Abstract
Collaborative problem-solving (CPS) involves the interaction and interdependence of students’ social and cognitive skills, making it a complex learning process. To delve into the complex dynamics of CPS, previous research has categorized socio-cognitive roles, providing insights into social-cognitive frameworks. However, despite the specific cognitive and social interaction structures employed by roles to engage in CPS interactions, most existing research primarily focuses on individual roles, neglecting inter-role interactions. To fill this gap, twelve triad groups were formed by engaging 36 undergraduate students in online CPS activities to examine differences in social and cognitive interaction structures across different roles and group compositions. Additionally, analyze the differences in CPS processes among various group compositions. The analyses identified five roles (Lurkers, Followers, Drivers, Influential Actors, and Innovators) and three group compositions (Balanced groups, Decentralized groups, and Power Struggle groups). The socio-cognitive structure of Balanced groups, along with other evidence, indicates effective information sharing and negotiation interactions. In contrast, Decentralized and Power Struggle groups exhibited various deficiencies in their socio-cognitive structures, negatively impacting group collaboration processes. These insights provide educators with a comprehensive guide to fostering effective group compositions and role dynamics in online CPS settings, thereby enhancing the overall success of CPS. Additionally, possible activity design considerations and scaffolding strategies are also discussed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Andrews-Todd, J., & Forsyth, C. M. (2020). Exploring social and cognitive dimensions of collaborative problem solving in an open online simulation-based task. Computers in Human Behavior, 104, 105759. https://doi.org/10.1016/j.chb.2018.10.025
Bause, I. M., Brich, I. R., Wesslein, A. K., & Hesse, F. W. (2018). Using technological functions on a multitouch table and their affordances to counteract biases and foster collaborative problem solving. International Journal of Computer-Supported Collaborative Learning, 13(1), 7–33. https://doi.org/10.1007/s11412-018-9271-4
Benne, K. D., & Sheats, P. (1948). Functional roles of group members. Journal of Social Issues, 4, 41–49.
Bogarín, A., Cerezo, R., & Romero, C. (2018). A survey on educational process mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1), e1230. https://doi.org/10.1002/widm.1230
Chen, B., Hwang, G. H., & Lin, T. S. (2020a). Impacts of a dynamic grouping strategy on students’ learning effectiveness and experience value in an item bank-based collaborative practice system. British Journal of Educational Technology, 51(1), 36–52. https://doi.org/10.1111/bjet.12794
Chen, L., Inoue, K., Goda, Y., Okubo, F., Taniguchi, Y., et al. (2020b). Exploring factors that influence collaborative problem solving awareness in science education. Technology Knowledge and Learning, 25, 337–366. https://doi.org/10.1007/s10758-020-09436-8
De Wever, B., & Strijbos, J. W. (2021). Roles for structuring groups for collaboration. In International Handbook of Computer-Supported Collaborative Learning (pp. 315–331). Springer.
Dowell, N. M., Lin, Y., Godfrey, A., & Brooks, C. (2020). Exploring the relationship between emergent sociocognitive roles, collaborative problem-solving skills, and outcomes: A group communication analysis. Journal of Learning Analytics, 7(1), 38–57. https://doi.org/10.18608/jla.2020.71.4
Dowell, N. M. M., Nixon, T. M., & Graesser, A. C. (2019). Group communication analysis: A computational linguistics approach for detecting sociocognitive roles in multiparty interactions. Behavior Research Methods, 51(3), 1007–1041. https://doi.org/10.3758/s13428-018-1102-z
Du, X., Zhang, L., Hung, J. L., Li, H., Tang, H., & Xie, Y. (2022). Understand group interaction and cognitive state in online collaborative problem solving: Leveraging brain-to-brain synchrony data. International Journal of Educational Technology in Higher Education, 19(1), 1–21. https://doi.org/10.1186/s41239-022-00356-4
Escalante, J., Pack, A., & Barrett, A. (2023). AI-generated feedback on writing: Insights into efficacy and ENL student preference. International Journal of Educational Technology in Higher Education, 20(1), 57. https://doi.org/10.1186/s41239-023-00425-2
Fleiss, J. L. (1981). Statistical methods for rates and proportions (2nd ed.). John Wiley.
Gao, Q., Zhang, S., Cai, Z., Liu, K., Hui, N., & Tong, M. (2022). Understanding student teachers’ collaborative problem solving competency: Insights from process data and multidimensional item response theory. Thinking Skills and Creativity, 45, 101097. https://doi.org/10.1016/j.tsc.2022.101097
Gervits, F., Eberhard, K., & Scheutz, M. (2016). Team communication as a collaborative process. Frontiers in Robotics and AI, 3, 62. https://doi.org/10.3389/frobt.2016.00062
Griffin, P., & Care, E. (2015). The ATC21S method. In P. Griffin & E. Care (Eds.), Assessment and Teaching of 21st Century skills (pp. 3–33). Springer.
Gu, X., Shao, Y., Guo, X., & Lim, C. P. (2015). Designing a role structure to engage students in computer-supported collaborative learning. The Internet and Higher Education, 24, 13–20. https://doi.org/10.1016/j.iheduc.2014.09.002
Hao, J., Liu, L., von Davier, A. A., & Kyllonen, P. C. (2017). Initial steps towards a standardized assessment for collaborative problem solving (CPS): Practical challenges and strategies. Innovative Assessment of Collaboration, 135–156. https://doi.org/10.1007/978-3-319-33261-1_9
Heinimäki, O. P., Volet, S., Jones, C., Laakkonen, E., & Vauras, M. (2021). Student participatory group composition in collaborative science learning: Relation of within-group configurations of group composition and achievement. Learning Culture and Social Interaction, 30, 100539. https://doi.org/10.1016/j.lcsi.2021.100539
Heinimäki, O. P., Volet, S., & Vauras, M. (2020). Core and activity-specific functional participatory roles in Collaborative Science Learning. Frontline Learning Research, 8(2), 65–89. https://doi.org/10.14786/flr.v8i2.469
Hu, L. (2021). Conceptualization and operationalization of group thinking sustainability in dialogic collaborative problem solving. Thinking Skills and Creativity, 42, 100964. https://doi.org/10.1016/j.tsc.2021.100964
Hu, L., & Chen, G. (2021). Trajectories of idea emergence in dialogic collaborative problem solving: Toward a complex dynamic systems perspective. Frontiers in Psychology, 12, 735534. https://doi.org/10.3389/fpsyg.2021.735534
Kang, S. M. (2022). Internal fights over resources: The effect of power struggles on team innovation. Frontiers in Psychology, 13, 7106. https://doi.org/10.3389/fpsyg.2022.996737
Li, S., Pöysä-Tarhonen, J., & Häkkinen, P. (2022). Patterns of action transitions in online collaborative problem solving: A network analysis approach. International Journal of Computer-Supported Collaborative Learning, 17(2), 191–223. https://doi.org/10.1007/s11412-022-09369-7
Li, X., Hu, W., Li, Y., & Zheng, Y. (2023). Individuals in a group: Exploring engagement patterns via within-group configurations of role profiles and their impact on performance in collaborative problem solving. Interactive Learning Environments, 1–16, 1. https://doi.org/10.1080/10494820.2023.2239295
Liu, L., Hao, J., von Davier, A. A., Kyllonen, P., & Zapata-Rivera, J. D. (2016). A tough nut to crack: Measuring collaborative problem solving. In Handbook of Research on Technology Tools for Real-world Skill Development (pp. 344–359). IGI Global. https://doi.org/10.4018/978-1-4666-9441-5.ch013
Lu, Y., Li, K. R., Sun, Z., Ma, N., & Sun, Y. F. (2023). Exploring the effects of role scripts and goal-orientation scripts in collaborative problem-solving learning. Education and Information Technologies, 1–23. https://doi.org/10.1007/s10639-023-11674-z
Marchand, G. C., & Hilpert, J. C. (2020). Complex systems approaches to educational research: Introduction to the special issue. Journal of Experimental Education, 88(3), 351–357. https://doi.org/10.1080/00220973.2020.1746625
Marcos-García, J. A., Martínez-Monés, A., & Dimitriadis, Y. (2015). DESPRO: A method based on roles to provide collaboration analysis support adapted to the participants in CSCL situations. Computers & Education, 82, 335–353. https://doi.org/10.1016/j.compedu.2014.10.027
Mathieu, J. E., Tannenbaum, S. I., Kukenberger, M. R., Donsbach, J. S., & Alliger, G. M. (2015). Team role experience and orientation: A measure and tests of construct validity. Group & Organization Management, 40(1), 6–34. https://doi.org/10.1177/1059601114562000
OECD. (2013). PISA 2015 draft collaborative problem solving framework. OECD.
OECD. (2017). PISA 2015 results (volume V): Collaborative problem solving. OECD.
O’Neil, H. F., Chuang, S., & Chung, G. K. W. K. (2004). Issues in the computer-based assessment of collaborative problem solving. Assessment in Education: Principles Policy & Practice, 10, 361–374. https://doi.org/10.1080/0969594032000148190
Ouyang, F., Ling, T., & Jiao, P. (2022). Development of group cognition in online collaborative problem-solving processes. Journal of Educational Computing Research, 60(3), 599–630. https://doi.org/10.1177/07356331211047784
Ouyang, F., Xu, W., & Cukurova, M. (2023). An artificial intelligence-driven learning analytics method to examine the collaborative problem-solving process from the complex adaptive systems perspective. International Journal of Computer-Supported Collaborative Learning, 1–28. https://doi.org/10.1007/s11412-023-09387-z
Premchaiswadi, W., Porouhan, P., & Premchaiswadi, N. (2018). Process modeling, behavior analytics and group performance assessment of e-learning logs via fuzzy miner algorithm. In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) (Vol. 2, pp. 304–309). IEEE. https://doi.org/10.1109/COMPSAC.2018.10247
Putra, A. A. I. A., Aminah, N. S., Marjuki, A., & Pamungkas, Z. S. (2020). The profile of student’s problem solving skill using analytical problem solving test (apst) on the topic of thermodynamic. In Journal of Physics: Conference Series (Vol. 1567, No. 3, p. 032082). IOP Publishing. https://doi.org/10.1088/1742-6596/1567/3/032082
Roschelle, J., & Teasley, S. D. (1995). The construction of shared knowledge in collaborative problem solving. In Computer supported collaborative learning (pp. 69–97). Springer. https://doi.org/10.1007/978-3-642-85098-1_5
Rosen, Y., Wolf, I., & Stoeffler, K. (2020). Fostering collaborative problem solving skills in science: The Animalia project. Computers in Human Behavior, 104, 105922. https://doi.org/10.1016/j.chb.2019.02.018
Saqr, M., & López-Pernas, S. (2022). How CSCL roles emerge, persist, transition, and evolve over time: A four-year longitudinal study. Computers & Education, 189, 104581. https://doi.org/10.1016/j.compedu.2022.104581
Shaffer, D. W., Collier, W., & Ruis, A. R. (2016). A tutorial on epistemic network analysis: Analyzing the structure of connections in cognitive, social, and interaction data. Journal of Learning Analytics, 3(3), 9–45. https://doi.org/10.18608/jla.2016.33.3
Strijbos, J. W., & De Laat, M. F. (2010). Developing the role concept for computer-supported collaborative learning: An explorative synthesis. Computers in Human Behavior, 26(4), 495–505. https://doi.org/10.1016/j.chb.2009.08.014
Sun, C., Shute, V. J., Stewart, A. E., Beck-White, Q., Reinhardt, C. R., Zhou, G., & D’Mello, S. K. (2022). The relationship between collaborative problem solving behaviors and solution outcomes in a game-based learning environment. Computers in Human Behavior, 128, 107120. https://doi.org/10.1016/j.chb.2021.107120
Sun, C., Shute, V. J., Stewart, A., Yonehiro, J., Duran, N., & D’Mello, S. (2020). Towards a Peripheralized competency model of collaborative problem solving. Computers & Education, 143, 103672. https://doi.org/10.1016/j.compedu.2019.103672
Swiecki, Z., Ruis, A. R., Farrell, C., & Shaffer, D. W. (2020). Assessing individual contributions to collaborative problem solving: A network analysis approach. Computers in Human Behavior, 104, 105876. https://doi.org/10.1016/j.chb.2019.01.009
Tang, H., Dai, M., Du, X., Hung, J. L., & Li, H. (2023). Understanding college students’ cognitive engagement in online collaborative problem-solving: A multimodal data analysis. Distance Education, 44(2), 306–323. https://doi.org/10.1080/01587919.2023.2209025
Tang, H., Dai, M., Yang, S., Du, X., Hung, J. L., & Li, H. (2022). Using multimodal analytics to systemically investigate online collaborative problem-solving. Distance Education, 43(2), 290–317. https://doi.org/10.1080/01587919.2022.2064824
Zhang, S., Gao, Q., Sun, M., Cai, Z., Li, H., Tang, Y., & Liu, Q. (2022). Understanding student teachers’ collaborative problem solving: Insights from an epistemic network analysis (ENA). Computers & Education, 183, 104485. https://doi.org/10.1016/j.compedu.2022.104485
Zheng, L., Long, M., Niu, J., & Zhong, L. (2023). An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL. International Journal of Computer-Supported Collaborative Learning, 18(1), 101–133. https://doi.org/10.1007/s11412-023-09386-0
Zhong, Z., Wang, J., Deng, Y., Jin, S., Feng, S., & Li, R. (2023). Effects of external scripts incorporating capabilities, roles and tasks on IVE’s collaborative learning. Education and Information Technologies, 1–22. https://doi.org/10.1007/s10639-023-11640-9
Zhu, J., & Dawson, K. (2023). Lurkers versus posters: Perceptions of learning in informal social media-based communities. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13303
Funding
This paper was supported by National Natural Science Foundation of China (61877027, 61937001, 62177020), and CCNU Teaching Innovation Research Project (CCNUTEIII 2021-18, ZNXBJY202115).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Ethical approval
This study was conducted in accordance with the Academic Integrity Code of Central China Normal University.
Consent to participate
Informed consent was obtained from all individual participants included in this study.
Competing interests
There is no issue related to journal’s policy and no conflicts of any potential competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Tang, Y., Du, X., Hung, JL. et al. Exploring the effects of roles and group compositions on social and cognitive interaction structures in online collaborative problem-solving. Educ Inf Technol 29, 18149–18180 (2024). https://doi.org/10.1007/s10639-024-12569-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-024-12569-3