Last Sunday, I hurt my finger and as a result needed to wear a bandage through Friday of last week. In light of my predicament, I decided to track the number of times that I needed help with something in comparison to when I could still do something myself.
I’m usually not one to ask for help, so having an impaired finger stretched me outside of my comfort zone. I tracked this from three perspectives – personal, household, and work for five days.
- The personal activities were limited to getting coffee/water, hair brushing, hand washing, and texting/calling.
- The household activities were limited to dishes, laundry, taking the trash out, and mopping/sweeping.
- Most of the work activities I could do by myself with the exception of sending the two parcels that I needed to send last week.
In reflection (and in reality), the daily activities that I could have tracked are countless, but some are ones that I don’t necessarily want to make public (like help getting dressed), connecting us to the privacy theme explored in the previous blocks.
When I was thinking about how to visualise the activities earlier in the week, I did a quick Google search and checked out the images tab for inspiration. One of the the images that I stumbled upon was related to a marketing persona with a timeline of a users activity on a website. Looking at that detailed audit trail was part of the reason that I didn’t want to track every activity. On a certain level, it starts to feel creepy, just like your phone recommending a new friend on Facebook when you’ve only had a conversation about them with a friend…
The other thing that I wanted to explore was what I could do by myself versus what I needed help with. The idea behind this was to spend some time reflecting on comparison as a one reason why we collect the data is for comparison (and ultimately ranking for decision making around policy and governance), e.g. one class is doing better than another, one teacher has more engagement in their class, etc.
In particular, this week made me reflect on several points made in the Ozga (2016) reading:
- What is ‘good’ data? Where could needing help fall on the spectrum for collecting and reflecting on good data?
- Does needing help rank well or poorly if it ultimately achieves the same outcome, like the laundry being done?
- If the context of being hurt was left out, how does this change the perception of the data?
- If I was ranking how much, or the value, of the help that I received, how would I write the descriptions for ‘outstanding’, ‘good’, ‘needs improvement’, and ‘inadequate’?
Maybe a more useful way to have visualised this week’s data is on a timeline to see the trend of needing less help throughout the week rather than categorisation? Would that make the data appear more ‘good’, or make it rank better?
These are just a few questions that I would have for someone responsible for collecting and visualising student data, if the goal was decision making, policy and governance.
Ozga, J. 2016. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1) pp.69-81
One reply on “Getting Help throughout the week”
‘In light of my predicament, I decided to track the number of times that I needed help with something in comparison to when I could still do something myself.’
Sorry to hear about that, but useful way of turning your injury into some productive reflection on data!
‘Looking at that detailed audit trail was part of the reason that I didn’t want to track every activity.’
And I suppose this becomes particularly important when thinking about governance, given that it implies one’s data being exposed to wider scrutiny, beyond, for example teachers and peers, where one might feel more of a sense of trust.
‘Does needing help rank well or poorly if it ultimately achieves the same outcome, like the laundry being done?’
Great questions. This seems to point to issues related to performativity. Where ‘outcomes-based’ visions of education prevail, one can see that data analysis would be directed towards achievement, rather than perhaps the more interesting and educationally-beneficial information about *how* one gets to the point of achievement.
‘If the context of being hurt was left out, how does this change the perception of the data?’
Again, super question that raises the concern of how much contextual information we include in data-driven analysis. Given the focus on comparison, one wonders what kind of assumptions are made about where students have come from, and the position they are in *before* they undertake standardised education. Data analysis made by applied equally, but not necessarily ‘equitably’?