Enfoques de aprendizaje y factores sociodemográficos como predictores del rendimiento académico en estudiantes universitarios Learning approaches and psychosocial factors as predictors of academic performance in college students
Barra lateral del artículo
Contenido principal del artículo
Resumen
La presente investigación analiza los enfoques de aprendizaje y los factores sociodemográficos como predictores del rendimiento académico en estudiantes universitarios en la Universidad Estatal de Bolívar. El rendimiento académico es un indicador clave del éxito profesional de los estudiantes, influenciado no solo por competencias técnicas y cognitivas, sino también por estrategias de aprendizaje y aspectos sociodemográficos. Se fundamenta en teorías previas existentes, destacando la importancia de la motivación y factores sociodemográficos en el desempeño estudiantil. Se desarrolló bajo un enfoque cuantitativo, descriptivo-correlacional, con el uso del método inductivo que partió del análisis de las características particulares de los resultados de investigación para llegar a su generalización y el método analítico para descomponer las variables y determinar la relación existente se utilizó una muestra de 313 estudiantes. Se emplearon técnicas como el Análisis Factorial Exploratorio (AFE) y Análisis Factorial Confirmatorio (AFC) para validar los instrumentos de medición y estructurar las variables en factores subyacentes. Además, se aplicaron ecuaciones estructurales para determinar las relaciones de dependencia entre los enfoques de aprendizaje, los factores sociodemográficos y el rendimiento académico. Los resultados muestran que los enfoques profundos y estratégicos están positivamente asociados con un mejor rendimiento académico, mientras que los enfoques superficiales tienen un impacto limitado. Asimismo, factores sociodemográficos como zona, sexo, niveles educativos de los padres y tipo de familia se identificaron como elementos que influyen en el rendimiento académico. el estudio destaca la necesidad de implementar estrategias educativas que fortalezcan los enfoques de aprendizaje efectivos y la eliminación de brechas demográficas. Permitirá a las instituciones de educación superior promover el éxito académico, así como mejorar los procesos de enseñanza-aprendizaje
Descargas
Detalles del artículo
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Citas
Abbiati, M., Savoldelli, G., Baroffio, A., & Bajwa, N. (2020). Motivational factors influencing student intentions to practise in underserved areas. Medical Education, 54(4), 356 - 363. https://doi.org/10.1111/medu.14063
Acosta-Enriquez, B., Guzmán, M., Arbulú, M., Arbulú, J., Arbulu, C., & Torres, S. (2025). What is the influence of psychosocial factors on artificial intelligence appropriation in college students? BMC Psychology, 13(1). https://doi.org/10.1186/s40359-024-02328-x
Alnasraween, M., & Al-Samadi, M. (2024). The Factorial Structure of the Self-Control and Self-Management (SMCS) Scale Using Exploratory and Confirmatory Factor Analysis among Jordanian University Students. An-Najah University Journal for Research - B (Humanities), 38(8), 1623 - 1650. https://doi.org/10.35552/0247.38.8.2245
Anders, P., Davis, E., & McCall, J. (2020). Psychometric properties of an instrument to assess critical thinking disposition and metacognition in dental students. Journal of Dental Education, 84(5), 559 - 565. https://doi.org/10.1002/jdd.12038
Asare, P. (2025). Cognitive strain and performance reflection: Unpacking Financial Management-induced test anxiety across educational programmes, age, and gender. International Journal of Management Education, 23(2). https://doi.org/10.1016/j.ijme.2025.101162
Ausubel, D. P. (1986). Educational psyology, A cognitive view. New York: Holt, Rinehart and Winston.
Awang-Hashim, R., Yusof, N., Kanageswari, S., Shanmugam, S., Kaur, A., & Benlahcene, A. (2023). Psychometric Properties of the Quality of Undergraduate Learning Experiences in Malaysian Universities. Asia Pacific Journal of Educators and Education, 38(1), 33 - 53. https://doi.org/10.21315/apjee2023.38.1.3
Axiotidou, M., Koutroulou, A., Karapanayiotides, T., & Papakonstantinou, D. (2025). Prevalence, triggers, and impact of migraine on university students: a scoping review. Egyptian Journal of Neurology, Psychiatry and Neurosurgery, 61(1). https://doi.org/10.1186/s41983-025-00945-w
Barrón, H., & Mitma, Y. (2017). Enfoques de aprendizaje y rendimiento académico en estudiantes de medicina del primer año de la Universidad Nacional Mayor de San Marcos. Anales de la Facultad de Medicina.
Bazán-Perkins, B., & Santibañez-Salgado, J. (2025). Relationship between the learning gains and learning style preferences among students from the school of medicine and health sciences. BMC Medical Education, 25(1). https://doi.org/10.1186/s12909-024-06554-0
Benka-Coker, G., Young, B., Oliver, S., Schaeffer, J., Manning, M., Suter, J., . . . Magzamen, S. (2021). Sociodemographic variations in the association between indoor environmental quality in school buildings and student performance. Building and Environment.
Bonsaksen, T., Magne, T., & Stigen, L. (2020). Associations between occupational therapy students’ academic performance and their study approaches and perceptions of the learning environment. BMC Medical Education. https://doi.org/https://doi.org/10.1186/s12909-021-02940-0
Brown, S., White, S., Wakeling, L., & Naiker, M. (2015). Approaches and Study Skills Inventory for Students (ASSIST) in an Introductory Course in Chemistry. Journal of University Teaching & Learning Practice, 12(3). https://doi.org/http://ro.uow.edu.au/jutlp/vol12/iss3/6
Cadena-Povea, H., Hernández-Martínez, M., Bastidas-Amador, G., & Calderón-Muñoz, J. (2025). Perceived Stress: Psychosocial-Sociodemographic Factors as Predictors of Tension, Irritability, and Fatigue Among Ecuadorian University Professors. International Journal of Environmental Research and Public Health, 22(1). https://doi.org/10.3390/ijerph22010107
Cheng-Hsien, L. (2016). The performance of ML, DWLS, and ULS estimation with robust corrections in structural equation models with ordinal variables. Psychological Methods, 21(3), 369 - 387. https://doi.org/10.1037/met0000093
Chik, Z., & Hakim, A. (2018). Developing and Validating Instruments for Measurement of Motivation, Learning Styles and Learning Disciplines for Academic Achievement. International Journal of Academic Research in Bussines and Social Science, 8(4), 594 - 605. https://doi.org/10.6007/IJARBSS/v8-i4/4035
Chin, Y., Chua, N., Muhammad, N., Mohammad, W., & Siew, N. (2024). Psychometric evaluation: exploratory and confirmatory factor analysis of the malay version of the student stress inventory (ssi) for university students. Jurnal Ilmi, 14(1), 20-35.
Cristea, T. S., Heikkinen, S., Snijders, S., Saqr, M., & Matzat, U. (2025). Dynamics of self-regulated learning: The effectiveness of students’ strategies across course periods. Computers and Education, 28. https://doi.org/10.1016/j.compedu.2025.105233
Curelaru, M., & Curelaru, V. (2025). Psychological Distress and Online Academic Difficulties: Development and Validation of Scale to Measure Students’ Mental Health Problems in Online Learning. Behavioral Sciences, 15(1). https://doi.org/10.3390/bs15010026
da Cruz, I., & Esperidião, F. (2024). Difference in educational performance between students from rural and urban areas in Brazil: An unconditional quantile analysis. Nova Economia, 34(3). https://doi.org/10.1590/0103-6351/8384
de la Fuente, J., Malpica-Chavarria, E., Garzón-Umerenkova, A., & Pachón-Basallo, M. (2021). Effect of Personal and Contextual Factors of Regulation on Academic Achievement during Adolescence: The Role of Gender and Age. Int. J. Environ. Res. Public Health, 18(17). https://doi.org/https://doi.org/10.3390/ijerph18178944
Elsayed, A., Wardat, Y., Alawaed, M., & Albaraami, Y. (2025). The effect of employing project-web learning approach in teaching mathematics instruction methods course on developing the mind habits among Dhofar University students. Eurasia Journal of Mathematics, Science and Technology Education, 21(2). https://doi.org/10.29333/ejmste/15930
Entwistle, N. (1988). Motivational factors in students’ Approaches to Learning, In Schmeck, R.R. (ed.) Learning Strategies and Learning Styles. New York: Plenum Press.
Erçetin, S., Güngör, H., & Hamedoğlu, M. (2020). Academic Success Scale: Second-Order Confirmatory and Exploratory Factor Analysis. International Journal of Educational Research Review, 5(3), 178-189. https://doi.org/https://doi.org/10.24331/ijere.727245
Escobedo, F., Córdova, E., Flores, C., Clavijo-López2, R., Cruz-Tarrillo, J., & Sánchez, R. (2024). Virtual Education and Post-Pandemic Academic Performance in University Students. Academic Journal of Interdisciplinary Studies, 13(4), 481-492.
Fernández-Aráuz, A. (2015). Aplicación del análisis factorial confirmatorio a un modelo de medición del rendimiento académico en lectura. Revista De Ciencias Económicas, 33(2), 39-65. https://doi.org/https://doi.org/10.15517/rce.v33i2.22216
Hayat, A., Shateri, K., Amini, M., & Shokrpour, N. (2020). Relationships between academic self-efficacy, learning-related emotions, and metacognitive learning strategies with academic performance in medical students: a structural equation model. BMC Medical Education, 20(76). https://doi.org/https://doi.org/10.1186/s12909-020-01995-9
Intan, A., Ramlee, I., & Khoo, Y. (2022). Validation of Learning Style Instruments and Holistic Intelligence on Achievement of Form Six Economics Students: Exploratory Factor Analysis (EFA). International Journal of Academic research in Progressive Education and Development, 11(2), 474–493. https://doi.org/10.6007/IJARPED/v11-i2/13237
Jamil, N., Baharuddin, F., & Ratul, T. (2015). Factors Mining in Engaging Students Learning Styles Using. International Accouting and business conference 2015, IABC 2015. https://doi.org/10.1016/S2212-5671(15)01161-2
Jiménez, R., Dalmau, J., & Gargallo, E. (2024). Factors associated with academic performance in adolescents from La Rioja (Spain): lifestyle habits, health indicators, and sociodemographic factors. Nutricion Hospitalaria, 41(1), 19 - 27. https://doi.org/10.20960/nh.04599
Lai, K., & Simoes, S. (2023). Reflecting on the “Robust” Standard Errors for Two-Stage SEM Estimation With Categorical Data: Mistakes and Correction. Structural Equation Modeling, 30(5), 691 - 707. https://doi.org/10.1080/10705511.2022.2141246
Lévy-Mangin, J., & Varela-Mallou, J. (2006). Modelización con estructuras de covarianzas en ciencias sociales. Temas esenciales, avanzados y aportaciones especiales. Netbiblo.
MacCallum, R. C. (2001). Sample size in factor analysis: The role of model error. Multivariate Behavioral Research, 36(4), 611-637. https://doi.org/10.1207/S15327906MBR3604_06
Mahdavi, p., Valibeygi, A., & Sadeghi, S. (2021). Relationship Between Achievement Motivation, Mental Health and Academic Success in University Students. Community Health Equity Research & Policy, 43(3). https://doi.org/Sadeghi
Masa’Deh, R., AlAzzam, M., Al-Dweik, G., Masadeh, O., Hamdan-Mansour, A., & Basheti, I. (2021). Academic performance and socio-demographic characteristics of students: Assessing moderation effect of self-esteem. International Journal of School & Educational Psychology , 9(4). https://doi.org/https://doi.org/10.1080/21683603.2021.1901811
Mavrou, I. (2015). Análisis factorial exploratorio:cuestiones conceptuales y metodológicas. Revista Nebrija.
Méndez-Martínez, C., & Rondón-Sepúlveda, M. A. (2012). Introducción al análisis factorial exploratorio. Revista Colombiana de Psiquiatría, 41(1), 197-207. https://doi.org/https://doi.org/10.1016/S0034-7450(14)60077-9
Merchant, A., Afzal, N., Rahim, K., Shah, S., & Jamal, W. (2025). Application to achievement: association between pre-admission factors, admission scores, and medical students’ performance. BMC Medical Education, 25(1). https://doi.org/10.1186/s12909-025-06800-z
Miličević, A., Despotović-Zrakić, M., Stojanović, D., & Suvajžić, M. (2024). Academic performance indicators for the hackathon learning approach – The case of the blockchain hackathon. Journal of Innovation and Knowledge, 9(3). https://doi.org/10.1016/j.jik.2024.100501
Muca, E., Molino, M., Ghislieri, C., Baratta, M., Odore, R., Bergero, D., & Valle , E. (2023). Relationships between psychological characteristics, academic fit and engagement with academic performance in veterinary medical students. BMC Veterinary Research, 19(132). https://doi.org/https://doi.org/10.1186/s12917-023-03695-0
Norouzian, R., & Plonsky, L. (2018). Eta- and partial eta-squared in L2 research: A cautionary review and guide to more appropriate usage. Second Language Research. https://doi.org/https://doi.org/10.1177/026765831668490
Nozaleda, B., Dayag-Tungpalan, M., Arao, H., & Ramos, C. (2025). Cluster analysis of learning styles and ICT competence: Towards a typology of flexible learners in higher education. Multidisciplinary Reviews, 8(5). https://doi.org/10.31893/multirev.2025144
Ocaña-Moral, M., Gavín-Chocano, Ó., Pérez-Navío, E., & Martínez-Serrano, M. (2021). Relationship among Perceived Stress, Life Satisfaction and Academic Performance of Education Sciences Students of the University of Jaén after the COVID-19 Pandemic. 11(12). https://doi.org/Martínez-Serrano
Okwuduba, E., Nwosu, K., Okigbo, E., & Samuel, N. (2021). Impact of intrapersonal and interpersonal emotional intelligence and self-directed learning on academic performance among pre-university science students. Heliyon, 7(3).
Ortiz-Gómez, M., Lizarte-Simón, E., & Mingorance-Estrada, Á. (2025). The attitudes towards mathematics: analysis in a multicultural context. Humanities and Social Sciences Communications, 12(1). https://doi.org/10.1057/s41599-025-04548-x
Ouahi, M., Ait, M., Bliya, A., Hassouni, T., & Al Ibrahmi, E. (2021). The Effect of Using Computer Simulation on Students’ Performance in Teaching and Learning Physics: Are There Any Gender and Area Gaps? Education Research International. https://doi.org/https://doi.org/10.1155/2021/6646017
Oyeniran, S., Ayanwale, M., Atolagbe, A., & Mochekele, M. (2025). Construction and validation of goal achievement scale for colleges of education. Journal of Applied Research in Higher Education, 17(7), 131 - 150. https://doi.org/10.1108/JARHE-07-2024-0336
Pellas, N. (2023). The influence of sociodemographic factors on students' attitudes toward AI-generated video content creation. Smart Learning Environments, 10(57). https://doi.org/https://doi.org/10.1186/s40561-023-00276-4
Piscitello, J., Youn-kyoung, K., Orooji, M., & Robison, S. (2022). Sociodemographic risk, school engagement, and community characteristics: A mediated approach to understanding high school dropout. Children and Youth Services Review, 133. https://doi.org/https://doi.org/10.1016/j.childyouth.2021.106347
Rajendran, S., Chamundeswari, S., & Amitanand, A. (2022). Predicting the academic performance of middle- and high-school students using machine learning algorithms. Social Sciences & Humanities Open, 6(1). https://doi.org/https://doi.org/10.1016/j.ssaho.2022.100357
Richardson, J. (2011). Eta squared and partial eta squared as measures of effect size in educational research. Educational Research Review, 6(2), 135-147. https://doi.org/https://doi.org/10.1016/j.edurev.2010.12.001
Saha, M., Islam, S., Akhi, A., & Saha, G. (2024). Factors affecting success and failure in higher education mathematics: Students' and teachers’ perspectives. Heliyon, 10(7). https://doi.org/10.1016/j.heliyon.2024.e29173
Samir, A., Elamir, A., Basyouni, M., Goudy, Y., Elbarbary, K., & El-Mezayen, M. (2025). Sociodemographic, lifestyle, and psychological factors as controllable predictors of academic self-efficacy after reforming a medical education system; the Egyptian Nationwide experience. BMC Medical Education, 25(1). https://doi.org/10.1186/s12909-025-06805-8
Selvitopu, A., & Kaya, M. (2021). A Meta-Analytic Review of the Effect of Socioeconomic Status on Academic Performance. Studies in Educational Evaluation, 36(4). https://doi.org/https://doi.org/10.1177/00220574211031978
Silva, A., Vautero, J., & Usssene, C. (2021). The influence of family on academic performance of Mozambican university students. International Journal of Educational Development, 87. https://doi.org/https://doi.org/10.1016/j.ijedudev.2021.102476
Smit, B. (2021). Introduction to ATLAS.ti for Mixed Analysis.
Tadese, M., Yeshaneh, A., & Baye Mulu, G. (2022). Determinants of good academic performance among university students in Ethiopia: a cross-sectional study. BMC Medical Education, 22. https://doi.org/https://doi.org/10.1186/s12909-022-03461-0
Tait, H., Entwistle, N., & McCune, V. (1998). A Reconceptualization of the approaches to Studying Inventory. Oxford Brookes University, The Oxford Center for Staff and Learning Development.
Tayyaba, S. (2012). Rural‐urban gaps in academic achievement, schooling conditions, student, and teachers' characteristics in Pakistan. International Journal of Educational Management, 26(1), 6-26. https://doi.org/https://doi.org/10.1108/09513541211194356
Ullah, R. (2016). Learning environment, approaches to learning and learning preferences: Medical students versus general education students. Journal of Pakistám Medical Association, 541-544.
Urbano, L., & Pere, F. (2021). Not Positive Definite Correlation Matrices in Exploratory Item Factor Analysis: Causes, Consequences and a Proposed Solution. Structural Equation Modeling, 28(1), 138 - 147. https://doi.org/https://doi.org/10.1080/10705511.2020.1735393
Usán, P., Salavera, C., & Quílez-Robres, A. (2021). Self-Efficacy, Optimism, and Academic Performance as Psychoeducational Variables: Mediation Approach in Students. Physical Education, Physical Activity, and Health Education in Children and Adolescents, 9(3). https://doi.org/https://doi.org/10.3390/children9030420
Vîrgă, D., & Okros, N. (2024). Why should you believe in yourself? Students' performance-approach goals shape their approach to learning through self-efficacy: A longitudinal analysis. European Journal of Education, 59(2). https://doi.org/10.1111/ejed.12624
Willison, J., Draper, C., Fornarino, L., Li, M., Sabri, T., & Shi, Y. (2024). Metacognitively ALERT in science: literature synthesis of a hierarchical framework for metacognition and preliminary evidence of its viability. Studies in Science Education , 60(2). https://doi.org/https://doi.org/10.1080/03057267.2023.2207147
Wittrock, M. C. (1974). Learning as a generative process. Educational Psycologist, 87-95.
Zafeer, H., Maqbool, S., Rong, Y., & Maqbool, S. (2025). Beyond the Classroom: How Socioeconomic Status, Parental Involvement and Home Environment Impact on Students' Science Academic Performance at Secondary Schools. European Journal of Education, 60(1). https://doi.org/10.1111/ejed.70023
Zafeer, K., Maqbool, S., Rong, Y., & Maqbool, S. (2025). Beyond the Classroom: How Socioeconomic Status, Parental Involvement and Home Environment Impact on Students' Science Academic Performance at Secondary Schools. European Journal of Education, 60(1). https://doi.org/10.1111/ejed.70023