Factores clave para el éxito del aprendizaje colaborativo en línea en la educación superiorpercepciones del alumnado

  1. Pablo-César Muñoz-Carril 1
  2. Nuria Hernández-Sellés 2
  3. Mercedes González-Sanmamed 3
  1. 1 Universidad de Santiago de Compostela, USC(España)
  2. 2 Centro Superior de Estudios Universitarios La Salle (España)
  3. 3 Universidad de A Coruña, UDC(España)
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Revista:
RIED: revista iberoamericana de educación a distancia

ISSN: 1138-2783

Ano de publicación: 2024

Volume: 27

Número: 2

Tipo: Artigo

DOI: 10.5944/RIED.27.2.39093 DIALNET GOOGLE SCHOLAR lock_openAcceso aberto editor

Outras publicacións en: RIED: revista iberoamericana de educación a distancia

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Xornadas sobre Formación e Innovación Educativa Forinnova Lite - AXUDAS CONSELLO SOCIAL 2023

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Resumo

Online collaborative learning (CSCL) has expanded considerably following the restrictions imposed during the pandemic, leading to a need to analyse its foundations and the conditions that affect how well it is delivered. The aim of this study was to develop a model in order to analyse the key factors affecting purposeful online collaborative learning. The participants in the study were 799 students in higher education who had experienced this type of methodology. A questionnaire was created, organized into 7 constructs. This was used to produce a research model with reflective variables using the Partial Least Squares (PLS) technique, which demonstrated good predictive ability (R2=0.712). The 10 hypotheses underpinning the model were confirmed. The results indicate that variables such as satisfaction, perceptions of use and enjoyment, and group dynamics had a significant, positive influence on students’ perceptions of online collaborative learning. Mediating variables of interest were also identified, such asintra-group emotional support (R2=0.595)—with its link to perceived enjoyment—and the importance of online tools and group dynamics as fundamental elements for developing proper emotional support within the framework of CSCL processes. Finally, the results are discussed, along with their impact on improving teaching in higher education when implementing CSCL

Referencias bibliográficas

  • 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.
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