Block 3: Week 11

Week of Visitations

This week was a rather challenging one because I spent most of it in the hospital due to ill health, however this did not stop me from capturing data for my blog activity. As I lay on my hospital bed and wondered what data to capture to be able to still add value to my reading I noticed the visits of different medical practitioners in the ward to look after patients’ needs. There it was. I decided to track every time a medical personnel came into the ward, the duration of their visit and the time of day they visited. Since I was weak and asleep most of the time I told my kid sister who was mostly with me to do the tracking.

ankara escort bahçelievler escort balgat escort batıkent escort beşevler escort büyükesat escort çankaya escort cebeci escort çukurambar escort demetevler escort dikmen escort elvankent escort eryaman escort esat escort etimesgut escort etlik escort

It was interesting because my initial thought was to record the visits which were made to me but I realized she recorded every visit whether it was directed to me or not as long as medical personnel walked in, she recorded it. Initially, I was upset but after critical consideration, I realized this will be a good opportunity to expound and visualize something that will buttress Ozga’s concept of ‘thin description’  about data-based decision making. The image below represents the visits to my ward by medical personnel over a five days period at the hospital.

Findings from my visualization

  • Across all the days, over 80% of the visits made were under 30mins 
  • Over the week, I recorded an average of ten visits per day by medical practitioners 
  • Visits that lasted more than 30mins were only made during the day and night visits were always relatively short
  • Monday and Wednesday recorded the highest visits with Sunday accounting  for the least number of visits 

Relationship with Governance

As discussed by Jenny Ozga, one of the limitations of data based decision making over expert knowledge is the inability for data to tell the whole story especially when certain indicators have been excluded from the visualization.  This is what she calls a thin quantitative description of data. Where the data is stripped of its qualitative contextual complexities and replaced with accessible and available data which is used as a base for policy making. 

 One might look at my hospital dataviz and infer that because I had several visits  a day by medical practitioners, it means I had the best care or even go a step further to recommend the hospital for good performance. Except that was not the case, most visits were not for any medical purposes and sometimes it took hours for medical personnel to show up when patients are in distress.

Envisioning data in its completeness including all its complexities is a fix for such problematic description of data. For this to be possible proper infrastructure needs to be put in place from the indicators, the data collection process through to the analysis and interpretation of data. This can help curb some of the issues arising from the use of data for policymaking in education and other areas of governance.


Ozga, J., 2015. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1), pp.69-81.

One reply on “Block 3: Week 11”

I do hope you are recovering well Festus. So sorry to hear about your stay in hospital. But it has proved to be very productive, as you’ve made some really important observations here based on your reading of Ozga. Your point about “thin quantitative description of data … stripped of its qualitative contextual complexities” is really important, and it would be great to see you developing these ideas further when you are able to start thinking about the assignment. I wonder what kind of “infrastructure” would need to be developed to enable more contextual data to be collected too?

Leave a Reply

Your email address will not be published. Required fields are marked *