P’s Laptop

Investigating Data Collected from P’s Laptop.

P’s professional work involves a mixture of computer use and face-to-face interaction. Often he is expected to spend long periods of time working on specific projects on his computer. As such, P, W and I decided it might be beneficial for them to track their productivity when using the computer. This was because both participants said that they spend a lot of their time using their computer, both for work and recreation, and not tracking data from this central part of their day would be an oversight.

For this project we decided to run a week-long productivity experiment, where by we would track all activities done on the computers. These activities were then categorised depending on how ‘productive’ they were. This allowed for specific activities to remain concealed, but for the nature of the activity to be revealed in the wider picture. Further, it was also agreed with the participants that daily quantified break-downs of productivity would not be revealed in my project so not to endanger their employment.

However, it’s not just numbers that should be used to provide ethnographic insight, contextual correlation plays a huge role when it comes to reaching conclusions. This quantitive data is complemented with qualitative data generated through conversation with P.


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Visualising P’s Productivity Data for the Week

P felt that this week was a usual one for him, it was neither relaxed nor stressful. He thought that most days would be equal in productivity, and that we wouldn’t see much activity over the weekend as he was busy a lot of the time.

When prompted, P told me that he expected Wednesday to have been his most productive day and so was surprised that Thursday was quite considerably the most productive day. When questioned further why he thought this was, he said he remembered the work he did on Wednesday being the most “intense”. We eventually discussed the fact that not all the work he did that day was at the computer and so would not have been logged. This brought up an interesting insight as it identified that P didn’t separate work at his computer from work away from it. It is likely that Wednesday would have been P’s most productive day, it is just that the work he did that day was not tracked, logged and therefore isn’t shown above.


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Visualising P’s Computer Activity by the Hour

P spoke to me about how he feel most productive in the mornings most days, but often feels if he isn’t working well in the morning, he “makes up for lost time in the afternoon”; otherwise usually he feels he usually slacks a little bit more in the afternoon. This was represented in the data collected.

P also told me that during the weekend his partner was visiting, and so he went to a few events in the local area such as art galleries and restaurants. This meant that not much time was spent on the computer. This is reflected in the data gathered on Saturday and Sunday.