We store enough information in our Blackbox data set to look at when most programming activity in BlueJ occurs. Most BlueJ users are students, so this should give us an idea of when most student programming occurs. Methodology notes below, but what you really want is the graph, so here it is, for the USA:
It’s a heatmap: time of day on the X axis, days on the Y axis, red is highest amount of activity, down through orange to white being zero activity (e.g. overnight). A few thoughts:
- Lunchtime is clearly visible in the data. Most programming takes place during the working day, partly because of scheduled classes. Despite stereotypes of night owl programmers, on average, people don’t program late at night.
- Not much programming on Mondays. One reason for this is that US federal public holidays mostly fall on Monday, which reduces the amount of activity (including in the evening), but I’m not sure if that completely explains it.
- No-one programs Friday night or Saturday… but check out that Sunday night my-assignment-is-due panic! At least, I’m guessing that’s the explanation
We could just look at time of day, but that loses a bit too much detail if you average across all days in the week. So it is better to look at times of day across the week, Monday to Sunday. Weekends are different across the world, so I’ve chosen to narrow by country. Rather than try and pick a list of all the Monday-to-Friday-workweek countries, I just looked at the USA, which is by far and away the country from which we get most data. We store enough information to know the user’s timezone, so I am adjusting properly for the multiple timezones and daylight savings.
The number for activity is a count of IDE events recorded in that hour. That is primarily source code edits, but also includes other things like debugger interactions. I think it’s a good proxy for programming activity, though.
Two of the next most frequent countries in the data are Germany and the UK. Here’s Germany:
and here’s the UK:
These frequencies are much lower (note the scale adjustments) so the data is noisier, and I suspect the frequencies are low enough that some patterns in the data are caused by individual institutions using BlueJ at particular times (remember that the students in our data are not totally independent: 100 UK students all programming at the same time at a university on Friday morning could noticeably affect the data). Germany has an odd pattern: more programming on Sunday than almost any other day of the week. This might be because there’s not much to do in Germany on a Sunday, but it also hints that maybe more work is done outside classes. Feel free to add your own speculation for any of the patterns above.