Hacia un modelo explicativo del rendimiento académicovariables orécticas y cognitivas

  1. Francisco González-Primo 1
  2. Pelayo Montes-Álvarez 1
  3. Álvaro Postigo 1
  4. Álvaro Menéndez-Aller 1
  5. Eduardo García-Cueto 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Journal:
REMA

ISSN: 1135-6855

Year of publication: 2022

Volume: 24

Issue: 2

Pages: 45-59

Type: Article

DOI: 10.17811/REMA.24.2.2022.45-59 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: REMA

Abstract

Reasoning, creativity, openness, motivation, conscientiousness and neuroticism are variables that have usually been associated with academic performance. However, there are hardly any studies that jointly examine the effects of these factors. The objective was to study some of the variables that affect academic performance and construct a model that includes them. In addition, it was observed if there were differences in creativity, academic performance and reasoning according to gender and type of education. The Big Five (NEO-FFI), reasoning (PMA), creativity, and achievement motivation of 281 undergraduates (M = 21.16; SD = 3.14; 20% men) were assessed. The proposed model of path-analysis showed a good fit(휒휒2not statistically significant; SRMR = .05; CFI = .973). Academic performance can be explained by several variables: reasoning, creativity, openness to experience, achievement motivation and conscientiousness. Statistically significant differences were found in creativity according to sex, and in creativity and reasoning according to the type of studies.

Bibliographic References

  • Andreas, S. F. K., Zech, S., Coyle, T. R., y Rindermann, H. (2016). Unconventionality and originality: Does self-assessed unconventionality increase original achievement? Creativity Research Journal, 28(2), 198–206. https://doi.org/10.1080/10400419.2016.1162556
  • Aranguren, M. (2015). Influencia del conocimiento previo sobre el Test de Pensamiento Creativo de Torrance. International Journal of Psychological Research, 8(2), 75–89. https://doi.org/10.21500/20112084.1511
  • Baer, J., y Kaufman, J. C. (2008). Gender differences in creativity. The Journal of Creative Behavior, 42(2), 75–105. https://doi.org/10.1002/j.2162-6057.2008.tb01289.x
  • Batey, M., y Furnham, A. (2006). Creativity, intelligence, and personality: A critical review of the scattered literature. Genetic, Social, and General Psychology Monographs, 132(4), 355–429. https://doi.org/10.3200/MONO.132.4.355-430
  • Beaty, R. E., Silvia, P. J., Nusbaum, E. C., Jauk, E., y Benedek, M. (2014). The roles of associative and executive processes in creative cognition. Memory & Cognition, 42(7), 1186–1197. https://doi.org/10.3758/s13421-014-0428-8
  • Cáceres, S. F. (2017). Relación entre los factores de personalidad y depresión con el rendimiento académico en estudiantes de una facultad en una universidad privada de Lima Metropolitana (Tesis doctoral). Universidad peruana Cayetano Heredia, Lima.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2ªed.). Erlbaum.
  • Cordero, A., Seisdedos, N., González, M., y de la Cruz, M. V. (2007). PMA: Aptitudes Mentales Primarias (manual técnico) [PMA: Primary Mental Abilities. (Technical manual)]. TEA Ediciones.
  • Costa, P. T., y McCrae, R. R. (2002). NEO PI-R Inventario De Personalidad NEO Revisado Manual. TEA Ediciones.
  • Crawford, I., y Wang, Z. (2015). The impact of individual factors on the academic attainment of Chinese and UK students in higher education. Studies in Higher Education, 40(5), 902–920. https://doi.org/10.1080/03075079.2013.851182
  • Crew Universidades Españolas (2018). La Universidad española en cifras. http://www.crue.org/SitePages/La-Universidad-Española-en-Cifras.aspx
  • Cuesta, M., Suárez-Álvarez, J., Lozano, L. M., García-Cueto, E., & Muñiz, J. (2018). Assessment of eight entrepreneurial personality dimensions: Validity evidence of the BEPE battery. Frontiers in Psychology, 9, 2352, 1-10. https://doi.org/10.3389/fpsyg.2018.02352
  • De Sixte, R., Jáñez, A., Ramos, M., y Rosales, J. (2020). Motivación, rendimiento en matemáticas y prácticas familiares: Un estudio de su relación en 1º de Educación Primaria. Psicología Educativa, 26, 67–75. https://doi.org/10.5093/psed2019a16
  • Di Domenico, S. I., y Fournier, M. A. (2015). Able, ready, and willing: Examining the additive and interactive effects of intelligence, conscientiousness, and autonomous motivation on undergraduate academic performance. Learning and Individual Differences, 40, 156–162. https://doi.org/10.1016/j.lindif.2015.03.016
  • Ding, L., Wei, X., y Liu, X. (2016). Variations in university students’ scientific reasoning skills across majors, years, and types of institutions. Research in Science Education, 46(5), 613–632. https://doi.org/10.1007/s11165-015-9473-y
  • Echavarri, M., Godoy, J., y Olaz, F. (2007). Diferencias de género en habilidades cognitivas y rendimiento académico en estudiantes universitarios. Universitas Psychologica, 6(2), 319–329.
  • Etikan, I. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
  • Evers, A., Muñiz, J., Hagemeister, C., Høstmælingen, A., Lindley, P., Sjöberg, A., y Bartram, D. (2013). Assessing the quality of tests: Revision of the EFPA review model. Psicothema, 29(3), 236–240. https://doi.org/10.7334/psicothema2013.97
  • Fenollar, P., Román, S., y Cuestas, P. (2007). University students’ academic performance: An integrative conceptual framework and empirical analysis. British Journal of Educational Psychology, 77(4), 873–891. https://doi.org/10.1348/000709907X189118
  • Fuller, B., y Marler, L. E. (2009). Change driven by nature: A meta-analytic review of the proactive personality literature. Journal of Vocational Behavior, 75(3), 329–345. https://doi.org/10.1016/j.jvb.2009.05.008
  • Furnham, A., Nuygards, S., y Chamorro-Premuzic, T. (2013). Personality, assessment methods and academic performance. Instructional Science, 41(5), 975–987. https://doi.org/10.1007/s11251-012-9259-9
  • Gajda, A., Karwowski, M., y Beghetto, R. A. (2017). Creativity and academic achievement: A meta-analysis. Journal of Educational Psychology, 109(2), 269–299. https://doi.org/10.1037/edu0000133
  • García-Cueto, E. (1984). Estructura factorial de la fluidez verbal escrita en sujetos de 11 a 18 años (Tesis doctoral). Universidad Complutense, Madrid.
  • Gatica, A., y Bizama, M. (2019). Inteligencia fluida y creatividad: Un estudio en escolares de 6 a 8 años de edad. Pensamiento Psicológico, 17(1), 113–120. https://doi.org/10.11144/Javerianacali.PPSI17-1.ifce.
  • Glück, J., Ernst, R., y Unger, F. (2002). How creatives define creativity: Definitions reflect different types of creativity. Creativity Research Journal, 14(1), 55–67. https://doi.org/10.1207/S15326934CRJ1401_5
  • Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement Routledge.
  • He, W.J. (2018). A 4-year longitudinal study of the sex-creativity relationship in childhood, adolescence, and emerging adulthood: Findings of mean and variability analyses. Frontiers in Psychology, 9, 2331. https://doi.org/10.3389/fpsyg.2018.02331.
  • Hu, L., y Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  • Janošević, M., y Petrović, B. (2018). Effects of personality traits and social status on academic achievement: Gender differences. Psychology in the Schools, 56(4), 497–509. https://doi.org/10.1002/pits.22215
  • Kaufman, J. C., Baer, J., y Gentile, C. A. (2004). Differences in gender and ethnicity as measured by ratings of three writing tasks. The Journal of Creative Behavior, 38(1), 56–69. https://doi.org/10.1002/j.2162-6057.2004.tb01231.x
  • Khan, R. M. S., Nawaz, K., Yaseen, S., Rouf, A., Maryam, M., y Tabassum, S. (2018). Relationship between birth order, personality and academic performance. Rawal Medical Journal, 43(1), 39–44.
  • Kim, J., y Michael, W. B. (1995). The relationship of creativity measures to school achievement and to preferred learning and thinking style in a sample of Korean high school students. Educational and Psychological Measurement, 55(1), 60–74. https://doi.org/10.1177/0013164495055001006
  • Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.
  • Komarraju, M., Karau, S. J., y Schmeck, R. R. (2009). Role of the Big Five personality traits in predicting college students’ academic motivation and achievement. Learning and Individual Differences, 19(1), 47–52. https://doi.org/10.1016/j.lindif.2008.07.001
  • Lorenzo-Seva, U., y Ferrando, P.J. (2006). FACTOR: A computer program to fit the exploratory factor analysis model. Behavioral Research Methods, Instruments and Computers, 38, 88–91.
  • Mammadov, S., Cross, T. L., y Ward, T. J. (2018). The Big Five personality predictors of academic achievement in gifted students: Mediation by self-regulatory efficacy and academic motivation. High Ability Studies, 29(2), 111–133. https://doi.org/10.1080/13598139.2018.1489222
  • McCrae, R. R., y Costa P. T. (1987). Validation of the Five Factor Model of Personality across instruments and observers. Journal of Personality and Social Psychology, 52(1), 81-90. https://doi.org/1037/0022-3514.52.1.81
  • Mendoza-Recarte, L. (2017). Baremos del test de aptitudes mentales primarias para universitarios hondureños. Revista Ciencia Y Tecnología, 19, 198–227. https://doi.org/10.5377/rct.v0i19.4281
  • Mourgues, C., Tan, M., Hein, S., Elliott, J. G., y Grigorenko, E. L. (2016). Using creativity to predict future academic performance: An application of Aurora’s five subtests for creativity. Learning and Individual Differences, 51, 378–386. https://doi.org/10.1016/j.lindif.2016.02.001
  • Muñiz, J., Suárez-Alvárez, J., Pedrosa, I., Fonseca-Pedrero, E., y García-Cueto, E. (2014). Enterprising personality profile in youth: Components and assessment. Psicothema, 26(4), 545–553. https://doi.org/10.7334/psicothema2014.182
  • Muthén, L. K., y Muthén, B. O. (2017). Mplus user’s guide (8th ed.). Muthén y Muthén.
  • Nori, R., Signore, S., y Bonifacci, P. (2018). Creativity style and achievements: An investigation on the role of emotional competence, individual differences, and psychometric intelligence. Frontiers in Psychology, 9, 1826. https://doi.org/10.3389/fpsyg.2018.01826
  • Ohtani, K., y Hisasaka, T. (2018). Beyond intelligence: A meta-analytic review of the relationship among metacognition, intelligence, and academic performance. Metacognition and Learning, 13(2), 179–212. https://doi.org/10.1007/s11409-018-9183-8
  • Pedrosa, I., Juarros-Basterretxea, J., Robles-Fernández, A., Basteiro, J., y García-Cueto, E. (2014). Pruebas de bondad de ajuste en distribuciones simétricas, ¿qué estadístico utilizar? Universitas Psychologica, 14(1), 245–254. https://doi.org/10.11144/Javeriana.upsy14-1.pbad
  • Postigo, Á., García-Cueto, E., Cuesta, M., Menéndez-Aller, Á., Prieto-Díez, F., y Lozano, L. M. (2020). Assessment of the enterprising personality: A short form of the BEPE battery. Psicothema, 32(4), 575–582. https://doi.org/10.7334/psicothema2020.193.
  • Richardson, M., y Abraham, C. (2009). Conscientiousness and achievement motivation predict performance. European Journal of Personality, 23(7), 589–605. https://doi.org/10.1002/per.732
  • Siddiquei, N., y Khalid, D. (2018). The relationship between personality traits, learning styles and academic performance of e-learners. Open Praxis, 10(3), 249–263. https://doi.org/10.5944/openpraxis.10.3.870
  • Solano, L. O. (2015). Rendimiento académico de los estudiantes de secundaria obligatoria y su relación con las aptitudes mentales y las actitudes ante el estudio (Tesis doctoral). Universidad Nacional de Educación a Distancia, Madrid.
  • Stajkovic, A. D., Bandura, A., Locke, E. A., Lee, D., y Sergent, K. (2018). Test of three conceptual models of influence of the Big Five personality traits and self-efficacy on academic performance: A meta-analytic path-analysis. Personality and Individual Differences, 120, 238-245. https://doi.org/10.1016/j.paid.2017.08.014
  • Steinmayr, R., Bipp, T., y Spinath, B. (2011). Goal orientations predict academic performance beyond intelligence and personality. Learning and Individual Differences, 21(2), 196–200. https://doi.org/10.1016/J.lindif.2010.11.026
  • Sternberg, R. J. (1984). Toward a triarchic theory of human intelligence. Behavioral and Brain Sciences, 7(2), 269–315. https://doi.org/10.1017/s0140525x00044629
  • Suárez-Álvarez, J., Campillo-Álvarez, Á., Fonseca-Pedrero, E., García-Cueto, E., y Muñiz, J. (2013). Professional training in the workplace: The role of achievement motivation and locus of control. The Spanish Journal of Psychology, 16, E35. https://doi.org/10.1017/sjp.2013.19.
  • Suárez-Riveiro, J. M., Martínez-Vicente, M., y Valiente-Barroso, C. (2020). Rendimiento académico según distintos niveles de funcionalidad ejecutiva y de estrés infantil percibido. Psicología Educativa, 26, 77–86. https://doi.org/10.5093/psed2019a17
  • Thurstone, T. G. (1941). Primary mental abilities of children. Educational and Psychological Measurement, 1(1), 103–115. https://doi.org/10.1177/001316444100100110
  • Voyer, D., y Voyer, S. D. (2014). Gender differences in scholastic achievement: A meta-analysis. Psychological Bulletin, 140(4), 1174–1204. https://doi.org/10.1037/a0036620
  • Zare, M., y Flinchbaugh, C. (2018). Voice, creativity, and Big Five personality traits: A meta-analysis. Human Performance, 32(1), 30–51. https://doi.org/10.1080/08959285.2018.1550782