10.14195/1647-8606_58

Stinebrickner, R., & Stinebrickner, T. (2014). Academic performance and college dropout: Using longitudinal expectations data to estimate a learning model. Journal of Labor Economics, 32(3), 601–644. https://doi.org/10.1086/675308

Stratton, L. S., O’Toole, D. M., & Wetzel, J. N. (2008). A multinomial logit model of college stopout and dropout behavior. Economics of Education Review, 27(3), 319–331. https://doi.org/10.1016/j.econedurev.2007.04.003

Tight, M. (2019). Student retention and engagement in higher education. Journal of Further and Higher Education, 1–16. https://doi.org/10.1080/0309877X.2019.1576860

Tinto, V. (2010). From theory to action: Exploring the institutional conditions for student retention. In Higher Education: Handbook of Theory and Research (Vol. 25, pp. 51–89). Springer Netherlands. https://doi.org/10.1007/978-90-481-8598-6_2

UNESCO. (2017). Six ways to ensure higher education leaves no one behind. In Policy Paper (Vol. 30, Issue April). https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Six+Ways+To+Ensure+Higher+Education+Leaves+No+One+Behind&btnG=%0Ahttp://unesdoc.unesco.org/images/0024/002478/247862E.pdf

Van den Broeck, L., De Laet, T., Lacante, M., Pinxten, M., Van Soom, C., & Langie, G. (2018). Predicting the academic achievement of students bridging to engineering: The role of academic background variables and diagnostic testing. Journal of Further and Higher Education, 9486, 1–19. https://doi.org/10.1080/0309877X.2018.1431209

Publicado
01-01-2024
Cómo citar
Casanova, J., Sinval, J., & Almeida, L. (2024). Éxito académico, compromiso y autoeficacia de los estudiantes universitarios de primer año: variables personales y desempeño del primer semestre. Anales de Psicología / Annals of Psychology, 40(1), 44–53. https://doi.org/10.6018/analesps.479151
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Sección
Psicología evolutiva y de la educación