Factores clave para el éxito del aprendizaje colaborativo en línea en la educación superior: percepciones del alumnado
DOI:
https://doi.org/10.5944/ried.27.2.39093Palabras clave:
aprendizaje colaborativo, educación superior, estudiantes universitarios, educación a distancia, mínimos cuadrados parciales, dinámicas de grupoResumen
El aprendizaje colaborativo en línea (CSCL) ha experimentado un impulso considerable después de las restricciones sufridas durante la pandemia y, por ello, es necesario analizar su fundamentación y las condiciones que inciden en su óptimo desarrollo. El propósito de este estudio ha consistido en elaborar un modelo a través del que se analizan los factores clave que inciden en el desarrollo del aprendizaje colaborativo en línea. Participaron 799 estudiantes de educación superior con experiencia en este tipo de metodología. Se empleó un cuestionario, organizado en 7 constructos, a partir del que se generó un modelo de investigación con variables de tipo reflectivo a través de la técnica Partial Least Squares (PLS), obteniéndose una elevada capacidad predictiva (R2 =0.712). Se confirmaron las 10 hipótesis establecidas que sustentaban el modelo. Se constató que las variables satisfacción, percepción de uso y disfrute, y dinámicas de grupo poseían una influencia positiva y significativa respecto a las percepciones del alumnado sobre el aprendizaje colaborativo en línea. Se identificaron también variables mediadoras de gran interés como es el caso del soporte emocional intra-grupo (R2 =0.595) y su vinculación con la percepción de alegría y disfrute, así como la importancia de las herramientas en línea y de las dinámicas de grupo como elementos fundamentales para desenvolver, en el seno de los equipos de trabajo, un adecuado apoyo emocional en el marco de procesos de CSCL. Finalmente, se contrastan estos resultados y su incidencia en la mejora de la enseñanza en la educación superior al implementar el CSCL.
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Ahmed, S. (2018). La política cultural de las emociones. Universidad Nacional Autónoma de México.
Alenazy, W., Al-Rahmi, W., & Khan, M. S. (2019). Validation of TAM Model on Social Media Use for Collaborative Learning to enhance Collaborative Authoring. IEEE Access, 7, 71550-71562. https://doi.org/10.1109/ACCESS.2019.2920242
Bagozzi, P., & Yi, Y. (1989). On the Use of Structural Equation Models in Experimental Designs. Journal of Marketing Research, 26(3), 271-284. https://doi.org/10.2307/3172900
Baloche, L., & Brody, C. M. (2017). Cooperative learning: exploring challenges, crafting innovations. Journal of Education for Teaching, 43(3), 274-283. https://doi.org/10.1080/02607476.2017.1319513
Bölen, M. C. (2020). Exploring the determinants of users’ continuance intention in smartwatches. Technology in Society, 60, 1-12. https://doi.org/10.1016/j.techsoc.2019.101209
Borge, M., Ong, Y. S., & Rosé, C. P. (2018). Learning to monitor and regulate collective thinking processes. International Journal of Computer-Supported Collaborative Learning, 13(1), 61-92. https://doi.org/10.1007/s11412-018-9270-5
Cabero Almenara, J., Gutiérrez Castillo, J. J., Guillén Gámez, F. D., & Gaete Bravo, A. F. (2022). Competencias digitales de estudiantes universitarios: creación de un modelo causal desde un enfoque PLS-SEM. Campus virtuales, 11(1), 167-179. https://doi.org/10.54988/cv.2022.1.1008
Cerro Martínez, J. P, Guitert, M., & Romeu Fontanillas, T. (2020). Impact of using learning analytics in asynchronous online discussions in higher education. International Journal of Educational Technology in Higher Education, 17(39), 1-18. https://doi.org/10.1186/s41239-020-00217-y
Chahal, J., & Rani, N. (2022). Exploring the acceptance for e-learning among higher education students in India: combining technology acceptance model with external variables. Journal of Computing in Higher Education, 34, 844-867. https://doi.org/10.1007/s12528-022-09327-0
Chen, C. M., & Chang, C. C. (2014). Mining learning social networks for cooperative learning with appropriate learning partners in a problem-based learning environment. Interactive Learning Environments, 22(1), 97-124. https://doi.org/10.1080/10494820.2011.641677
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160-175. https://doi.org/10.1016/j.compedu.2012.12.003
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295-336). Psychology Press.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Lawrence Erlbaum Associates.
Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Frania, M., & Correia, F. L. d. S. (2022). Interpersonal Competences and Attitude to Online Collaborative Learning (OCL) among Future Pedagogues and Educators—A Polish and Portuguese Perspective. Education Science, 12, 23. https://doi.org/10.3390/educsci12010023
Garrison, D. R., Anderson, T., & Archer, W. (1999). Critical Inquiry in a Text-Based Environment: Computer Conferencing in Higher Education. The Internet and Higher Education, 2(2-3), 87-105. https://doi.org/10.1016/S1096-7516(00)00016-6
González-Sanmamed, M., Sangrà, A., Souto-Seijo, A., & Estévez, I. (2020). Learning ecologies in the digital era: challenges for higher education. Publicaciones, 50(1), 83-102. https://doi.org/10.30827/publicaciones.v50i1.15671
González-Sanmamed, M., Muñoz-Carril, P. C., & Sangrà, A. (2017). We can, we know how. But do we want to? Teaching attitudes toward ICT based on the level of integration of technology in the schools. Technology, Pedagogy and Education, 26(5), 633-647. https://doi.org/10.1080/1475939X.2017.1313775
Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 1-16. https://doi.org/10.1016/j.rmal.2022.100027
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3 ed.). Sage. https://doi.org/10.1007/978-3-030-80519-7
Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106-121. https://doi.org/10.1108/EBR-10-2013-0128
Hair, J., Hult, G. T., Ringle, C., Sarstedt, M., Castillo Apraiz, J., Cepeda Carrión, G. A., & Roldán, J. L. (2019). Manual de Partial Least Squares Structural Equation Modeling (PLS-SEM). OmniaScience. https://doi.org/10.3926/oss.37
Hair, J., Ringle, C., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. The Journal of Marketing Theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J., Sarstedt, M., Ringle, C., Gudergan, S. P., Castillo Apraiz, J., Cepeda Carrión, G. A., & Roldán J. L. (2021). Manual Avanzado de Partial Least Squares Structural Equation Modeling (PLS-SEM). OmniaScience. https://doi.org/10.3926/oss.407
Hamid, S., Waycott, J., Kurnia, S., & Chang, S. (2015). Understanding students' perceptions of the benefits of online social networking use for teaching and learning. Internet and Higher Education, 26, 1-9. https://doi.org/10.1016/j.iheduc.2015.02.004
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Hernández Sampieri, R., & Mendoza Torres, C. P. (2020). Metodología de la investigación: las rutas cuantitativa, cualitativa y mixta. McGraw-Hill.
Hernández-Sellés, N. (2021a). La importancia de la interacción en el aprendizaje en entornos virtuales en tiempos del COVID-19. Publicaciones, 51(3), 257-294. https://doi.org/10.30827/publicaciones.v51i3.18518
Hernández-Sellés, N. (2021b). Herramientas que facilitan el aprendizaje colaborativo en entornos virtuales: nuevas oportunidades para el desarrollo de las ecologías digitales de aprendizaje. Educatio Siglo XXI, 39(2), 81-100. https://doi.org/10.6018/educatio.465741
Hernández-Sellés, N., Galindo, J. M., Arteaga, O., & García, S. (2023). Diseño de un enfoque humanista con impacto social positivo en el ámbito tecnológico de las enseñanzas de grado: el caso del grado en diseño y gestión de proyectos transmedia. In P. C. Muñoz Carril, C. Sarceda Gorgoso, E. J. Fuentes Abeledo & E. M. Barreira Cerqueiras (Eds.), La formación y la innovación educativa: ejes para la transformación social (141-164). Dykinson S.L. https://doi.org/10.2307/jj.2010047.11
Hernández-Sellés, N., Muñoz-Carril, P. C., & González-Sanmamed, M. (2019). Computer-supported collaborative learning: An analysis of the relationship between interaction, emotional support and online collaborative tools. Computers & Education, 138, 1-12. https://doi.org/10.1016/j.compedu.2019.04.012
Hernández-Sellés, N., Muñoz-Carril, P. C., & González-Sanmamed, M. (2020). Interaction in computer supported collaborative learning: an analysis of the implementation phase. International Journal of Educational Technology in Higher Education, 17(23), 1-13. https://doi.org/10.1186/s41239-020-00202-5
Hu, L., & Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
Hung-Ming, L., Min-Hsien, L., Jyh-Chong, L., Hsin-Yi, C., Pinchi, H., & Chin-Chung, T. (2020). A review of using partial least square structural equation modeling in e-learning research. British Journal of Educational Technology, 51(4), 1354-1372. https://doi.org/10.1111/bjet.12890
Ifinedo, P. (2017). Students’ perceived impact of learning and satisfaction with blogs. The International Information and Learning Technology, 34(4), 322-337. https://doi.org/10.1108/IJILT-12-2016-0059
Ifinedo, P. (2018). Determinants of students´ continuance intention to use blogs to learn: an empirical investigation. Behaviour & Information Technology, 37(4), 381-392. https://doi.org/10.1080/0144929X.2018.1436594
Keramati, M. R., & Gillies, R. M. (2022). Advantages and Challenges of Cooperative Learning in Two Different Cultures. Education Sciences, 12(3). 1-14. https://doi.org/10.3390/educsci12010003
Ku, H-Y., Hung, W. T., & Akarasriworn, C. (2013). Collaboration factors, teamwork satisfaction, and student attitudes toward online collaborative learning. Computers in Human Behavior, 29, 922-929. https://doi.org/10.1016/j.chb.2012.12.019
Kuo, Y.-C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education course. Internet and Higher Education, 20, 35-50. https://doi.org/10.1016/j.iheduc.2013.10.001
Kwon, K., Liu, Y., & Johnson, L. (2014). Group regulation and social-emotional interactions observed in computer supported collaborative Learning: Comparison between good vs. poor collaborators. Computers & Education, 78, 185-200. https://doi.org/10.1016/j.compedu.2014.06.004
Lasfeto, D., & Ulfa, S. (2020). The relationship between self-directed learning and students’ social interaction in online learning environment. Journal of e-Learning and Knowledge Society, 16(2), 34-41. https://doi.org/10.20368/1971-8829/1135078
Lin, J.-W., & Tsai, C. W. (2016). The impact of an online project-based learning environment with group awareness support on students with different self-regulation levels: An extended-period experiment. Computers & Education, 99, 28-38. https://doi.org/10.1016/j.compedu.2016.04.005
Lin, J.-W., & Lin, H.-C. K. (2019). User acceptance in a computer-supported collaborative learning (CSCL) environment with social network awareness (SNA) support. Australasian Journal of Educational Technology, 35(1), 100-115. https://doi.org/10.14742/ajet.3395
Martin, D. P., & Rimm-Kaufman, S. E. (2015). Do student self-efficacy and teacher-student interaction quality contribute to emotional and social engagement in fifth grade math? Journal of School Psychology, 53(5), 359-373. https://doi.org/10.1016/j.jsp.2015.07.001
Molinillo, S., Aguilar-Illescas, R., Anaya-Sánchez, R., & Vallespín-Arán M. (2018). Exploring the impacts of interactions, social presence and emotional engagement on active collaborative learning in a social web-based environment. Computers & Education, 123, 41-52. https://doi.org/10.1016/j.compedu.2018.04.012
Muñoz-Carril, P. C., González-Sanmamed, M., & Fuentes-Abeledo, E. J. (2020). Use of blogs for prospective early childhood teachers. Educación XX1, 23(1), 247-273. https://doi.org/10.5944/educxx1.23768
Muñoz-Carril, P. C., Hernández-Sellés, N., Fuentes-Abeledo, E. J., & González-Sanmamed, M. (2021). Factors influencing students' perceived impact of learning and satisfaction in computer supported collaborative learning. Computers & Education, 174:104310. https://doi.org/10.1016/j.compedu.2021.104310
Näykki, P., Isohätälä, J., Järvelä, S., Pöysä-Tarhonen, J., & Häkkinen, P. (2017). Facilitating socio-cognitive and socio-emotional monitoring in collaborative learning with a regulation macro script – an exploratory study. International Journal of Computer-Supported Collaborative Learning, 12(3), 251-279.
https://doi.org/10.1007/s11412-017-9259-5
Noroozi, O., Weinberger, A., & Kirschner, P. A. (2021). Editorial to the special issue: Technological and pedagogical innovations for facilitation of students’ collaborative argumentation-based learning. Innovations in Education and Teaching International, 58(5), 499-500. https://doi.org/10.1080/14703297.2021.1978703
Panadero, E., Alonso-Tapia, J., García-Pérez, D., Fraile, J., Sánchez-Galán, J. M., & Pardo, R. (2021). Estrategias de aprendizaje profundas: Validación de un modelo situacional y su cuestionario. Revista de Psicodidáctica, 26(1), 10-19. https://doi.org/10.1016/j.psicod.2020.11.003
Park, E. (2020). User acceptance of smart wearable devices: An expectation-confirmation model approach. Telematics and Informatics, 47. https://doi.org/10.1016/j.tele.2019.101318
Puntambekar, S. (2006). Analyzing collaborative interactions: divergence, shared understanding and construction of knowledge. Computers & Education, 47(3), 332-351. https://doi.org/10.1016/j.compedu.2004.10.012
Renninger, K. A., & Hidi, S. E. (2016). The Power of Interest for Motivation and Engagement. Routledge. https://doi.org/10.4324/9781315771045
Ringle, C. M., Wende, S., & Becker, J. M. (2022). SmartPLS 4 (Nº de versión 4.0.9.6). Windows. Boenningstedt: SmartPLS GmbH. http://www.smartpls.com
Tang, K. Y., Tsai, C. C., & Lin, T. C. (2014). Contemporary intellectual structure of CSCL research (2006–2013): A co-citation network analysis with an education focus. International Journal of Computer-Supported Collaborative Learning, 9, 335-363. https://doi.org/10.1007/s11412-014-9196-5
Vuopala, E., Hyvönen, P., & Järvelä, S. (2016). Interaction forms in successful collaborative learning in virtual learning environments. Active Learning in Higher Education, 17(1), 25-38. https://doi.org/10.1177/1469787415616730
Wang, S., Sun, Z., & Chen, Y. (2023). Effects of higher education institutes’ artificial intelligence capability on students' self-efficacy, creativity and learning performance. Education and Information Technologies, 28, 4919-4939. https://doi.org/10.1007/s10639-022-11338-4
Yang, H., Cai, M., Diao, Y., Liu, R., Liu, L., & Xiang, Q. (2023). How does interactive virtual reality enhance learning outcomes via emotional experiences? A structural equation modeling approach. Frontiers in Psychology, 13:1081372. https://doi.org/10.3389/fpsyg.2022.1081372
Yilmaz, R., & Yilmaz, F. G. K. (2022). Examination of the efectiveness of the task and group awareness support system used for computer-supported collaborative learning. Educational Technology Research and Development, 68, 1355-1380. https://doi.org/10.1007/s11423-020-09741-0
Zambrano, J, Kirschner, F., Sweller, J., & Kirschner, P. A. (2023). Effect of task-based group experience on collaborative learning: Exploring the transaction activities. British Journal of Educational Technology, 93(4), 879-902. https://doi.org/10.1111/bjep.12603
Zhan, H. (2008). The effectiveness of instructional models with collaborative learning approaches in undergraduate online courses. Ph.D. thesis, Northern Arizona University. https://www.learntechlib.org/p/125783/
Zimmermann, B. J., & Schunk, D. H. (2011). Self-regulated learning and performance. In D. H. Schunk & B. J. Zimmerman (Eds.), Handbook of Self-Regulated Learning and Performance (pp. 1-12). Routledge.
Zuboff, S. (2020). La era del capitalismo de la vigilancia: la lucha por un futuro humano frente a las nuevas fronteras del poder. Paidós.
