Understanding College Students’ Perceptions of Frustrations in Online Collaborative Group Work: A Mixed Methods Approach Using Q Methodology

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Statement of Purpose

​             Under the current constructivist movement in education and instructional design, collaborative learning is regarded as “the golden key to the future” (Kirschner, 2002, p. 10). Collaborative learning was defined by Dillenbourg (1999) as “a situation in which two or more people learn or attempt to learn something together” (p. 1). Collaborative learning usually involves a group of learners working jointly to create a project, solve a problem, accomplish a task, to construct meaning towards a mutual learning objective. Successful collaborative learning has many benefits, such as foster higher-order thinking, promoting self-regulation, enhancing communicative skills, apart from gaining domain-specific knowledge (Kirschner, Sweller, Kirschner, & Zambrano, 2018; Laal & Ghodsi, 2012; Roberts, 2004). Despite the fact that benefits of collaborative learning are well documented in research, problems and challenges of collaboration are equally common, for example, the free-rider effect, the sucker effect, the status sensitivity effect, and the ganging up on the task phenomenon, as concluded by Roberts (2014). 

                 Although many solutions were suggested by researchers, in reality, many students, especially online learners, are still found to be frustrated with collaborative learning and even become resistant towards team-based learning (Smith, 2011). Yet it is not clear how students perceive different sources of frustrations in the process of collaborative group work because most studies are not concerned with students' perspectives on this topic (Chiriac, 2014). I argue that different viewpoints on these frustrations are reflections of their collaborative styles or more fundamentally their philosophy of collaborative learning. As Friend and Cook (2014) maintained, personal beliefs, values, attitudes, and feelings about collaboration are prerequisites for effective collaboration, although they focused more on the collaboration between school professionals and parents rather than among students. As every individual is unique, open discussion and clarification about these beliefs, values, attitudes, and feelings can result in a better understanding of each other among group members (Blue-Banning et al., 2004).
              The current covid-19 pandemic has disrupted normal campus-based instruction and universities worldwide have transitioned to fully distance instruction or a hybrid format of course delivery. With many uncertainties, it seems more important than ever for instructors to adapt themselves to offer quality instruction in the technology-rich environments and to design and facilitate online collaborative learning activities, even after the pandemic ends. While collaborative learning can be adapted to increase student engagement and promote deep learning, college students are facing more potential frustrations in collaborative group work than before. It is even more important to provide them with opportunities to articulate their experiences and perspectives concerning collaboration, especially those negative aspects, even before they start to work together. Q methodology is a suitable approach for this purpose because it is designed to study human subjectivity (Brown, 1993). It is recognized as a mixed methods approach by some researchers (Ramlo & Newman, 2011; Ramlo, 2016) because it draws upon the objective statistical method of factor analysis to examine the subjective viewpoints of participants. 
              Q methodology is interested in topics involving subjective viewpoints or values. Researchers need to establish a series of statements representing both the breadth and depth of the topic at issue, which is called the concourse (Brown, 1983; Watts & Stenner, 2012). Participants are asked to rate the sampled statements sampled from the concourse and sort them into a normally distributed grid (Watts & Stenner, 2012). Their sorts are treated as a whole and factor analysis is conducted to identify shared viewpoints among participants (Watts & Stenner, 2012). Interpretation of the results of factor analysis is focused on how shared viewpoints are different among different groups. Qualitative methods such as interviews and focus groups are usually used to assist the process of interpretation. More often than not, participants are asked to discuss their own interpretation of the Q sort results, which helps them discover their own perspectives and differing viewpoints.

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Purpose of Study

               Q methodology has long been used as a research tool, its potential to be used as an instructional tool is rarely explored (Rieber, 2016; Walker, Lin, & McCline, 2018). The purpose of the study is twofold. First of all, this study attempts to utilize the framework of Q methodology to create a structured class activity, as a special instructional tool, to help students understand their own perspective on frustrations in online collaboration that is also shared by others and in the meantime to become aware of other divergent perspectives as represented by different groups of students. Secondly, Q methodology is also a research tool for me as an instructor and researcher to understand my students better in terms of their collaboration dispositions. My research questions are: 

1. What are students’ major sources of frustrations in online collaboration, from their own perspective? How do students perceive these frustrations?

2. How can the Q sort activity help students better understand the shared viewpoints of their group? And other groups (of different perspectives)? 

3. How can Q sort results help instructors better understand their students’ online collaborative traits?

4. What are the implications of the Q sort results for the design and implementation of online collaborative group work? 

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Significance of Study

             On the methodological level, this study is a response to the call of introducing Q to the broader research community of mixed methods (Ramlo & Newman, 2011; Ramlo, 2016). On the practical level, this study fills in the gap of using Q methodology as an instruction tool as suggested by Rieber (2016). More importantly, this research will contribute to the literature of collaborative learning by providing alternative means to deepen the understanding of the phenomenon of frustrations in online collaboration. 

 

Future research plan:

           I have developed a concourse of frustrations in collaboration (see Appendix) and have used it to collect three rounds of data. I have found some similar results and are intrigued to develop a generalizable Q sort that can be used in broader contexts. Ramlo and Newman (2011) gave examples of replicable Q sort studies, therefore, there is a potential for the replicability and generalizability of my Q study, although not totally in the quantitative sense of generalizability.

 

References

• Blue–Banning, M., Summers, J.A., Frankland, C., Lord Nelson, J., & Beegle, G. (2004). Dimensions of family and professional partnerships: Constructive guidelines for collaboration. Exceptional Children, 70(2), 167–184 (ERIC Document reproduction Service No. EJ695925).

• Dillenbourg P. (1999). What do you mean by collaborative learning?. In P. Dillenbourg (Ed.) Collaborative-learning: Cognitive and computational approaches. (pp.1-19). Oxford: Elsevier. 

• Friend, M., & Cook, L. (2014). Interactions: Collaboration skills for school professionals (7th ed.). Harlow: Pearson Education Limited.

• Kirschner, P. A., Sweller, J., Kirschner, F., & Zambrano, R. J. (2018). From Cognitive Load Theory to Collaborative Cognitive Load Theory. International Journal of Computer-Supported Collaborative Learning. doi:10.1007/s11412-018-9277-y

• Kirschner, P. A. (2002). Can we support CSCL? Educational, social and technological affordances for learning. In.

• Laal, M. & Ghodsi, S. M. (2012). Benefits of collaborative learning. Procedia - Social and Behavioral Sciences, 31, pp. 486 – 490. https://doi:10.1016/j.sbspro.2011.12.091

• Ramlo, S. (2016). Mixed method lessons learned from 80 years of Q methodology. Journal of Mixed Methods Research, 10(1), 28-45. doi:10.1177/1558689815610998

• Ramlo, S. E., & Newman, I. (2011). Q methodology and its position in the mixed methods continuum. Operant Subjectivity, 34(3), 172-191.

• Rieber, L. (2016). Adapting the Q Sort Research Methodology for Instructional Purposes. In Proceedings of E-Learn: World Conference on E-Learning (pp. 222-227). Washington, DC, United States: Association for the Advancement of Computing in Education (AACE). Retrieved April 10, 2019 from https://www.learntechlib.org/primary/p/173944/.

• Robers, T. S. (2004). Online collaborative learning: Theory and practice. Hershey, London, Melbourne, Singapore: Information Science Publishing.

• Smith, G. G., et al. (2011). Overcoming student resistance to group work: Online versus face-to-face. Internet and Higher Education, 14, pp. 121–128.

• Walker, B. B., Lin, Y., & McCline, R. M. (2018). Q Methodology and Q-Perspectives[R] Online: Innovative Research Methodology and Instructional Technology. TechTrends: For Leaders in Education & Training, (5), 450. https://doi.org/10.1007/s11528-018-0314-5

• Watts, S. & Stenner, P. (2012). Doing Q methodological research: Theory, method and interpretation. Los Angeles, London, New Delhi, Singapore, Washington DC: SAGE Publications, Ltd. 

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