Academic success, engagement and self-efficacy of first-year university students: personal variables and first-semester performance
Supporting Agencies
- 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
Abstract
Higher education can be hugely transformative for students and has an important role in empowering human capital, innovation, and society’s social, cultural, and environmental development. The expansion of higher education has promoted access for a more heterogeneous mix of students, but ensuring access does not guarantee academic success. This paper aims to analyse predictors of academic achievement in 447 first-year students in their 1st and 2nd semesters, considering variables including sex, age, parents’ educational level and grades on entering higher education, along with levels of students’ academic engagement and self-efficacy after some weeks at university. Results show statistically significant paths for sex, age, and GPA to 1st-semester achievement, for parent’s educational levels to perceived self-efficacy, for students’ academic engagement to 1st-semester achievement, and 1st-semester achievement to 2nd-semester achievement. Students’ academic engagement also had an indirect effect on the 2nd-semester achievement. The correlation between academic engagement and self-efficacy was positive, strong, and statistically significant. The model explained 35.2% of the variance in 2nd-semester achievement and 15.0% of the variance in 1st-semester achievement. Knowledge about predictors of academic achievement and the importance of engagement and self-efficacy will support timely interventions, promoting success and preventing failure and dropout.
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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
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