Category: Governing

This week it felt like a natural step to track anxiety as the final visualisation. As a student, the time leading up to the end of the year can be filled with excitement about what’s next, but it can also be filled with stress and anxiety around studying for exams and doing well.

What I wanted to highlight visually in this visualisation was the weight and continuous cycle you can find yourself in once you become anxious of something… one thing can often lead to many others piling on. For me, the early part of the week was work focused and the end of the week more personal focused. Sunday scaries anyone?

When thinking about how this relates to governance, is collecting data on anxiety? As a non-educator, my first instinct is to imagine that it’s more useful to think about from both a learning and teaching perspective.

One theme that has stood out in this governance block is that data collected is collected with the end result in mind. An outcome ‘should‘ prove or disprove something so that action (i.e. policy and governance) can be created. Often, it can be forgotten that the itself data in isolation, however, can be personal and individualistic. Tracking anxiety as an example is an emotion that likely has different meanings to different individuals. It’s difficult to track because we cannot determine the meaning in absolutes as we can for a maths equation. This is one reason that today, tracking anxiety may not be valuable in terms of governance and policy.

When combined for the purpose of analysis, the collection of data has a chance becoming an average. The average then in turn is what the policy is based on. An important note is that tracking something ambiguous like anxiety, can have many causes. For example, while it may be fair to say that most students feel anxious about exams, students may feel that anxiety as a result of very different reasons, e.g. a fear of failure, a desire to make parents proud, or even a recent breakup or other personal matter that has significantly impacted their ability to focus on studying.

This may or may not impact student grades. For the student looking at the grade, they can relate and understand the correlation. However, if those grades are used to measure a teacher’s performance, a school administrator would see the ‘average‘ picture. The assumption here is that there are enough students to provide data that lessens the extremes. Another reason why I considered tracking anxiety was because this past pandemic year, I can imagine anxiety had an overall impact on the education system because students found themselves in an unknown and uncertain time. It could have an impact on governance and policy.

If we could accurately quantify, describe and standardise anxiety to collect data on it, this may prove valuable from a governance and policy perspective in the future, if we face a similar situation again. A likely scenario is using technology to do this as artificial intelligence models continue to be created and trained for the purpose of tracking emotions.

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.

Sources:

Ozga, J. 2016. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1) pp.69-81

This week I tracked personal interactions that I had with family, friends, and my partner through technology. In this case, technology is a phone call, Facetime hangout, and text messages.

My focus was to analyze how I interact with loved ones, and to keep this separate from the technology-enabled interactions that I have at work. Side note – If I was tracking interactions like Slack messages at work, we would likely need a small booklet of papers for the visual.

The interactions are very text message heavy and light on phone calls. I used colors to represent who I was interacting with and the symbol to denote the type of interaction.

I decided to place the symbols on the three lines radiating out, symbolising the interactions radiating out from me, i.e. I was the one initiating the interactions (calling, initiating a Facetime, or sending a text message). Each radiating line has slashes to represent day breaks, starting with Monday as the first day of the week. I chose not to track the specific time as that data point seemed ‘too much’.

In reflection, these three lines with symbols could in reality be anything – watching videos, engaging on online forums, sending tweets, etc. The only thing that ‘makes’ them interactions is the key of the visualisation. This point highlighted the following:

Labeling, understanding the label, and determining value of the data points is key when using a data set like this (i.e. a count) for policy and governance.

For example, there is nothing to denote whether or not these interactions were positive or negative. The symbols and colors only show that they occurred.

In this visualisation, the value is missing of each interaction.

For example, just because I used text messaging the most, does that mean it is the most valuable? Because I only made 3 phone calls, does that indicate that I don’t like phone calls?

My conclusion with this week’s visualisation is that data is just that – data. Without context, it’s difficult to demonstrate value. Conversely, too much context may spill into bias. Culturally, we often try very hard to find labels and categories to fit everything into a neat box, e.g. the box ticking exercise on any standardised test (age, gender, ethnicity, parents income level, etc).

The ironic thing is that life isn’t a neat box. It’s messy. I’d argue education is the same – messy. Education is where you are meant to make mistakes, learn by doing, practice and constantly build upon what you know. The learning process is messy, yet it appears that we are trying to fit it into a box when ‘everything’ becomes data (datafication) for the sake of policy and governance.