Locating and Analyzing Critical Patterns of Collective Activity in an Online Course

Author(s):

Johanna Pöysä-Tarhonen(presenting / submitting)Maarit Arvaja

Conference:

ECER 2017

Network:

16. ICT in Education and Training

Format:

Paper

Session Information

16 SES 01 B, Online Learning

Paper Session

Time:

2017-08-22

13:15-14:45

Room:

W4.21

Chair:

Philippe Gabriel

Contribution

Much of the research methods for studying technology-rich learning orderings have the emphasis on searching for commonalities, representing coherence in data (Kapur, 2011; Kapur, Voiklis, & Kinzer, 2008; Strijbos et al., 2006) and, the scope on small-scale interaction as seen from the outside. It is argued here that we need methods that can address and elucidate the complexities of situations as grounds of modern education rather than aiming at simplifications (Clarke, 2005). This paper centres on framing a method designed to locate and analyse critical patterns of collective activity during an online course as they are produced and consumed by participants. The method aims at bringing the full complexity of the collective in the situation of inquiry, combining individual, collective and nonhuman elements (e.g. technologies) present in the collective learning activity.

Method

The research participants (n=11) were students in sport and health sciences studying philosophy of science in an online course in higher education context. In this presentation, the primary focus is on three course participants. The data set includes textual materials (students’ individual writings) and observation data in the online learning environment (shared online discussions on the individuals’ writings), followed by participant interviews (n=3). The data comprise textual materials (individuals’ postings) from an online learning environment and shared discussions on these postings, combined with participant interviews. The transcribed interview data were first analysed using open coding with the assistance of Atlas.ti data analysis software (Friese, 2014) to look for emerging themes and categories that represent individual, collective and nonhuman elements in this data, be they e.g. study-, work-, and discipline-related. Second, the analysis involved extracting from the online discussion data those contents that represent similar meanings as those being found in the first phase of analysis. Next, to help to frame the key elements/perspectives that characterize the collective activity in these data, situational analysis was applied by creating social worlds (Clarke, 2015; Eastwood, Kemp, & Jaludin, 2016; Salazar Perez & Cannella, 2013). Social worlds are cartographies of the collective activity that aim at understanding its elements and their relations as the primary goal. The cartographies lay out the collective actors, arenas of commitment and discourse together with nonhuman elements and show the actions between different perspectives.

Expected Outcomes

As an outcome, the cartographies have the full potential to coherently communicate what constitutes to (successful) collective activity to flourish and, what it means to heterogeneously dwell with technologies here; contributing to sensitive reading of collective activity originating in this setting. In the presentation, the method will be presented and discussed with analysed empirical examples and is evaluated in light of its advantages and limitations.

References

Clarke, A. (2005). Situational analysis: Grounded theory after the postmodern turn. Thousand Oaks, CA: Sage. Eastwood, J., Kemp, L., & Jaludin, B. (2016). Each is in different circumstances anyway: A realistic multilevel situational analysis of maternal depression in South Western Sydney, Australia. SAGE Open 6, 4, 1-14. Friese, S. (2014). Qualitative data analysis with Atlas.ti. London: Sage. Kapur, M., Voiklis, J., &Kinzer, C.K. (2008). Sensitives to early exchange in synchronous computer-supported collaborative learning (CSCL) groups. Computers & education, 51, 54-66. Kapur, M. (2011). Temporality matters: Advancing a method for analysing problem-solving processes in a computer-supported collaborative environment. Computer-Supported Collaborative Learning, 6, 39-56. Salazar Perez, M. & Cannella, G.S. (2013). Situational analysis as an avenue for critical qualitative research: Mapping Post-Katrina New Orleans. Qualitative Inquiry 19, 7, 505-517. Strijbos, J.W., Martens, R.L., Prins, F.J. & Jochems, W.M.G. (2006). Content analysis: What are they talking about? Computers & Education, 46, 29-48.

Author Information

Johanna Pöysä-Tarhonen (presenting / submitting)

University of Jyvaskyla

Finnish Institute for Educational Research

University of Jyvaskyla

Maarit Arvaja

University of Jyvaskyla, Finland

Search the ECER Programme

Search for keywords and phrases in "Text Search"
Restrict in which part of the abstracts to search in "Where to search"
Search for authors and in the respective field.
For planning your conference attendance you may want to use the conference app, which will be issued some weeks before the conference
If you are a session chair, best look up your chairing duties in the conference system (Conftool) or the app.