Workshop 3: Analysis

To start the session we were asked to add images of Human and Non-Human Researchers to the group Padlet page as a form of semiotic analysis.

You can probably imagine, the memes were my additions – I feel memes and gifs bring a bit of humour into something which may be considered a little dry… It was interesting to see different images of ‘researchers’, and there seemed to be a few entries of children or babies researching through taste and touch.

It was interesting to see how the images could then be categorised. Potential categories other than human / non-human were:

• Digital & analogue
• Sensory & digital
• Experiential – taste / play / listen
• Play / focused vs structured / unstructured
• Subjective vs factual/scientific

Next, we looked at data poetry as a mode of research analysis. The idea was to create a poem from the dataset you were given, without adjusting the order of words or adding words.

I was apprehensive at first about the idea of using poetry as a form of data analysis, but when we split off into our smaller groups (I was with Megan and Deborah) and were analysing the dialogue we had chosen out of those provided, a beautiful poem had emerged. Although grammatically incorrect, the gist of what was expressed in the dialogue was well captured. Our process was to look through the raw data and assess what seemed to be key words or phrases, eliminating the unnecessary filler words in sentences. We were left with a string of profound words, what we decided to call our data poem. You can see what our working document here, and the rest of the group’s data poetry here. It was interesting to see how we all tackled the task in different ways.

The battle of neoliberal universities

Identity formation,  
university studies,  
focus, mature, better 
critically engaged student, pain the ass.  

Deeply flawed institutions 
Business model, Dominate 
It’ problematic 
I was on the other side for a change.  

acutely aware 
students short changed 
I don’t think it’s unique  
not enough contact time.  

universities making money 
companies, corporations 
Students, Resource 
maximised profit.  

going to university 
Disconnect 
what students are sold 
what they pay for, 
what they receive.

by Frances, Megan and Deborah

Finally, we looked at thematic coding as a form of data analysis. Although it sounds simple, I find thematic coding difficult to do as you continuously question the validity of your themes. Are you creating too many themes within the data, are you extracting the right bits? Nevertheless, we tackled the task given as another small group:

Personally, I felt like the themes which were pulled were more around key topics which was covered in the dialogue, and perhaps that is how I should be looking at what thematic analysis is. Here’s our themes we came up with.

I appreciate that thematic analysis is a good data analysis method, and hope to be using it with my dataset form the survey (and follow-up interview/s) to see if there are recurring experiences or views.

---

Sadly, I had to leave early during this session due to an appointment at the Japanese Embassy, however, I was able to catch up with what I missed asynchronously.

A very useful reminder on assessed elements during the presentation:

I also saw that there was a collated Padlet created during the session of my peers’ opinions on what bad presentation practice is. I had a read through and I’m very conscious of the fact that I will need to ensure I don’t have too much text on my slides, that I avoid being ‘too dry’ (no monotone voice) and do not assume the knowledge of the audience. Here’s to hoping that I can pull this off.