Mostrar el registro sencillo del ítem
Mining Pre-Grade Academic and Demographic Data to Predict University Dropout
dc.rights.license | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.contributor.author | Martínez-Navarro Á. | |
dc.contributor.author | Verdú E. | |
dc.contributor.author | Moreno-Ger P. | |
dc.date.accessioned | 2024-12-02T20:15:46Z | |
dc.date.available | 2024-12-02T20:15:46Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 21964963 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14112/28958 | |
dc.description.abstract | Digital transformation is enabling institutions to enhance their processes by using data and technology. In education, digital transformation allows improving the learning experience as well as the institution processes. Within education 4.0, artificial intelligence applied to learning analytics is playing a key role for universities, particularly in the dropout issue, especially in STEM with the highest dropout rates. This is particularly relevant in the Latin American Higher Education scope, given the low labour productivity in these countries. In these countries, universities often have more demand than supply, and achieving an adequate balance between admission rates and dropout rates is a key issue. A high dropout rate harms the prestige of the university and damages students who were admitted without being adequate candidates. Understanding why students abandon their studies help to know what a university can do to avoid it. Data mining (DM) techniques can help discover the individual features that influence the dropout. There are different studies proposing models to predict dropout, and most are based on data that are not at the admission stage. We propose an approach that uses DM techniques to predict dropout based on data at the admission stage. We discover factors influencing dropout by a decision tree and association rules. We use a dataset of students of a computer science degree from a University in South America and achieve good performance when predicting dropout. The most attributes influencing dropout are the pre-grade performance in STEM subjects and the location of the city of residence. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. | |
dc.format | 18 | |
dc.format.medium | Recurso electrónico | |
dc.format.mimetype | application/pdf | |
dc.language.iso | eng | |
dc.publisher | Springer Science and Business Media Deutschland GmbH | |
dc.rights.uri | Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) | |
dc.source | Lecture Notes in Educational Technology | |
dc.source | Lect. Notes Educ. Technol. | |
dc.source | Scopus | |
dc.title | Mining Pre-Grade Academic and Demographic Data to Predict University Dropout | |
datacite.contributor | Universidad Mariana, Calle 18 No. 34-104, Pasto, Nariño, Colombia | |
datacite.contributor | Edifico Bella Vista del Norte, Calle 11 No. 40-54, Pasto, Nariño, AP301, Colombia | |
datacite.contributor | Universidad Internacional de La Rioja, Av. de la Paz, 137, Logroño, La Rioja, 26006, Spain | |
datacite.contributor | Martínez-Navarro Á., Universidad Mariana, Calle 18 No. 34-104, Pasto, Nariño, Colombia, Edifico Bella Vista del Norte, Calle 11 No. 40-54, Pasto, Nariño, AP301, Colombia | |
datacite.contributor | Verdú E., Universidad Internacional de La Rioja, Av. de la Paz, 137, Logroño, La Rioja, 26006, Spain | |
datacite.contributor | Moreno-Ger P., Universidad Internacional de La Rioja, Av. de la Paz, 137, Logroño, La Rioja, 26006, Spain | |
datacite.rights | http://purl.org/coar/access_right/c_abf2 | |
oaire.resourcetype | http://purl.org/coar/resource_type/c_3248 | |
oaire.version | http://purl.org/coar/version/c_ab4af688f83e57aa | |
dc.contributor.contactperson | Á. Martínez-Navarro | |
dc.contributor.contactperson | Universidad Mariana, Pasto, Nariño, Calle 18 No. 34-104, Colombia | |
dc.contributor.contactperson | email: amartinez@umariana.edu.co | |
dc.identifier.doi | 10.1007/978-981-16-3941-8_11 | |
dc.identifier.instname | Universidad Mariana | |
dc.identifier.reponame | Repositorio Clara de Asis | |
dc.identifier.url | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111657353&doi=10.1007%2f978-981-16-3941-8_11&partnerID=40&md5=a5002ca709e8035b07d302a422517ce0 | |
dc.relation.citationendpage | 215 | |
dc.relation.citationstartpage | 197 | |
dc.relation.iscitedby | 3 | |
dc.relation.references | Abad-Segura E., Gonzalez-Zamar M.-D., Infante-Moro J.C., Ruiperez Garcia G., Sustainable management of digital transformation in higher education: Global research trends, Sustainability, 12, 5, (2020) | |
dc.relation.references | Arias Ortiz E., Dehon C., Roads to success in the Belgian French community’s higher education system: Predictors of dropout and degree completion at the Université Libre de Bruxelles, Research in Higher Education, 54, 6, pp. 693-723, (2013) | |
dc.relation.references | Asha P., Vandana E., Bhavana E., Shankar K.R., Predicting university dropout through data analysis, 4Th International Conference on Trends in Electronics and Informatics, ICOEI, 2020, pp. 852-856, (2020) | |
dc.relation.references | Barbe T., Kimble L.P., Bellury L.M., Rubenstein C., Predicting student attrition using social determinants: Implications for a diverse nursing workforce, Journal of Professional Nursing, 34, 5, pp. 352-356, (2018) | |
dc.relation.references | Benhacine F.Z., Atmani B., Abdelouhab F.Z., Contribution to the association rules visualization for decision support: A combined use between Boolean modeling and the colored 2D matrix. International Journal of Interactive Multimedia and, Artificial Intelligence, 5, 5, pp. 38-47, (2019) | |
dc.relation.references | Canton-Roda R.-M., Gibaja-Romero D.-E., Castillo-Villar F.-R., The promotion of graduate programs through clustering prospective students. International Journal of Interactive Multimedia and, Artificial Intelligence, 5, 6, pp. 23-32, (2019) | |
dc.relation.references | Dutt A., Ismail M.A., Herawan T., A systematic review on educational data mining, IEEE Access, 5, pp. 15991-16005, (2017) | |
dc.relation.references | Garcia-Ros R., Perez-Gonzalez F., Cavas-Martinez F., Tomas J.M., Effects of pre-college variables and first-year engineering students’ experiences on academic achievement and retention: A structural model, International Journal of Technology and Design Education, (2018) | |
dc.relation.references | Hamoud A.K., Hashim A.S., Awadh W.A., Predicting student performance in higher education institutions using decision tree analysis. International Journal of Interactive Multimedia and, Artificial Intelligence, 5, 2, pp. 26-31, (2018) | |
dc.relation.references | Henderikx M.A., Kreijns K., Kalz M., Refining success and dropout in massive open online courses based on the intention–behavior gap, Distance Education, 38, 3, pp. 353-368, (2017) | |
dc.relation.references | Hilliger I., Ortiz-Rojas M., Pesantez-Cabrera P., Scheihing E., Tsai Y.-S., Munoz-Merino P.J., Broos T., Whitelock-Wainwright A., Perez-Sanagustin M., Identifying needs for learning analytics adoption in Latin American universities: A mixed-methods approach, The Internet and Higher Education, 45, (2020) | |
dc.relation.references | Iam-On N., Boongoen T., Generating descriptive model for student dropout: A review of clustering approach, Human-Centric Computing and Information Sciences, 7, 1, (2017) | |
dc.relation.references | Lange P., Neumann A.T., Nicolaescu P., Klamma R., An integrated learning analytics approach for virtual vocational training centers. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 32–38. https://doi.org/10.9781/ijimai.2018.02.006 Martínez-Navarro, Á., & Moreno-Ger, P. (2018). Comparison of Clustering Algorithms for Learning Analytics with Educational Datasets. International Journal of Interactive Multimedia and Artificial Intelligence, 5, 2, pp. 9-16, (2018) | |
dc.relation.references | Meedech P., Iam-On N., Boongoen T., Prediction of student dropout using personal profile and data mining approach. In Lavangnananda, K., Phon-Amnuaisuk, S., Engchuan, W., & Chan, J. H. (Eds.), Intelligent and evolutionary systems (Vol. 5, pp. 143–155), Springer International Publishing, (2016) | |
dc.relation.references | Mendoza P., Autonomy and weak governments: Challenges to university quality in Latin America, Higher Education, 719-737, 2020, (2020) | |
dc.relation.references | Mikheev A., Serkina Y., Vasyaev A., Current trends in the digital transformation of higher education institutions in Russia, Education and Information Technologies, (2021) | |
dc.relation.references | Pappas I.O., Giannakos M.N., Jaccheri L., Investigating Factors Influencing students’ Intention to Dropout Computer Science Studies, pp. 198-203, (2016) | |
dc.relation.references | Revinova S., Chavarry Galvez D.P., E-government and government support for the digital economy in Latin America and the Caribbean, Proceedings of the 2Nd International Scientific and Practical Conference on Modern Management Trends and the Digital Economy: From Regional Development to Global Economic Growth (MTDE, 2020, pp. 1003-1011, (2020) | |
dc.relation.references | Ritz J.M., Fan S., STEM and technology education: International state-of-the-art, International Journal of Technology and Design Education, 25, 1, pp. 429-451, (2015) | |
dc.relation.references | Rokach L., Maimon O., Top-down induction of decision trees—A survey. IEEE Transactions on Systems, Man, and Cybernetics: PART C, 35, 4, pp. 476-487, (2005) | |
dc.relation.references | Salamonson Y., Ramjan L.M., van den Nieuwenhuizen S., Metcalfe L., Chang S., Everett B., Sense of coherence, self-regulated learning and academic performance in first year nursing students: A cluster analysis approach, Nurse Education in Practice, 17, pp. 208-213, (2016) | |
dc.relation.references | Swan K., Learning analytics and the shape of things to come, The Quarterly Review of Distance Education, 17, 3, pp. 5-12, (2016) | |
dc.relation.references | Therneau T., Atkinson B., Ripley B., Package “rpart, (2018) | |
dc.relation.references | Timaran Pereira R., Caicedo Zambrano J., Application of decision trees for detection of student dropout profiles, 16Th IEEE International Conference on Machine Learning and Applications, ICMLA, 2017, pp. 528-531, (2017) | |
dc.relation.references | van Westhuizen S.D., de Beer M., Bekwa N., Psychological strengths as predictors of postgraduate students’ academic achievement, Journal of Psychology in Africa, 21, 3, pp. 473-478, (2011) | |
dc.relation.references | Viloria A., Pineda Lezama O.B., Mixture structural equation models for classifying university student dropout in Latin America, Procedia Computer Science, 160, pp. 629-634, (2019) | |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
dc.subject.keywords | Computer science education | |
dc.subject.keywords | Decision trees | |
dc.subject.keywords | Digital transformation | |
dc.subject.keywords | Machine learning | |
dc.subject.keywords | Predictive models | |
dc.type.driver | info:eu-repo/semantics/bookPart | |
dc.type.hasversion | info:eu-repo/semantics/acceptedVersion | |
dc.type.redcol | http://purl.org/redcol/resource_type/CAP_LIB | |
dc.type.spa | Libro |
Ficheros en el ítem
Ficheros | Tamaño | Formato | Ver |
---|---|---|---|
No hay ficheros asociados a este ítem. |
Este ítem aparece en la(s) siguiente(s) colección(ones)
-
Artículos Scopus [165]