Identification of university students at risk due to gender using the Kostick test
DOI:
https://doi.org/10.56162/transdigital603Keywords:
competence, aptitude test, quantitative analysis, higher education, teachingAbstract
The aim of this research was to identify and analyze the characteristics and needs of incoming university students, in order to address them and facilitate their academic performance. To this end, Kostick test was administered to 1,922 students. A quantitative analysis was conducted, highlighting the leadership competency (5.61), indicating a strength to be considered in any academic activity inside and outside the classroom. They advocate for intense academic activities (6.41) in terms of physical and mental effort. They recognize that they need to improve their lifestyle (diet, exercise, rest, time management, habits). Achievement has ceased to be their primary motivator, with the lowest score (4.05). The study provides valuable information for university administrators, faculty, and student support services to develop specific interventions and programs that support students' comprehensive development and improve their chances of success in higher education.
References
AFB Compucenter. (2021). Kostick (inventario de la percepción): Manual del Examinador. AFB Compucenter. https://afbcompucenter.com/blog/wp-content/uploads/2021/02/Kostick-Manual.pdf
Aguiar, E., Ambrose, G. A. A., Chawla, N. V., Goodrich, V., & Brockman, J. (2014). Engagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Persistence. Journal of Learning Analytics, 1(3), 7-33. https://doi.org/10.18608/jla.2014.13.3
Alwarthan, S., Aslam, N., y Khan, I. U. (2022). An Explainable Model for Identifying At-Risk Student at Higher Education. IEEE Access, 10, 107649-107668. https://doi.org/10.1109/ACCESS.2022.3211070
Caviglia-Harris, J., & Maier, K. (2020). It’s not all in their heads: The differing role of cognitive factors and non-cognitive traits in undergraduate success. Education Economics, 28(3), 245-262.
Chemers, M. M., Hu, L., & Garcia, B. F. (2001). Academic self-efficacy and first year college student performance and adjustment. Journal of Educational Psychology, 93(1), 55-64. https://doi.org/10.1037/0022-0663.93.1.55
Credé, M., & Kuncel, N. R. (2008). Study Habits, Skills, and Attitudes: The Third Pillar Supporting Collegiate Academic Performance. Perspectives on Psychological Science, 3(6), 425-453.
Franestian, I. D., Suyanta, & Wiyono, A. (2020). Analysis problem solving skills of student in Junior High School. Journal of Physics: Conference Series, 1440(1), 012089. https://doi.org/10.1088/1742-6596/1440/1/012089
Glandorf, D., Lee, H. R., Orona, G. A., Pumptow, M., Yu, R., & Fischer, C. (2024). Temporal and Between-Group Variability in College Dropout Prediction [Sesión de congreso]. 14th Learning Analytics and Knowledge Conference, Kyoto, Japan.
Guha, R., Wagner, T., Darling-Hammond, L., Taylor, T., & Curtis, D. (2018). The Promise of Performance Assessments: Innovations in High School Learning and Higher Education Admissions. Learning Policy Institute.
Jaramillo Flores, P. D. (2024). Aplicación de algoritmos predictivos para mejorar la retención y el éxito académico en la educación superior. Revista Multidisciplinaria de Desarrollo Agropecuario Tecnólogico, Empresarial y humanista, 6(2). https://doi.org/10.61236/dateh.v6i2.944
Kuo, M.-M., Li, X., Qian, L., Obiomon, P., & Dong, X. (2024). Deep Knowledge Tracing for Personalized Adaptive Learning at Historically Black Colleges and Universities. arXiv. https://doi.org/10.48550/arXiv.2410.13876
Malkoç, A., & Mutlu, A. K. (2018). Academic Self-efficacy and Academic Procrastination: Exploring the Mediating Role of Academic Motivation in Turkish University Students. Universal Journal of Educational Research, 6(10), 2087-2093. https://doi.org/10.13189/ujer.2018.061005
Mansfield, P. M., Pinto, M. B., Parente, D. H., & Wortman, T. I. (2004). College Students and Academic Performance: A Case of Taking Control. NASPA Journal, 41(3), 551-567.
Martin, A. J., Marsh, H. W., Williamson, A., & Debus, R. L. (2003). Self-handicapping, defensive pessimism, and goal orientation: A qualitative study of university students. Journal of Educational Psychology, 95(3), 617-628. https://doi.org/10.1037/0022-0663.95.3.617
Masia, C. L., & Chase, P. N. (1997). Vicarious learning revisited: A contemporary behavior analytic interpretation. Journal of Behavior Therapy and Experimental Psychiatry, 28(1), 41-51.
Matz, S. C., Bukow, C. S., Peters, H., Deacons, C., Dinu, A., & Stachl, C. (2023). Using machine learning to predict student retention from socio-demographic characteristics and app-based engagement metrics. Scientific Reports, 13(1), 5705.
Mayer, J. D., & Salovey, P. (1997). What is emotional intelligence. En P. Salovey & D. J. Sluyter (Eds.), Emotional development and emotional intelligence: Educational implications (pp. 3–34). Basic Books.
Mccausland, D. F., & Stewart, N. E. (1974). Academic Aptitude, Study Skills, and Attitudes and College GPA. The Journal of Educational Research, 67(8), 354-357. https://doi.org/10.1080/00220671.1974.10884654
Merante, J. A. (1983). Predicting student success in college: what does the research say? NASSP Bulletin, 67(460), 41-46.
Oussedik, E., Foy, C. G., Masicampo, E. J., Kammrath, L. K., Anderson, R. E., & Feldman, S. R. (2017). Accountability: A missing construct in models of adherence behavior and in clinical practice. Patient preference and adherence, 11, 1285-1294.
Roque Rodríguez, E. (2024). Iluminemos el camino de aquellos que han tropezado para que puedan salir adelante. RIDE Revista Iberoamericana para la Investigación y el Desarrollo Educativo, 15(29). https://doi.org/10.23913/ride.v15i29.2049
Rubio-Tobar, X. (2025). Factores de deserción estudiantil y estrategias de retención en carreras universitarias de Ingeniería y Áreas Técnicas. Revista Científica FINIBUS - Ingeniería, Industria y Arquitectura, 8(15), 133–142. https://doi.org/10.56124/finibus.v8i15.014
Smith, M. A. (2021). Social Learning and Addiction. Behavioural Brain Research, 398, 112954. https://doi.org/10.1016/j.bbr.2020.112954
Soobramoney, R., & Singh, A. (2019). Identifying Students At-Risk with an Ensemble of Machine Learning Algorithms [Sesión de congreso]. 2019 Conference on Information Communications Technology and Society (ICTAS), Durban, South Africa.
Tamhane, A., Ikbal, S., Sengupta, B., Duggirala, M., & Appleton, J. (2014). Predicting student risks through longitudinal analysis [Sesión de congreso]. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, Jeju Island, Republic of Korea.
Tross, S. A., Harper, J. P., Osher, L. W., & Kneidinger, L. M. (2000). Not just the usual cast of characteristics: Using personality to predict college performance and retention. Journal of College Student Development, 41(3), 323–334.
Universidad de Guadalajara. (2024). Regresan al CUCEA 22,592 estudiantes para el Ciclo Escolar 2024-A. Página web oficial de la Universidad de Guadalajara. https://sitioanterior.cucea.udg.mx/es/noticia/16-ene-2024/regresan-al-cucea-22592-estudiantes-para-el-ciclo-escolar-2024
Downloads
Autor de correspondencia
El autor de correspodencia se identifica con el siguiente símbolo: *Published
How to Cite
License
Copyright (c) 2026 Ernesto Roque Rodríguez, Blanca Zamora Mata
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles in Transdigital are licensed under a Creative Commons Attribution 4.0 International License. Authors hold the copyright and retain publishing rights without restrictions.
