by Deb Catavello and Grant Hartley
When we set out to research teachers’ feedback practices, we had a very vague idea of how to go about it. What we knew was what we did not want to do: we didn’t want to end up making qualitative judgements on what individual teachers do when giving feedback. Instead, we wanted to 1. find out what influences our (like the participants in our study, we’re first and foremost teachers) practices and 2. avoid oversimplifying things.
This is when Complexity Theory (CT) came in handy. In the words of Larsen-Freeman & Cameron (2008, p.1), “complexity theory aims to account for how the interacting parts of a complex system give rise to the system’s collective behaviour and how such a system simultaneously interacts with its environment.” This focus on how the relationships among constituents give rise to the whole seemed to fit with our researchers’ intent as we started looking at EAP teaching environments as complex systems which are situated in many networks: external networks such as education policy contexts, institutional policies & principles (e.g. the Centre principles) as well as internal networks (i.e. teachers’ identities, attitudes) which encompass also EAP subject matter understanding (textual and linguistic theories), and understandings of (language) learning (see Martin and Dismuke, 2018).
However, we’d be lying if we claimed that we had framed our study drawing on complexity theory from the beginning. This came later on, when we started puzzling over the data we’d collected. What came first was the idea of collecting data through screen capture. We wanted to observe teachers give feedback on a written assignment the same way you would observe them teach a class. The idea of using screen capture technology came from Ursula Wingate’s talk at the BALEAP Conference 2021. When we got in touch with her to seek advice, she rightly pointed out that screen recording on its own would not be enough and suggested combining it with interviews. So, we settled on the following data sources: semi-structured interviews with teachers followed by screen capture recordings. With hindsight, this made it possible to capture what Dotger and McQuitty (2014, p.77) call the teacher’s operative system which consists of an ideation level (=stated practices as presented in interviews) and a social level (behaviors observed in the screen capture enacting the stated practices).
Regarding the interview data, we felt it was important to analyze the data using some theoretical framework. We felt that Legitimation Code Theory’s (LCT) Autonomy dimension would be useful for two reasons. Firstly, autonomy codes seem to be especially relevant to this study, for as Maton (2018: p.6) states, “Autonomy begins from the simple premise that any set of practices comprises constituents (Positional Autonomy) that are related together in particular ways (Relational Autonomy)”. The second way in which we thought autonomy codes may prove useful is that they can account for alignment of practices within a field or context. This is relevant to this study which draws data from the 2021 Pre-sessional course situated within CALD, situated itself within a tertiary educational context. Being able to evaluate feedback practices in relation to CALD and the wider field of EAP in tertiary education might prove insightful in developing understandings of where teacher principles and practices come from, and how these are applied within the context of Pre-sessional courses.
Once we had established the analytical framework that we would work with, we needed a way of coding the data (what we will call “translation device” see Maton and Howard, 2018). To do this, we first needed to identify the constituents that seemed to be at play when teachers engaged with feedback. We did this through basic thematic analysis trying to identify what the main factors were that seemed to affect the way feedback was done. As a guide we started with some concepts outlined in Maton and Howard (2018) and these were adapted, added to or deleted as we saw fit in terms of this particular study (see Table 1).
| Positional Autonomy | Relational Autonomy |
| actors ideas artefacts institutions textual elements textual movements |
explicit procedures tacit conventions mechanisms purposes reasons ways of working unstated orthodoxies formal rules expectations |
As we transcribed and coded further interview data, the translation device evolved to account for the new data, allowing for “iterative movements between theory and data” (Maton, Hood and Shay, 2016: p.33) essential in maintaining a balance between the two. Our current existing translation device looks like the following (Table 2), and we feel it is beginning to account for the data in a way which is more user friendly while at the same time encompassing of all the meaning units so far encountered in the data.
| Positional Autonomy | Relational Autonomy |
| actors institutions learning theories linguistic theories |
causal dogma techniques/ technologies methods/ approaches |
As mentioned before, our hope is that our analysis using the autonomy dimension of LCT will give us insights not only into the constituents related to teachers’ feedback practices (PA), but also how these interact (RA). This is what we are now trying to investigate and below (Figure 1) are some provisional ideas we think would be interesting to explore. To give one example, our initial analysis appears to highlight how previous experiences (Actors) collides with linguistic and learning theories to inform the way teachers view the aims (Causal) of feedback.
So what is going to be the next stage in our research process? We feel that, to be true to the complexity theory perspective we’ve adopted and the social constructivist view we espouse here at the Centre, we now need to contact the teachers who participated in our study to get their views on our own interpretations. We hope their input will help us refine our translation device and perhaps make it fit to investigate other sets of practices in the future.
References
- Dotger, S., McQuitty, V., 2014. Describing elementary teachers’ operative systems: A case study. Elementary School Journal, [e-journal] 115(1), pp. 73-96. https://doi.org/10.1086/676945
- Larsen-Freeman, D. and Cameron, L., 2008. Complex systems and applied linguistics. Oxford: Oxford University Press.
- Maton, K. and Howard, S. K., 2018. Taking autonomy tours: A key to integrative knowledge-building, LCT Centre Occasional Paper 1 (June): 1–35. Available at <https://www.researchgate.net/profile/Karl-Maton/publication/325655299_Taking_autonomy_tours_A_key_to_integrative_knowledge-building/links/5cec782992851c1ad49802d5/Taking-autonomy-tours-A-key-to-integrative-knowledge-building.pdf> [Accessed 12 January 2022].
- Maton, K., Hood, S. and Shay, S., 2016. Knowledge Building Educational studies in Legitimation Code Theory. Oxon: Routledge.
- Martin, S. D. and Dismuke, S., 2018. Investigating Differences in Teacher Practices Through a Complexity Theory Lens: The Influence of Teacher Education. Journal of Teacher Education, [e-journal] 69(1), pp. 22–39. https://doi.org/10.1177/0022487117702573