Week 5 Data Visualization


This week I decided to test how different events impacted my learning. I knew from the beginning that it was going to be a very eventful week, hence I capitalized on that and tried to do my analysis around this fact. Considering the fact that this was my birthday week, my sibling’s graduation, and my relocation to a new house I knew it was definitely going to have a toll on my learning. Hence I decided to record data to investigate how the different events and my emotions at different times will impact my productivity. Most of the events were towards the end of the week and others spread across the week so it was easy to have valuable data to record all through situs slot gacor.


To properly understand what was going on I tracked my academic engagements and what event was distracting me during my engagement in that particular event. I decided to represent the activities as follows:

Downward and upward cone to represent the learning activities 

Sun moon and star represent birthday, relocating, and graduation respectively.

The greater than, less than and, equal to signs represent high, low, and moderate productivity respectively.

I used different emojis to represent different emotions. This was an attempt to datafy and personalize my learning to a degree.


  • The visualization did not give an explicit correlation between my emotions and my productivity is given a particular event.
  • If it was hard for me to link my emotions and their impact on learning I wonder how a machine will achieve this
  • I had assumed that if am tired and frustrated I would definitely perform low but I was surprised that it was not the case. This shows me that sometimes not so positive emotions do not necessarily have a negative impact on learning 
  • Also, I found that just because one is happy or calm does not mean they will have high productivity 
  • In relation to emotions and events, I realized that positive emotions were mostly linked with my birthday events. This showed the value of the individual 

One reply on “Week 5 Data Visualization”

I really enjoyed your dataviz–it’s an inventive way to try to capture the relation between events and emotions. I wonder how you decided on the emotion categories? Is this just a common sense way of thinking about different emotional states? In the analytics industry, there is now considerable uptake of emotion detection techniques, often based on psychological classifications of ‘Basic Emotions’, for things like facial emotional detection. The interesting thing here is how using facial emotional detection technologies depends on a long history of reducing the complexity of human feelings to identifiable signals given off by facial muscles. In the next block on teaching with data, you may want to consider how teachers might engage with ’emotion learning analytics’, which involves identifying student feelings related to learning tasks from either facial vision cameras, voice tone analysis, wearable biometrics, or text analysis. It’s really fascinating and in some ways potentially really troubling.

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