Abstract
The low academic performance rates in the majority of the STEAM (Science, Technology, Engineering, Arts and Math) degrees, is an issue that influences not only the degree quality rates, but also the teaching staff assessments. In this study, 254 graduates of the Industrial Electronics and Automation Engineering degree were monitored over eight academic years. For this purpose, individual academic records were compiled and the individual grades were analyzed. The results showed that there were inter-course differences. Significant differences were also observed in the grades for different subjects. Statistical analysis results also showed that in subjects with low performance rates less than 20% of grades exceeded pass mark. Subjects where final exam does not have a 100% weight in the qualification, performance rates are higher. In addition, significant differences were found between the grades of compulsory and the optional subjects.
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References
IESE: Business school. University of Navarra. Accreditations. ANECA [Online] (2021)
ACSUG: Agency for Quality Assurance in the Galician University System. Guide to the evaluation of university degrees external to the Spanish university system [Online] (2018)
Spanish Ministry of Universities: Facts and Figures on the Spanish University System. Publication. General Technical Secretariat of the Spanish Ministry of University, Madrid (2021)
University of A Coruña: Transparency portal. Academic information [Online]. Available: https://www.udc.es/es/transparencia/resultados/informacion-academica/.
Crespo-Turrado, C., et al.: Comparative study of imputation algorithms applied to the prediction of student performance. Logic J. IGPL 28(1), 58–70 (2020)
Miguéis, V.L., Freitas, A., Garcia, P.J., Silva, A.: Early segmentation of students according to their academic performance: a predictive modelling approach. Decis. Support Syst. 115, 36–51 (2018)
Larson, L.M., Pesch, K.M., Surapaneni, S., Bonitz, V.S., Wu, T., Werbel, J.D.: Predicting graduation: the role of mathematics/science self-efficacy. J. Career Assess. 23(3), 399–409 (2015)
Guerra, R., Marrero, I., Abdul-Jalbar, B., González, R., Darias, N.: Un modelo predictivo del rendimiento académico a partir de las calificaciones de bachillerato y PAU. In: Navarro, A.V. (ed.) De los procesos de cambio al cambio con sentido, pp. 119–136. Vicerrectorado de Docencia. Universidad de La Laguna. Servicio de Publicaciones de la Universidad de La Laguna (2019). https://doi.org/10.25145/b.innovaull.2019.009
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López-Vázquez, J.A., Arce, E., Fernández-Ibáñez, M.I., Casteleiro-Roca, J.L., Zayas, F., Suárez-García, A. (2022). Longitudinal Study of Grades for the Industrial Electronics and Automation Engineering Degree Programme. In: Gude Prego, J.J., de la Puerta, J.G., García Bringas, P., Quintián, H., Corchado, E. (eds) 14th International Conference on Computational Intelligence in Security for Information Systems and 12th International Conference on European Transnational Educational (CISIS 2021 and ICEUTE 2021). CISIS - ICEUTE 2021. Advances in Intelligent Systems and Computing, vol 1400. Springer, Cham. https://doi.org/10.1007/978-3-030-87872-6_29
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DOI: https://doi.org/10.1007/978-3-030-87872-6_29
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