Factores clave para el éxito del aprendizaje colaborativo en línea en la educación superior: percepciones del alumnado
DOI:
https://doi.org/10.5944/ried.27.2.39093Palavras-chave:
aprendizaje colaborativo, educación superior, estudiantes universitarios, educación a distancia, mínimos cuadrados parciales, dinámicas de grupoResumo
A aprendizagem colaborativa online (CSCL) teve um impulso considerável após as restrições sofridas durante a pandemia e, portanto, é necessário analisar a sua fundação e as condições que afetam o seu desenvolvimento ideal. O objetivo deste estudo foi desenvolver um modelo através do qual sejam analisados os principais fatores que afetam o desenvolvimento da aprendizagem colaborativa online. Participaram 799 estudantes do ensino superior com experiência neste tipo de metodologia. Foi utilizado um questionário, organizado em 7 construtos, a partir do qual foi gerado um modelo de pesquisa com variáveis reflexivas através da técnica de Mínimos Quadrados Parciais (PLS), obtendo alta capacidade preditiva (R2=0,712). As 10 hipóteses estabelecidas que sustentaram o modelo foram confirmadas. Verificou-se que as variáveis satisfação, percepção de uso e prazer e dinâmica de grupo tiveram influência positiva e significativa na percepção dos alunos sobre a aprendizagem colaborativa online. Foram também identificadas variáveis mediadoras de grande interesse, como o apoio emocional intragrupo (R2=0,595) e a sua ligação com a perceção de alegria e prazer, bem como a importância das ferramentas online e da dinâmica de grupo como elementos fundamentais a desenvolver, nas equipes de trabalho, apoio emocional adequado no âmbito dos processos CSCL. Por fim, contrastam-se estes resultados e o seu impacto na melhoria do ensino no ensino superior ao implementar o CSCL.
Downloads
Referências
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.
Downloads
Publicado
Como Citar
Edição
Secção
Licença
Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição 4.0.
