How does your research methodology impact methods?
This blog has been rather quiet for various reasons but, as we are cruising towards the next Networked Learning Conference (NLC), the time is ripe (if not very overdue) to share some media, including the video (poor sound though sorry), from my ‘short paper’ presentation in Malta 2024, slides as images below. I’ve hosted the video on archive.org because there are less concerns about big tech profiteering over there, however this also may mean the video does not play, or not play smoothly for you. There’s a link to the submitted paper below.
We at hanfod.NL have some exciting plans for the coming conference and I’ll share more in another post.
The paper highlights one of the key methodological insights from my doctoral thesis and recent book chapter. At the conference, in short form, the paper was a provocation, as much as anything, trying to encourage researchers to reflect on what exactly they think they have when they gather data. In many areas of research it is presumed that data will be collected, analysed, and presented. However, there are so many assumptions made all along that method. One thing that phenomenology teaches us is that there is a gap between some thing and what we notice about it, and probably even more drift between the thing and what other people tell us about it. Phenomenologists try to account for this gap by focusing on the phenomena, rather than the person’s individual experience of that phenomena (which should ring alarm bells for readers who want phenomenology to be ‘ideographic’, focusing on an individual’s experience). This is why we have ‘bracketing’: we may wish to try and exclude some thoughts as polluting my conscious distilling of the essence of a phenomenon. To whatever extent this may be possible, my paper suggests that we may also wish to actively pre-load our thinking as we approach data gathering. If human science values the human researcher as the premiere research instrument, more attention could be given to the researcher’s frame of mind in those epic moments when phenomena are encountered.
Johnson, M. R. (2024). Mobilage thinking and empirical encounters: Data gathering and analysis of networked learning experiences. Proceedings of the International Conference on Networked Learning , 14(1). https://doi.org/10.54337/nlc.v14i1.8087
We try and avoid hyperbole, but Dr Kyungmee’s next job seems really amazing. At the end of the month, she’s relocating from Lancaster’s CTEL, to join the Department of Education at Seoul National University, South Korea as Associate Professor in Qualitative Research Methodology. Her task: bring qualitative research to South Korea, where a very high proportion of research outputs are quantitative.
Kyungmee was keen to visit us in Wales for a number of reasons, apart from simply sharing the same time and space to discuss ideas in-person, which was a wonderful privilege. Kyungmee is contributing a chapter to our book, and I’ve known her since 2014 when I started the CTEL doctoral programme. Since then we’ve also popped up at the Networked Learning Conferences together, and hopefully we’ll meet again in Malta for NLC there next year. Hope you can join us!
Yesterday, for an hour in the Glamorgan Council Chamber, we piggy-backed onto Cardiff University School of Social Sciences’ education research seminar series for 2022-23, with a session exploring the claims/discourses around Artificial Intelligence with respect to qualitative research. Kyungmee noticed that AI does not ‘struggle’, indeed that is a selling point, where AI promises to alleviate struggle and help us achieve ‘better research findings’ in a ‘smarter’ way and outputs that we can have greater confidence in. This can be seen in marketing for recent AI enhancements to ATLAS.ti, a popular qualitative data analysis platform. But are AI shortcuts legitimate to authentically develop deep insights into human experiences, such as those featured in a recent ‘Autoethnography’ special issue of Studies in Technology Enhanced Learning, where authors are concerned with workplace bullying, discrimination, institutional racism…?? AI discourses play into dominant wider (meta-)discourses of an ‘economic-pragmatic nature, that demands fast, efficient, predictable and controllable productivity from the educational institutions.” (Hodgson et al. 2012, p300, drawing upon Levinson & Nielsen’s use of Dyson, 1999). This is at least a paradox when also considering educational trajectories that cherish students’ development towards autonomous and collaborative criticality and creativity. In our post-digital era, student and researcher already faced an existential threat from information over-production, a seemingly ever-growing barrier to enter and stay abreast of almost any field. AI solutions to the processes of literature reviewing seem benign, and even helpful. But the discourses around AI invite us to distrust humans: ‘Data has a better idea’. This runs counter to ground that qualitative researchers had presumed they occupied. As De Silva and El-Ayoubi (2023) indicate, all aspects of human science question ideation, method selection, data analysis, writing up and review, could be outsourced to software. Neoliberal higher education is sucking us dry with imperatives to do more with less: churn high-ranking impactful outputs under conditions of diminishing salaries, career uncertainty and over-work. We’re tired. Even while writing this, WordPress is suggesting that AI can make up for my humanity – how ironically demeaning.
Nevertheless, Kyungmee said, qualitative researchers contend that, “humans are political beings in unique historical contexts, with our own struggles, perspectives, experiences, and narratives that are subjective and partial.” We must continue to expose social inequalities, the lived experiences of struggle, power relationships/conflicts in people’s complex and nuanced ordinary everyday human life. In the face of Big Data and AI, autoethnography sails in the opposite direction. Indeed, the graft of writing is so bound up in autoethnography and phenomenology it is hard to see a place for AI, unless we meant Authentic Intelligence.
Dr Kyungmee Lee at the Glamorgan Building Council Chamber
Dyson, A. 1999. Inclusion and inclusions: theories and discourses in inclusive education. In: Daniels, H. and Garner, P. eds. World yearbook of education. 1999: Inclusive education. London: Kogan Page, pp. 36–53.
Hodgson, V., McConnell, D., & Dirckinck-Holmfeld, L. (2012). The Theory, Practice and Pedagogy of Networked Learning. In L. Dirckinck-Holmfeld, V. Hodgson, & D. McConnell (Eds.), Exploring the Theory, Pedagogy and Practice of Networked Learning (pp. 291–305). Springer New York.
Levinsen, K. T., & Nielsen, J. (2012). Innovating Design for Learning in the Networked Society. In L. Dirckinck-Holmfeld, V. Hodgson, & D. McConnell (Eds.), Exploring the Theory, Pedagogy and Practice of Networked Learning (pp. 237–256). Springer New York.
It is wonderful to hear Kyungmee speak on this episode. I’ve known Kyungmee for several years since she is a tutor on the doctoral programme I studied with, at Lancaster’s Centre for Technology Enhanced Learning. I particularly love the attitude she exemplifies that gracefully refuses to accept an unhealthy status quo. This recording previews Kyungmee’s paper which she is due to present, en route to England, via Seoul, Turkey and then Sundsvall! Kyungmee makes a fascinating case for using Lived Experience Descriptions (Van Manen, 2014) alongside evocative writing in autoethnography. Thank you Kyungmee! Not long now… Follow her on Twitter.
Daisy chain cc by jamessant on Flickr. Kyungmee previews her #NLC2022 paper
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