Inteligencia de Negocios y Analítica de Datos
Demandas actuales de información desde las Universidades
DOI:
https://doi.org/10.69633/g2087906Palabras clave:
gestión académica, inteligencia de negocios, analítica del aprendizaje, analítica de datos, educación superior, análisis bibliométrico, inteligencia artificialResumen
En la era digital, la inteligencia de negocio (BI) y la analítica de datos se han convertido en elementos esenciales para la gestión académica en instituciones de educación superior. Este artículo presenta un análisis bibliométrico de la literatura sobre BI y analítica de datos publicada entre 2021 y 2025. Utilizando una base de datos de 556 publicaciones indexadas en Scopus y las herramientas RStudio, VOSviewer y Microsoft Excel, se investigó la evolución del tema, autores principales, revistas, afiliaciones institucionales, palabras clave y tendencias emergentes. Los resultados destacan un notable crecimiento en áreas como la analítica de aprendizaje y la minería de datos educativos, evidenciando una convergencia con la inteligencia artificial y la innovación en educación. Predominan los métodos experimentales, cuantitativos y de aprendizaje automático, lo que subraya la sinergia entre educación y tecnología. La distribución geográfica de las contribuciones revela liderazgos investigativos en Estados Unidos, China, Reino Unido y Australia. Este estudio sugiere que futuras investigaciones deben profundizar en la aplicación de BI y analítica de datos en educación superior desde la administración.
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Adekitan, A. I., & Noma-Osaghae, E. (2019). Data mining approach to predicting the performance of first-year students. Education and Information Technologies, 24(2), 1527–1543. https://doi.org/10.1007/s10639-018-9839-7
Alachiotis, N. S., Stavropoulos, E. C., & Verykios, V. S. (2019). Analyzing learner behavior in a distance learning course. Journal of Information Science Theory and Practice, 7(3), 6–20. https://doi.org/10.1633/JISTaP.2019.7.3.1
Alasiri, M. M., & Salameh, A. A. (2020). The impact of business intelligence and decision support systems. International Journal of Management, 11(5), 1001–1016.
Allaham, M. V. (2022). Bibliometric analysis of HR analytics literature. Elektronik Sosyal Bilimler Dergisi, 21(83), 1147–1169. https://doi.org/10.17755/esosder.950426
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37(1), 179–255. https://doi.org/10.1002/aris.1440370106
Canty, A. J., Goldberg, L. R., & Sealy, A. S. (2020). Addressing student attrition in online programs. Journal of Applied Learning & Teaching, 3(S1), 140–152. https://doi.org/10.37074/jalt.2020.3.s1.3
Chen, C. J., & Teh, C. S. (2022). Exploring students’ online learning interaction behaviours. Teaching & Learning Inquiry, 10, 1–17. https://doi.org/10.20343/teachlearninqu.10.38
Cómbita Niño, H. A., Cómbita Niño, J. P., & Morales Ortega, R. (2020). Business intelligence governance framework in a university. International Journal of Information Management, 50, 405–412. https://doi.org/10.1016/j.ijinfomgt.2018.11.012
Correa-Peralta, M., Vinueza-Martínez, J., & Castillo-Heredia, L. (2024). Business intelligence and data analytics in higher education: A bibliometric analysis of evolution, trends and changes. Results in Engineering, 24, 103782. https://doi.org/10.1016/j.rineng.2024.103782
Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams. Campbell Systematic Reviews, 18(2), e1230. https://doi.org/10.1002/cl2.1230
Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68(4), 1961–1990. https://doi.org/10.1007/s11423-020-09788-z
Karlos, S., Kostopoulos, G., & Kotsiantis, S. (2020). Predicting and interpreting students’ grades in distance higher education through a semi-regression method. Applied Sciences, 10(23), 8413. https://doi.org/10.3390/app10238413
Moscoso-Zea, O., Saa, P., & Luján-Mora, S. (2019). Evaluation of algorithms to predict graduation rate in higher education institutions. Australasian Journal of Engineering Education, 24(1), 4–13. https://doi.org/10.1080/22054952.2019.1601063
Muntean, M., Bologa, A. R., Corbea, A. M. I., & Bologa, R. (2019). A framework for evaluating the business analytics maturity of university programmes. Sustainability, 11(3), 853. https://doi.org/10.3390/su11030853
Paideya, V., & Bengesai, A. V. (2021). Predicting patterns of persistence at a South African university: A decision tree approach. International Journal of Educational Management, 35(6), 1245–1262. https://doi.org/10.1108/IJEM-04-2020-0184
Pedraza-Navarro, I., & Sánchez-Serrano, S. (2022). Análisis de las publicaciones presentes en WoS y Scopus: Posibilidades de búsqueda para evitar literatura fugitiva en revisiones sistemáticas. Revista Interuniversitaria de Investigación en Tecnología Educativa, (13), 41–61. https://doi.org/10.6018/riite.548361
Rets, I., Herodotou, C., & Gillespie, A. (2023). Six practical recommendations enabling ethical use of predictive learning analytics in distance education. Journal of Learning Analytics, 10(1), 149–167. https://doi.org/10.18608/jla.2023.7743
Sadiq, M. H., & Ahmed, N. S. (2019). Classifying and predicting students’ performance using improved decision tree C4.5 in higher education institutes. Journal of Computer Science, 15(12), 1291–1306. https://doi.org/10.3844/jcssp.2019.1291.1306
Sarra, A., Fontanella, L., & Di Zio, S. (2019). Identifying students at risk of academic failure within the educational data mining framework. Social Indicators Research, 146(1–2), 41–60. https://doi.org/10.1007/s11205-018-1901-8
Seal, K. C., Leon, L. A., Przasnyski, Z. H., & Lontok, G. (2020). Delivering business analytics competencies and skills: A supply side assessment. Interfaces, 49(5), 374–385. https://doi.org/10.1287/inte.2020.1043
Sokout, H., Usagawa, T., & Mukhtar, S. (2020). Learning analytics: Analyzing various aspects of learners’ performance in blended courses: The case of Kabul Polytechnic University, Afghanistan. International Journal of Emerging Technologies in Learning, 15(12), 168–190. https://doi.org/10.3991/ijet.v15i12.13473
Tibaná-Herrera, G., Fernández-Bajón, M. T., & De Moya-Anegón, F. (2018). Categorization of e-learning as an emerging discipline: A bibliometric study in Scopus. International Journal of Educational Technology in Higher Education, 15, Artículo 13. https://doi.org/10.1186/s41239-018-0103-4
Tlili, A., Denden, M., Essalmi, F., Jemni, M., Chang, M., Kinshuk, & Chen, N. S. (2023). Automatic modeling learner’s personality using learning analytics approach in an intelligent Moodle learning platform. Interactive Learning Environments, 31(5), 2529–2543. https://doi.org/10.1080/10494820.2019.1636084
Uliyan, D., Al-Fawwaz, B., & Al-Zoubi, A. M. (2021). Deep learning model to predict student retention using BLSTM and CRF. IEEE Access, 9, 135550–135558. https://doi.org/10.1109/ACCESS.2021.3117117
Valarmathy, N., & Krishnaveni, S. (2019). Performance evaluation and comparison of clustering algorithms used in educational data mining. International Journal of Recent Technology and Engineering, 7(6), 103–112.
Wu, M., Long, R., Bai, Y., & Chen, H. (2021). Knowledge mapping analysis of environmental communication research. Journal of Environmental Management, 298, 113475. https://doi.org/10.1016/j.jenvman.2021.113475
Yang, Y., Hooshyar, D., Pedaste, M., Wang, M., Huang, Y. M., & Lim, H. (2020). Predicting course achievement based on procrastination behaviour on Moodle. Soft Computing, 24(24), 18777–18793. https://doi.org/10.1007/s00500-020-05110-4
Zhang, Y., An, R., Liu, S., Cui, J., & Shang, X. (2023). Predicting and understanding student learning performance using multi-source sparse attention convolutional neural networks. IEEE Transactions on Big Data, 9(1), 118–132. https://doi.org/10.1109/TBDATA.2021.3125204
Zuluaga-Ortiz, R., Camelo-Guarín, A., & Delahoz-Domínguez, E. (2023). Efficiency analysis trees as a tool to analyze quality. International Journal of Electrical and Computer Engineering, 13(4), 4412–4421. https://doi.org/10.11591/ijece.v13i4.pp4412-4421
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
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Derechos de autor 2026 Mirella Correa-Peralta (Autor/a)
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
