Creación y presentación de materialidad no corporal:

Using Ushioda’s metaphor of an analytical lens (2015, 2016), I have conducted cycles of analysis, trying to focus in on the critical incidents and most salient moments relating to motivational synergy. The collection of data is also part of the analytical model as well, and I

utilised a form of ‘narrative knowledging’ to make sense of emergent issues and identify the

key themes. I also relied heavily on NVivo 11 to organise the data and look for holistic themes by using analytical queries such as word frequencies and sociograms, which will be presented in Chapter Six.

Pedagogic Data

Assignments

Reflection pieces

Journal Data Teacher/Researcher

Observation Data

Recordings

Ad-Hoc class based interviews

In conducting this inquiry, I have recorded twenty-eight 90-minute lessons in the spring semester, and then twenty-seven in the autumn semester. On top of this I have collected hours of recordings from the students themselves, in the form of videos and audio recordings which they produced as course-work, and also recordings from on-task interactions that took

place during lessons in the CALL rooms. Furthermore, I have included the students’ essays

and reaction papers, as well as other samples of their work ranging from test-scores to personal emails, as well as trace-data (Rodriguez & Ryave, 2002) harvested from the interactions which they participated in on Moodle (the class VLE). I have also recorded my own audio-teaching journals (which serve simultaneously as both data and analysis), as well as my own written journal and field logs and other notes. In addition to all this there are numerous photographs I took of the board-work for the lessons, as well as the teaching materials used throughout the course. Although Table 3.3 does not account for all the data sources, it provides the most comprehensive list of the sources and data-types which were used in the compilation of this narrative.

Table 3.3 Summary of data types

Chronology Data Type Data Description

Data collected during the CLERAC course (April 2014 – January 2015)

Pedagogic data Work done by students as part of the CLERAC course, also includes my own teaching materials

Field notes (as opposed to journal entries)

Logs, observations and notes made during the CLERAC course

Trace data Includes emails and online

interactions as part of the course VLE.

Audio/Audio-visual data Recordings of every CLERAC class Audio Teacher/Researcher journal Recordings made by students as part of coursework

Recordings of students on-task Ad-Hoc interviews conducted whilst monitoring the students

Institutional end of course (EOC) evaluation questionnaire

End of course evaluation done by the students anonymously when the teacher/researcher was not present and conducted by the institution

Data collected after the CLERAC course (August 2012 – June 2015)

Academic writing My own published or in-progress academic writing that mentions the CLERAC course

Journal reflections and narrative (as opposed to field notes)

Notes and observations made subsequently and transcribed as teacher/researcher journal entries, many of which use stimulated recall Student follow-up

emails/meetings

Includes both coincidental and solicited reflections from students after the course. Meetings were only conducted with one participant (Mr Charge)

All told, I have composed over half a million words on word-processed documents, and recorded 13,609 minutes (227 hours, or 9.4 days) of audio data for this study. My field journal alone runs to over 80,000 words, with students’ essays adding up to a total of about 50,000

words. Of course, not all of this is relevant to the focus of this inquiry, and yet in order to do the study justice I have had to carefully manage how I approach this unwieldly amount of

data. One of the strategies I employed was selective sampling, both in terms of participants and events. By this I mean that I had to focus on certain key moments (defined as either snapshots or critical incidents) and pay particular attention to these moments whilst allowing the events which lead up to them to fall away into the background. Snapshots are basically

moments when I collected data in the class in order to try to ascertain my students’ reaction

to the lessons, whereas critical incidents are moments which have emerged through reflection as being of significance to my professional development (Farrell, 2008; Finch, 2010; Tripp, 1993). These two contextual-signifiers have thus provided me with a way to clear a path through the data which leads directly through the most important events in the collection phase towards the central themes of the study. Another aspect of the selective sampling technique was to focus on interactions with particular individuals, those whom I identified as being key persons (Focal Participants) within the class who contributed to its dynamics.

In order to manage the data I have also used a combination of structured/strategic and random sampling. In other words, I have gone back over data sources which I had indicated as important in my reflections and I have also selected other areas at random, looking for things which might surface as important. Often this stimulates me to look at other sources

and I sometimes ‘find a trail’ in the data which I follow up based on a random sample. Finally,

once the key themes were established (motivational synergy) I listened back to all of the audio reflections and wrote a summary-transcript which I added to the written journal in order to make it searchable and run queries from NVivo.

Mainly, I prioritised certain data types over others as well. For example, I decided not to listen back to all of the class audio, just the ones which seemed particularly important. Another way I have analysed the data is by talking to people about it. For example, despite not being a teacher herself, my wife has been an important person in helping me to ‘talk things into understanding’ (Mann, 2002). In particular, I made a special effort to have more

social contact with colleagues and other teachers, in which we nearly always ‘talk shop’. I

would often note down things from these informal social gatherings. I have also made more formal attempts to collaboratively reflect on my practice by joining a research group at my university. Also, my own academic writing (although mainly authored individually) always goes through a process of review, and on occasion I have written collaboratively. This also counts (although often indirectly) towards the analysis of the work I am doing on the PhD. Presenting at conferences is similarly collaborative in this respect. This is also something

recently advocated in the data-driven approach to reflective practice by Walsh and Mann (2015). Finally, there is of course a great deal of collaboration and feedback involved in the process of conducting a PhD, and clearly the tutorials with my supervisor have had a large impact on the way I approached the inquiry.