Tendencias en la Calidad del Servicio E-learning en Educación Superior
DOI:
https://doi.org/10.69633/xw2q5423Palabras clave:
Educación superior, enseñanza a distancia, tecnologías de la información, calidad de la educación, bibliometría, satisfacción de los estudiantesResumen
Este estudio presenta un resumen de la producción científica sobre calidad del servicio e-learning en la educación superior mediante un abordaje bibliométrico exhaustivo. Con las herramientas Bibliometrix (R), Biblioshiny y VOSviewer analizó 254 publicaciones arbitradas indexadas en Scopus y Web of Science (2015–2024), examinó tendencias de publicación, productividad autoral, afiliaciones institucionales, evolución de palabras clave y patrones de colaboración internacional.Los resultados revelaron una tasa de crecimiento anual del 10,3%, con un pico significativo en 2021 atribuible a la pandemia COVID-19. El análisis de productividad confirmó la distribución de Lotka: el 93,6% de los investigadores publicó un único artículo. La producción científica se concentra en Asia, con el 67%; China registra 60 publicaciones; India, 40; y Malasia, 37, mientras que América Latina y África permanecen críticamente subrepresentadas. El impacto promedio por artículo demuestra la distribución inversa: Reino Unido (64,7 citas/art.) y Estados Unidos (39,2 citas/art.) que superan significativamente a China (12,5 citas/art.).
Los modelos conceptuales más utilizados —SERVQUAL, TAM y DeLone & McLean— estructuran el 78% de las investigaciones. Las redes de colaboración internacional exhiben fragmentación crítica: el 71% de las coautorías ocurren dentro de los límites nacionales. La evolución temática evidenció tres fases: percepción del usuario (2015–2018), aprendizaje remoto por COVID-19 (2019–2022) y uso de la inteligencia artificial y de modelos híbridos (2023–2024).
Este trabajo resulta pionero en análisis bibliométrico que integra el protocolo PRISMA 2020, doble indexación y análisis temporal tripartito centrado en calidad del servicio e-learning en la educación superior; evidenciando brechas sistémicas, tendencias emergentes y oportunidades estratégicas para agendas investigativas y políticas institucionales
Descargas
Referencias
Abd El Halim, H., & Elbadrawy, R. (2022). Examining drivers affecting E-learning system quality during COVID-19. Total Quality Management and Business Excellence, 33(11–12), 1422–1437. https://doi.org/10.1080/14783363.2021.1962704
Ali, J., Jusoh, A., Idris, N., Abbas, A. F., & Alsharif, A. H. (2021). Everything is going electronic, so do services and service quality: Bibliometric analysis of e-services and e-service quality. International Journal of Interactive Mobile Technologies, 15(18), 148–167. https://doi.org/10.3991/ijim.v15i18.24519
Al-Mushasha, N. F., & Nassuora, A. B. (2012). Factors determining e-learning service quality in Jordanian higher education environment. Journal of Applied Sciences, 12(14), 1474–1480. https://doi.org/10.3923/jas.2012.1474.1480
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Blut, M., Chowdhry, N., Mittal, V., & Brock, C. (2015). E-service quality: A meta-analytic review. Journal of Retailing, 91(4), 679–700. https://doi.org/10.1016/j.jretai.2015.05.004
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
Bozkurt, A., Jung, I., Xiao, J., Vladimirschi, V., Schuwer, R., Egorov, G., Lambert, S. R., Al-Freih, M., Pete, J., Olcott, Jr. D., Rodes, V., Aranciaga, I., Bali, M., Alvarez, A. V., Roberts, J., Pazurek, A., Raffaghelli, J. E., Panagiotou, N., Coëtlogon, P. de, … Paskevicius, M. (2020). A global outlook to the interruption of education due to COVID-19 pandemic: Navigating in a time of uncertainty and crisis. Asian Journal of Distance Education, 15(1), 1–126. https://doi.org/10.5281/zenodo.3878572
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146–166. https://doi.org/10.1016/j.joi.2010.10.002
Correa-Peralta, M., Vinueza-Martínez, J., & Castillo-Heredia, L. (2025). Evolution, topics and relevant research methodologies in business intelligence and data analysis in the academic management of higher education institutions. A literature review. Results in Engineering, 25, 103782. https://doi.org/10.1016/j.rineng.2024.103782
Dangaiso, P., Makudza, F., & Hogo, H. (2022). Modelling perceived e-learning service quality, student satisfaction and loyalty. A higher education perspective. Cogent Education, 9(1). https://doi.org/10.1080/2331186X.2022.2145805
Dhawan, S. (2020). Online Learning: A Panacea in the Time of COVID-19 Crisis. Journal of Educational Technology Systems, 49(1), 5–22. https://doi.org/10.1177/0047239520934018
Gutiérrez-Salcedo, M., Martínez, M. Á., Moral-Munoz, J. A., Herrera-Viedma, E., & Cobo, M. J. (2017). Some bibliometric procedures for analyzing and evaluating research fields. Applied Intelligence, 48(5), 1275–1287. https://doi.org/10.1007/s10489-017-1105-y
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, with interactivity for optimised digital transparency and Open Synthesis. Campbell Systematic Reviews, 18(2), e1230. https://doi.org/10.1002/cl2.1230
Jiang, Y., Zhou, J., Li, Z., & Zhu, Y. (2024). Total Quality Management & Business Excellence: A 33-year overview using bibliometric and content analysis. Total Quality Management and Business Excellence, 35(5–6), 631–669. https://doi.org/10.1080/14783363.2024.2328252
Lancichinetti, A., & Fortunato, S. (2009). Community detection algorithms: A comparative analysis. Physical Review E, 80(5), 056117. https://doi.org/10.1103/PhysRevE.80.056117
Limbu, Y. B., & Pham, L. (2023). Impact of e-learning service quality on student satisfaction during the Covid-19 pandemic: A systematic review. Knowledge Management and E-Learning, 15(4), 523–538. https://doi.org/10.34105/j.kmel.2023.15.030
Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–323.
Malkawi, N. A. M., Rababah, L. M., Erkir, S., Bani-Khair, B., Al-Hawamdeh, B. M., Al-Omari, S. K., & Rababah, M. A. (2024). Effectiveness of English E-Learning Classes: University Students' Perspectives. Journal of Language Teaching and Research, 15(6), 1978–1987. https://doi.org/10.17507/jltr.1506.23
Martín-Martín, A., Thelwall, M., Orduna-Malea, E., & Delgado López-Cózar, E. (2021). Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations' COCI: A multidisciplinary comparison of coverage via citations. Scientometrics, 126(1), 871–906. https://doi.org/10.1007/s11192-020-03690-4
Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228. https://doi.org/10.1007/s11192-015-1765-5
Mutua, M. M., & Nyoni, C. N. (2023). Undergraduate e-learning programmes in health professions: An integrative review of evaluation standards in low- and middle-income countries. PLoS ONE, 18(2), 1–17. https://doi.org/10.1371/journal.pone.0281586
Naveed, Q. N., Alam, M. M., Qahmash, A. I., & Quadri, K. M. (2021). Exploring the determinants of service quality of cloud elearning system for active system usage. Applied Sciences, 11(9), 4176. https://doi.org/10.3390/app11094176
Safarifard, R., Lavasani, M. G., Hejazi, E., & Thani, F. N. (2024). Pedagogical aspect of e-learning in higher education: A systematic literature review. Knowledge Management and E-Learning, 16(3), 521–546. https://doi.org/10.34105/j.kmel.2024.16.024
Secinaro, S., Brescia, V., Calandra, D., & Biancone, P. (2020). Employing bibliometric analysis to identify suitable business models for electric cars. Journal of Cleaner Production, 264, 121503. https://doi.org/10.1016/j.jclepro.2020.121503
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: A structured literature review. BMC Medical Informatics and Decision Making, 21(1), 125. https://doi.org/10.1186/s12911-021-01488-9
Seitova, M., Temirbekova, Z., Kazykhankyzy, L., Khalmatova, Z., & Çelik, H. E. (2024). Perceived service quality and student satisfaction: A case study at Khoja Akhmet Yassawi University, Kazakhstan. Frontiers in Education, 9, 1492432. https://doi.org/10.3389/feduc.2024.1492432
Shahzad, A., Hassan, R., Aremu, A. Y., Hussain, A., & Lodhi, R. N. (2021). Effects of COVID-19 in E-learning on higher education institution students: The group comparison between male and female. Quality and Quantity, 55(3), 805–826. https://doi.org/10.1007/s11135-020-01028-z
Silva, D. S., de Moraes, G. H. S. M., Makiya, I. K., & Cesar, F. I. G. (2017). Measurement of perceived service quality in higher education institutions: A review of HEdPERF scale use. Quality Assurance in Education, 25(4), 415–439. https://doi.org/10.1108/QAE-10-2016-0058
Uppal, M. A., Ali, S., & Gulliver, S. R. (2018). Factors determining e-learning service quality. British Journal of Educational Technology, 49(3), 412–426. https://doi.org/10.1111/bjet.12552
Vinueza-Martinez, J. (2025). Quality of E-Learning Service in Higher Education Institutions. A Bibliometric Analysis of Global Trends and Future Directions. (Versión 1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.15003667
Vinueza-Martinez, J., Correa-Peralta, M., Ramírez-Anormaliza, R., Franco Arias, O., & Vera Paredes, D. (2024). Geographic Information Systems (GISs) Based on WebGIS Architecture: Bibliometric Analysis of the Current Status and Research Trends. Sustainability, 16(15), 6439. https://doi.org/10.3390/su16156439
Waltman, L., & Van Eck, N. J. (2013). A smart local moving algorithm for large-scale modularity-based community detection. The European Physical Journal B, 86(11), 471. https://doi.org/10.1140/epjb/e2013-40829-0
Zupic, I., & Čater, T. (2015). Bibliometric Methods in Management and Organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2026 Jorge Vinueza-Martínez (Autor/a)
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
