Correo Electrónico
DNINFOA - SIA
Bibliotecas
Convocatorias
Identidad U.N.
Published
Analysis of Academic Data to Group Students According to Their Academic Risk
Agrupamiento de información académica para identificar estudiantes según su riesgo académico
DOI:
https://doi.org/10.15446/rce.v47n2.112870Keywords:
K-means, Fuzzy C-means, Bootstrap clustering, Academic triage. (en)K-medias, Fuzzy C-medias, Bootstrap clustering, Triage académico. (es)
Downloads
The Consillium Academica initiative, spearheaded by the academic vice deanship of the Faculty of Sciences at Universidad Nacional de Colombia in Bogotá, is based on a comprehensive clustering analysis of undergraduate students. This study leverages data spanning from the 2012-1S to 2022-2S academic terms to semi-automatically identify a group of students consistently exhibiting academic underperformance each semester with a potential high risk of academic dropout. The methodology employed in this initiative serves as a proactive measure to identify and support students at risk, to improve the effectiveness of the intervention strategies of the tutor-teacher program, facilitating direct contact between mentors and identified students to provide personalized guidance and academic advisement. This article presents the methodology, key findings, and implications of the Consillium Academica initiative, shedding light on its potential to fortify academic support systems and contribute to the overall success and retention of undergraduate students.
La iniciativa Consillium Academica, liderada por la vice decanatura académica de la Facultad de Ciencias en la Universidad Nacional de Colombia sede Bogotá, lleva a cabo un exhaustivo análisis de agrupación de estudiantes de pregrado. Este estudio aprovecha los datos de los periodos académicos 2012-1S a 2022-2S para identificar de forma semiautomática una cohorte de estudiantes que muestran sistemáticamente un bajo rendimiento académico cada semestre, indicativo de un mayor riesgo de expulsión o abandono académico. La metodología empleada en esta iniciativa sirve como medida proactiva para identificar y apoyar a los estudiantes en situación de riesgo, con el objetivo de mejorar la eficacia de las estrategias de intervención del programa de profesores tutores, el cual, desempeña un papel fundamental en este proceso, facilitando el contacto directo entre los tutores y los estudiantes identificados para proporcionarles orientación personalizada y asesoramiento académico. Este artículo presenta la metodología, los principales resultados y las implicaciones de la iniciativa Consillium Academica, arrojando luz sobre su potencial para fortalecer los sistemas de apoyo académico y contribuir al éxito general y la retención de los estudiantes universitarios.
References
Hennig, C. (2007), 'Cluster-wise assessment of cluster stability', Computational Statistics and Data Analysis 52(1), 258-271.
Nithya, P., Umamaheswari, B. & Umadevi, A. (2016), 'A survey on educational data mining in field of education', International Journal of Advanced Research in Computer Engineering & Technology 5(1), 69-78.
Pardo, C. (2020), Estadística descriptiva multivariada.
Roldán Jiménez, L. S. (2021), 'Machine learning to predict student academic risk in engineering', Encuentro Internacional de Educación en Ingeniería. https://aco-papers.org/index.php/eiei/article/view/1579
Sarra, A., Fontanella, L. & Zio, S. D. (2019), 'Identifying students at risk of academic failure within the educational data mining framework', Social Indicators Research 146(1/2), pp. 41-60. https://www.jstor.org/stable/48704856
Ulriksen, L., Madsen, L. & Holmegaard, H. (2010), 'What do we know about explanations for drop out/opt out among young people from stm higher education programmes?', Studies in Science Education 46, 209-244.
Vendramin, L., Naldi, M. C. & Campello, R. J. G. B. (2015), Fuzzy Clustering Algorithms and Validity Indices for Distributed Data, Springer International Publishing, Cham, pp. 147-192.
How to Cite
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Download Citation
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
