I recently needed to calculate the number active users on our Moodle system so I downloaded a “login” file from Intelliboard, our Moodle analytics system, and started playing around with that data. Like most data analysis projects, the first step is to clean up the data. When I imported the .CSV file there were 55344 records, but many of those records were bad and needed to be removed. For example, there were more than 1700 records with blank names and more than 31000 had a “Time On Site” listed as zero seconds. After filtering out all of those records, I ended up with a data frame containing only 5291. That’s what I call cleaning up data!

Once I had the data prepped, it was time to start playing around with it. For example, I knew the date each of those 5291 accounts were created. The first was January 24, 2012, the last was September 06, 2018, and the median date was November 17, 2016.

Next, I decided to graph the number of accounts created for each day over the past few years. Note: the following graph is interactive and the number of accounts created along with the date is displayed when the mouse hovers over the graph.

Woah! Do you see that spike in the middle of 2015? I looked at the data again and found the maximum number of registrations for a single day was 477 on 2015-07-24. I’m not sure what happened that day but there is obviously bad data. Unfortunately, there could have been several legitimate registrations on that day so it would not be possible for me to figure out what is good data and what is bad.

I then decided to play around with the amount of time spent on the Moodle site. I found that the shortest time on site was 2S, the median time on site was 4H 39M 22S, and the longest time on site was 23H 59M 36S. I wondered if there was any sort of pattern to the total time spent on site so I dropped all of those 5291 times into a graph. Note: this graph is interactive and the amount of time for any given student is displayed when the mouse hovers over the graph.

There are 5291 students in this data frame but more than 1700 of them, almost a third, have less than one hour total in the Moodle system. It is possible that some of our students are using Moodle for a face-to-face class and only have ot occasionally access it, or maybe some students drop their class and do not spend much time in the system. However, most of our Moodle students are taking online classes and we certainly do not have a third of our students drop, so it seems reasonable to assume that many of our students are simply not spending as much time in Moodle as they probably should for a class.

I took one last look at this data and plotted the month and year that students first logged into Moodle. I expected to see a lot of students first logging in during August and January, the start of a new semester, and I was not disappointed. However, the following graph shows a significant uptick in new students starting in August 2017. Note: the following graph is interactive. The month/year and number of students first logging into Moodle are displayed in the popup tooltip.

This was a fun excursion into Moodle. While there were a few minor surprises along the way, these calculations and graphs were well within my unexpectation.