Evolución y cartografía temática del aprendizaje significativo: un análisis estructural
Barra lateral del artículo
Contenido principal del artículo
Resumen
La teoría del aprendizaje significativo gestada hace 65 años por D. Ausubel es una influyente corriente de ideas que mantienen relevancia en el mundo pedagógico actual. El presente estudio realizó una exploración cienciométrica de este campo a partir de un estudio bibliométrico basado en la exploración de (n=16439) documentos. A nivel metodológico se establecen redes conceptuales derivadas del campo y la evolución temática del aprendizaje significativo a partir de la aplicación de técnicas como agrupamiento por acoplamiento de documentos, mapa temático, grafos de los clústeres abordados, evolución temática a lo largo del tiempo, red conceptual de las temáticas centrales y sus indicadores algorítmicos y evolución de los temas tendencia del aprendizaje significativo. Los resultados presentan una serie de contenidos determinantes y actuales en el desarrollo de esta teoría y forma de aprendizaje, la importancia de la resolución de problemas de manera cooperativa o conjunta, el fomento del pensamiento crítico, el uso de tecnologías digitales disponibles y la aplicación de las innovaciones pedagógicas adecuadas a diversos contextos educativos. Se concluyó con la importancia de evaluar los logros y dificultades del aprendizaje significativo en tanto proceso y forma de aprendizaje.
Detalles del artículo
Sección
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Cómo citar
Referencias
Andrews, D., van Lieshout, E., & Kaudal, B. B. (2023). How, where, and when do students experience meaningful learning?. International Journal of Innovation in Science and Mathematics Education, 31(3). https://doi.org/10.30722/IJISME.31.03.003
Angelelli, M., Ciavolino, E., Ringle, C. M., Sarstedt, M., & Aria, M. (2025). Conceptual structure and thematic evolution in partial least squares structural equation modeling research. Quality & Quantity, 59(3), 2753–2798. https://doi.org/10.1007/s11135-025-02071-4
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
Aria, M., Misuraca, M., & Spano, M. (2020). Mapping the evolution of social research and data science on 30 years of social indicators research. Social Indicators Research, 149, 803–831. https://doi.org/10.1007/s11205-020-02281-3
Aria, M., Le, T., Cuccurullo, C., Belfiore, A., & Choe, J. (2024). openalexR: An R-tool for collecting bibliometric data from OpenAlex. The R Journal, 15(4), 167–180. https://doi.org/10.32614/RJ-2023-089
Ausubel, D. P. (1960). The use of advance organizers in the learning and retention of meaningful verbal material. Journal of Educational Psychology, 51(5), 267–272. https://doi.org/10.1037/h0046669
Ausubel, D. P. (1962). A subsumption theory of meaningful verbal learning and retention. The Journal of General Psychology, 66, 213–224. https://doi.org/10.1080/00221309.1962.9711837
Ausubel, D. P. (1963). The psychology of meaningful verbal learning. Grune Stratton.
Ausubel, D. P. (1968). Educational psychology: A cognitive view. Holt, Rinehart and Winston.
Ausubel, D. P. (2000). The acquisition and retention of knowledge: A cognitive view. Kluwer Academic Publishers.
Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. Proceedings of the International AAAI Conference on Web and Social Media, 3(1), 361–362. https://doi.org/10.1609/icwsm.v3i1.13937
Boehmke, B., & Greenwell, B. (2019). Hands-On Machine Learning with R and Python: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd ed.). CRC Press.
Bringle, R. G., Reeb, R. N., Naudé, L., Ruiz, A. I., & Ong, F. (2023). Service learning. In J. Zumbach, D. A. Bernstein, S. Narciss, & G. Marsico (Eds.), International handbook of psychology learning and teaching (pp. 1305–1330). Springer. https://doi.org/10.1007/978-3-030-28745-0_61
Bryce, T. G. K., & Blown, E. J. (2024). Ausubel’s meaningful learning re-visited. Current Psychology, 43(5), 4579–4598. https://doi.org/10.1007/s12144-023-04440-4
Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics, 22, 155–205. https://doi.org/10.1007/BF02019280
Castañeda, K., Sánchez, O., Herrera, R. F., & Mejía, G. (2022). Highway planning trends: A bibliometric analysis. Sustainability, 14(9), 5544. https://doi.org/10.3390/su14095544
Chen, L.-F., Bin, J.-F., Zhang, Q., Li, H., Chen, W., & Ge, H. (2025). Hotspots and scientometrics in gallbladder cancer surgery research: A bibliometric and visualization analysis (2014–2024). Frontiers in Oncology, 15, 1522992. https://doi.org/10.3389/fonc.2025.1522992
Cherven, K. (2013). Network graph analysis and visualization with Gephi. Packt Publishing Ltd.
Demetriou, A., Spanoudis, G., Christou, C., Greiff, S., Makris, N., Vainikainen, M.-P., Golino, H., & Gonida, E. (2023). Cognitive and personality predictors of school performance from preschool to secondary school: An overarching model. Psychological Review, 130(2), 480–512. https://doi.org/10.1037/rev0000399
Díaz, F., & Hernández, G. (2010). Estrategias docentes para un aprendizaje significativo (3.ª ed.). McGraw-Hill.
Espino-Díaz, L., Fernandez-Caminero, G., Hernandez-Lloret, C. M., Gonzalez-Gonzalez, H., & Alvarez-Castillo, J. L. (2020). Analyzing the impact of COVID-19 on education professionals. Toward a paradigm shift: ICT and neuroeducation as a binomial of action. Sustainability, 12(14), 5646. https://doi.org/10.3390/su12145646
Fatima, U., Hina, S., & Wasif, M. (2023). A novel global clustering coefficient-dependent degree centrality (GCCDC) metric for large network analysis using real-world datasets. Journal of Computational Science, 70, 102008. https://doi.org/10.1016/j.jocs.2023.102008
Ghaemi, R., & Lam, G. (2021). Characterizing patterns in student engagement in a team-based and project-centric course. Proceedings of the Canadian Engineering Education Association (CEEA), 1-6. https://doi.org/10.24908/pceea.vi0.14921
Hanani, N. (2020). Meaningful learning reconstruction for millennial: Facing competition in the information technology era. IOP Conference Series: Earth and Environmental Science, 469(1), 012107. https://doi.org/10.1088/1755-1315/469/1/012107
Hsbollah, H. M., & Hassan, H. (2022). Creating meaningful learning experiences with active, fun, and technology elements in the problem-based learning approach and its implications. Malaysian Journal of Learning & Instruction, 19(1), 147–181. https://doi.org/10.32890/mjli2022.19.1.6
Karimi, F., Green, D., Matous, P., Varvarigos, M., & Khalilpour, K. R. (2021). Network of networks: A bibliometric analysis. Physica D: Nonlinear Phenomena, 421, 132889. https://doi.org/10.1016/j.physd.2021.132889
Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632. https://doi.org/10.1145/324133.324140
Kong, X., Shi, Y., Yu, S., Liu, J., & Xia, F. (2019). Academic social networks: Modeling, analysis, mining and applications. Journal of Network and Computer Applications, 132, 86–103. https://doi.org/10.1016/j.jnca.2019.01.029
Kostiainen, E., Nousiainen, T., & Näykki, P. (2025). From a bitter start to meaningful learning in online teacher education. Scandinavian Journal of Educational Research, 1–18. https://doi.org/10.1080/00313831.2025.2459404
Lim, W. M., Kumar, S., & Donthu, N. (2024). How to combine and clean bibliometric data and use bibliometric tools synergistically: Guidelines using metaverse research. Journal of Business Research, 182, 114760. https://doi.org/10.1016/j.jbusres.2024.114760
Loomis, A., Dreifuerst, K. T., & Bradley, C. S. (2022). Acquiring, applying and retaining knowledge through debriefing for meaningful learning. Clinical Simulation in Nursing, 68, 28-33. https://doi.org/10.1016/j.ecns.2022.04.002
López de Aguileta, G., & Soler-Gallart, M. (2021). Aprendizaje significativo de Ausubel y segregación educativa. Multidisciplinary Journal of Educational Research, 11(1), 1-19.
https://doi.org/10.17583/remie.0.7431
Luján-Villar, J. D. (2026a). Ecosistema global investigativo de la escucha y percepción musical: estructuras, redes y evolución científica. Revista Uniandes Episteme, 13(2), 265–287. https://doi.org/10.61154/rue.v13i2.4488
Luján-Villar, J. D. (2026b). Redes sociales de producción científica en la etnomusicología mundial. Redes. Revista Hispana para el análisis de Redes Sociales, 37(2), 176–194. https://doi.org/10.5565/rev/redes.1159
Mezirow, J. (2018). Transformative learning in practice: Insights from community, workplace, and higher education. John Wiley & Sons.
Mukherjee, D., Lim, W. M., Kumar, S., & Donthu, N. (2022). Guidelines for advancing theory and practice through bibliometric research. Journal of business research, 148, 101–115. https://doi.org/10.1016/j.jbusres.2022.04.042
Novak, J. D. (2010). Learning, creating, and using knowledge: Concept maps as facilitative tools in schools and corporations. Routledge.
Ormrod, J. E. (2020). Human learning (8th ed.). Pearson.
Pereira, V., Basilio, M. P., & Santos, C. H. T. (2025). PyBibX–a Python library for bibliometric and scientometric analysis powered with artificial intelligence tools. Data Technologies and Applications, 59(2), 302–337. https://doi.org/10.1108/DTA-08-2023-0461
Python Software Foundation (2024). Python (Versión 3.13.2) [Software de computación]. https://www.python.org/
R Core Team (2024). R: A language and environment for statistical computing (Version 4.4.2) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/
Reid, A. (2020). The connection between experience and learning: Student perspectives on the significance of international study (Thesis, School of Chemistry and Molecular Biosciences, The University of Queensland). UQeSpace. https://doi.org/10.14264/1988281
Roy, M., & DeRoche, M. (2025). Mapping misinformation gatekeepers in non-western contexts: A computational network analysis on X (Twitter) using Gephi. Telematics and Informatics Reports, 100214. https://doi.org/10.1016/j.teler.2025.100214
Sabon, Y. O. S., & Telussa, R. P. (2024). Ethnomathematics-based learning design of mountainous Papua to increase student engagement and create meaningful learning. Jurnal Pendidikan Matematika (JUPITEK), 7(1), 66–74. https://doi.org/10.30598/jupitekvol7iss1pp66-74
Simöes, C. C., & Voelzke, M. R. (2020). Astronomy teaching and meaningful learning: a look at integrated technical teaching. Research Society and Development, 9(3), 1–22. https://doi.org/10.33448/RSD-V9I3.2463
Vargas-Hernández, J. G., & Vargas-González, O. C. (2023). Didactic strategies for meaningful learning. En Vargas-Hernández, J. G., & Vargas-González, O. C. (Eds.), Multifaceted analysis of sustainable strategies and tactics in education (pp. 163–183). IGI Global. https://doi.org/10.4018/978-1-6684-6035-1.ch007
Ward, K. R., & Hober, C. L. (2020). Meaningful learning actualized through a perinatal continuity of care experience. Nursing Forum, 56(1), 66–73. https://doi.org/10.1111/nuf.12526
Yan, E., & Ding, Y. (2012). Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other. Journal of the American Society for Information Science and Technology, 63, 1313–1326. https://doi.org/10.1002/asi.22680
Yan, V. X., Sana, F., & Carvalho, P. F. (2024). No simple solutions to complex problems: Cognitive science principles can guide but not prescribe educational decisions. Policy Insights from the Behavioral and Brain Sciences, 11(1), 59–66. https://doi.org/10.1177/23727322231218906