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Article

Curricular Analytics to Characterize Educational Trajectories in High-Failure Rate Courses That Lead to Late Dropout

1
Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
2
Institute of Informatics, Universidad Austral de Chile, Valdivia 5110701, Chile
*
Author to whom correspondence should be addressed.
Academic Editor: Carlos Alario-Hoyos
Appl. Sci. 2021, 11(4), 1436; https://doi.org/10.3390/app11041436
Received: 11 January 2021 / Revised: 31 January 2021 / Accepted: 2 February 2021 / Published: 5 February 2021
(This article belongs to the Special Issue Advanced Technologies in Lifelong Learning)
Late dropout is one of the most pressing challenges currently facing higher education, and the process that each student follows to arrive at that decision usually involves several academic periods. This work presents a curricular analytics approach at the program level, to analyze how educational trajectories of undergraduate students in high-failure rate courses help to describe the process that leads to late dropout. Educational trajectories (n = 10,969) of high-failure rate courses are created using Process Mining techniques, and the results are discussed based on established theoretical frameworks. Late dropout was more frequent among students who took a stopout while having high-failure rate courses they must retake. Furthermore, students who ended in late dropout with high-failure rate courses they must retake had educational trajectories that were on average shorter and less satisfactory. On the other hand, the educational trajectories of students who ended in late dropout without high-failure rate courses they must retake were more similar to those of students who graduated late. Moreover, some differences found among ISCED fields are also described. The proposed approach can be replicated in any other university to understand the educational trajectories of late dropout students from a longitudinal perspective, generating new knowledge about the dynamic behavior of the students. This knowledge can trigger improvements to the curriculum and in the follow-up mechanisms used to increase student retention. View Full-Text
Keywords: learning analytics; curricular analytics; process mining; educational trajectories; dropout; higher education; stopout learning analytics; curricular analytics; process mining; educational trajectories; dropout; higher education; stopout
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MDPI and ACS Style

Salazar-Fernandez, J.P.; Sepúlveda, M.; Munoz-Gama, J.; Nussbaum, M. Curricular Analytics to Characterize Educational Trajectories in High-Failure Rate Courses That Lead to Late Dropout. Appl. Sci. 2021, 11, 1436. https://doi.org/10.3390/app11041436

AMA Style

Salazar-Fernandez JP, Sepúlveda M, Munoz-Gama J, Nussbaum M. Curricular Analytics to Characterize Educational Trajectories in High-Failure Rate Courses That Lead to Late Dropout. Applied Sciences. 2021; 11(4):1436. https://doi.org/10.3390/app11041436

Chicago/Turabian Style

Salazar-Fernandez, Juan P., Marcos Sepúlveda, Jorge Munoz-Gama, and Miguel Nussbaum. 2021. "Curricular Analytics to Characterize Educational Trajectories in High-Failure Rate Courses That Lead to Late Dropout" Applied Sciences 11, no. 4: 1436. https://doi.org/10.3390/app11041436

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