Keywords
Academic performance
Student evaluation
Added value
How to Cite
Abstract
This research presents a model to analyze and predict the performance in quantitative reasoning skills among engineering students in Colombia. The study population included 12 411 engineering students for the year 2020. The input variables used were the competencies in mathematics, science, English, reading, and social studies obtained in the standardized test SABER 11, while the response variable was the performance in quantitative reasoning from the SABER PRO. A descriptive analysis was conducted, considering the variables of gender, school system, and student employment status. Subsequently, a random forest model was implemented, identifying that competencies in mathematics and biology have the greatest partial impact on predicting performance in quantitative reasoning. The predictive model achieved an RMSE of 10.95 and an R² of 69 %, demonstrating its ability to forecast performance in this key competency effectively.
Aparicio, J., Cordero, J. M., & Ortiz, L. (2019). Measuring efficiency in education: The influence of imprecision and variability in data on DEA estimates. Socio-Economic Planning Sciences, 68, 100698. https://doi.org/10.1016/j.seps.2019.03.004
Berens, J., Schneider, K., Gortz, S., Oster, S., & Burghoff, J. (2019). Early Detection of Students at Risk—Predicting Student Dropouts Using Administrative Student Data from German Universities and Machine Learning Methods. Journal of Educational Data Mining, 11(3), 1-41. https://eric.ed.gov/?id=EJ1241620
Bosch, E., Seifried, E., & Spinath, B. (2021). What successful students do: Evidence-based learning activities matter for students’ performance in higher education beyond prior knowledge, motivation, and prior achievement. Learning and Individual Differences, 91, 102056. https://doi.org/10.1016/j. lindif.2021.102056
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
Columbu, S., Porcu, M., & Sulis, I. (2021). University choice and the attractiveness of the study area: Insights on the differences amongst degree programmes in Italy based on generalised mixed-effect models. Socio-Economic Planning Sciences, 74, 100926. https://doi.org/10.1016/j.seps.2020.100926
Delahoz-Dominguez, E. J., Guillen-Ibarra, & Fontalvo-Herrera, T. (2020a). Análisis de la acreditación de calidad en programas de ingeniería industrial y los resultados en las pruebas nacionales estandarizadas, en Colombia. Formación Universitaria, 13(1), 127-134. https://doi.org/10.4067/S0718 50062020000100127
Delahoz-Dominguez, E., Zuluaga, R., & Fontalvo-Herrera, T. (2020b). Dataset of academic performance evolution for engineering students. Data in Brief, 30, 105537. https://doi.org/10.1016/j.dib.2020.105537
Eichler, A., & Gradwohl, J. (2021). Investigating Motivational and Cognitive Factors which Impact the Success of Engineering Students. International Journal of Research in Undergraduate Mathematics Education, 7(3), 417-437. https://doi.org/10.1007/s40753-020-00127-4
Gasevic, D., Rose, C., Siemens, G., Wolff, A., & Zdrahal, Z. (2014). Learning analytics and machine learning. En S. Teasley, & A. Pardo (Eds.), LAK 2014: Fourth International Conference on Learning Analytics and Knowledge (pp. 287-288). Association for Computing Machinery (ACM). https://doi.org/10.1145/2567574.2567633
Gwelo, A. S. (2019). Determinants of career choice among university students. MOJEM: Malaysian Online Journal of Educational Management, 7(1), Article 1.
Hart, P. F., & Rodgers, W. (2024). Competition, competitiveness, and competitive advantage in higher education institutions: A systematic literature review. Studies in Higher Education, 49(11), 2153-2177. https://doi.org/10.1080/03 075079.2023.2293926
Heidrich, L., Victória Barbosa, J. L., Cambruzzi, W., Rigo, S. J., Martins, M. G., & dos Santos, R. B. S. (2018). Diagnosis of learner dropout based on learning styles for online distance learning. Telematics and Informatics, 35(6), 1593 1606. https://doi.org/10.1016/j.tele.2018.04.007
Kamal, N., Sarker, F., Rahman, A., Hossain, S., & Mamun, K. A. (2024). Recommender System in Academic Choices of Higher Education: A Systematic Review. IEEE Access, 12, 35475-35501. https://doi.org/10.1109/ACCESS. 2024.3368058
Kongara, D., & Sathyanarayana, N. (2019). Relief-F and Budget Tree Random Forest Based Feature Selection for Student Academic Performance Prediction. International Journal of Intelligent Engineering and Systems, 12, 30-39. https://doi.org/10.22266/ijies2019.0228.04
Londoño-Vélez, J., Rodríguez, C., & Sánchez, F. (2020). Upstream and Downstream Impacts of College Merit-Based Financial Aid for Low-Income Students: Ser Pilo Paga in Colombia. American Economic Journal: Economic Policy, 12(2), 193-227. https://doi.org/10.1257/pol.20180131
Mayes, R., Long, T., Huffling, L., Reedy, A., & Williamson, B. (2020). Undergraduate Quantitative Biology Impact on Biology Preservice Teachers. Bulletin of Mathematical Biology, 82(6), 63. https://doi.org/10.1007/s11538-020-00740-z
McLaughlin, J. E., Morbitzer, K., Meilhac, M., Poupart, N., Layton, R. L., & Jarst fer, M. B. (2023). Standards needed? An exploration of qualifying exams from a literature review and website analysis of university-wide policies. Studies in Graduate and Postdoctoral Education, 15(1), 19-33. https://doi.org/10.1108/SGPE-11-2022-0073
Nachouki, M., Mohamed, E. A., Mehdi, R., & Abou Naaj, M. (2023). Student course grade prediction using the random forest algorithm: Analysis of predictors’ importance. Trends in Neuroscience and Education, 33, 100214. https://doi.org/10.1016/j.tine.2023.100214
Oates, B. J. (2005). Researching information systems and computing. Sage.
Salazar-Fernandez, J. P., Sepúlveda, M., & Munoz-Gama, J. (2019). Influence of Student Diversity on Educational Trajectories in Engineering High-Failure Rate Courses that Lead to Late Dropout. 2019 IEEE Global Engineering Education Conference (EDUCON), Dubai, United Arab Emirates, pp. 607-616. https://doi.org/10.1109/EDUCON.2019.8725143
Soto-Acevedo, M., Abuchar-Curi, A. M., Zuluaga-Ortiz, R. A., & Delahoz-Dominguez, E. J. (2023). A Machine Learning Model to Predict Standardized Tests in Engineering Programs in Colombia. IEEE Revista Iberoamericana de Tecnologías del Aprendizaje, 18(3), 211-218. https://doi.org/10.1109/rita.2023.3301396
Tossavainen, T., Rensaa, R. J., Haukkanen, P., Mattila, M., & Johansson, M. (2021). First-year engineering students’ mathematics task performance and its relation to their motivational values and views about mathematics. European Journal of Engineering Education, 46(4), 604-617. https://doi.org/10.1080/03043797.2020.1849032
Visbal-Cadavid, D., Martínez-Gómez, M., & Guijarro, F. (2017). Assessing the efficiency of public universities through DEA. A case study. Sustainability, 9(8), 1416.
Wright, M. N., & Ziegler, A. (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 077(i01). https://econpapers.repec.org/article/jssjstsof/v_3a077_3ai01.htm
Zakariya, Y. F., Nilsen, H. K., Bjørkestøl, K., & and Goodchild, S. (2023). Analysis of relationships between prior knowledge, approaches to learning, and mathematics performance among engineering students. International Journal of Mathematical Education in Science and Technology, 54(6), 1015-1033. https://doi.org/10.1080/0020739X.2021.1984596
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