Data Mining: 6 Things I Learned from Student Login Times

With students visiting our site every day to do their homework, we've built up a large database of information. In this article (and future articles like it), we'll see what we can see in that data. This data is based on student logins between January 9, 2011, and February 13, 2011, adjusted to the student's local timezone.

#1. Weekends = Friday + Saturday

We noticed this one a while back just from our Google Analytics data: at least as far as students working on homework are concerned, the work week runs Sunday through Thursday, leaving Friday and Saturday for extracurricular activities.

#2. Any Time's a Good Time for Homework

Although more students log in per hour from about 7pm-10pm (their local time) than during other hours, login times spread throughout the day. One hour is 4.2% of a day, and there are 13 time slots (11am through 11pm) that are at or above that percentage of the daily logins. Even at 4am and 5am we get several hundred logins most days, but those are definitely our least-active hours. That's why we wake up early on release days to push releases out starting then.

#3. Students Who Use Macs Prepare for the Weekend

This difference is very small, and it may not be statistically significant, but our Mac users (shown in red here) appear to start their weeks early to prepare for the weekend. A greater percentage of our Mac logins occur on Sunday and Monday compared to Windows users. Conversely, Thursday through Saturday are slightly weighted toward PC.

#4. Early to Bed and Early to Rise Makes a Student (ever-so-slightly-more) Likely to Use a PC

A similar trend appears when I group the data by hour, rather than by day of the week. Through about noon, a greater percentage of our PC-using students log in than do our Mac users. Things mostly level out during the day, and then the Mac users begin to top their PC-using counterparts.

A Break for Pie

Most of our students still use Windows. However, Mac's share is definitely growing. Over the last year, Mac's share of the student pie has grown by about 10%.

Our students use a healthy mix of the four biggest browsers (Internet Explorer, Firefox, Safari, and Google Chrome), plus occasional logins from virtually every other browser out there (including, for example, 9 logins from Sony Playstations). As with OS, student use has definitely changed in the last year, but this pie is more divided. Both IE and Firefox have lost students, while Chrome use has more than doubled (and continues to increase every month). Safari use has also increased, but not everyone switching to Mac is switching to Safari.

#5. Geeks are Slightly More Likely to Work on Saturday

A few trends can be spotted in the browser login data if you squint. For example, the Safari daily logins, unsurprisingly, track with the Mac logins: Safari users get on the site earlier in the week, and then disappear for Friday and Saturday. Somewhat similarly, a smaller percentage of Internet Explorer users log in on Sundays than on other days (when compared to the other browsers). As a Chrome user looking at stats on a Saturday, I also found it interesting that users of Firefox and Chrome log in more on Saturday than do their Safari- and IE-using counterparts. Since Firefox and Chrome require the user to specifically install them (while Safari and IE are the default browsers for Mac and PC, respectively), one (stereotypically unsurprising) way to look at this is that the geekier users log in more on Saturday than do their less-tech-loving cohorts.

#6. IE and Firefox Users Like Mornings, Chrome and Safari Users Like Nights

Like with the Mac/PC time-of-day stats, our Internet Explorer and Firefox users start logging on earlier than our Safari and Chrome users. IE usage drops off faster than Firefox usage, though, until, around midnight, Firefox users are more likely to log on than even night-loving Safari users.

To be clear, I've drawn a lot of conclusions from very slight differences in this article. However, we may have to take a deeper look. Each of these groupings involves hundreds  or thousands of data points, so these small differences very well could be significant.

I plan to do more of these data mining expeditions to see what we might see. What else would you like to know?