How are the students doing?

I was wrapping up an eight hour day of marking submitted Google Drive Assignments, returning some assignments for further work, and answering questions coming in via Gmail and Schoology messages, when a colleague sent me a Messenger message asking, "How are your students doing?" And I realized that I really did not know. I was so far down in the weeds of marking incoming assignments that I had no overview. 

Sure, I can see work coming in, much of the work correctly done, but the rest of the picture is the work that I am not seeing. I am seeing only the success stories: the students who are getting their work submitted. At the end of a solid day of looking at work that has come in, I can come away with the feeling that things are generally going well. But I am only seeing good news. There is no direct way to measure learning, or the lack thereof, in what is not being submitted. There are many things that could cause an absence of submission, and failure to learn the material is only one possibility. 

Thus the only broader view I can have is the submission rate. Looking at the submission rate hides whether the submissions are coming in correctly done, but provides that view from on high of what is happening for my 120 students in six sections.

The data above is the average submission rate for assignments in each of my six sections: two sections of MS 150 Statistics, two sections of SC 130 Physical Science, one section of SC/SS 115 Ethnobotany, and one section of ESS 101w Walking for Fitness. The intent is to mimic a dashboard that provides at a glance the status of the submission rates in the four sections. The value shown at the bottom of a dial is a percentage out of 100. 100 would be all students having turned in all assignments due to date. 

The one bias in this data is that the average is sensitive to outliers. In the above chart a few students in a section turning in no assignments brings the average down significantly. The median is less sensitive to outliers in data.

The median submission rate is not as impacted by low outliers and is perhaps a more realistic picture of how things are going for those students who are engaging with the course.

At this point I realized I could use the approach taken to generate the medians to display boxplots of submission rates that would better show where there students were located in terms of submission rates.

One can see that there are low outliers including those with zero submissions, but that the bulk of the students are doing far better in terms of submission rates. The ethnobotany boxplot appears unusual because 20 of the 25 students have made all required submissions to date. 

I would note that the above charts were made possible by data derived from DropOut Detective. Although the charts required manual tabulation and some data analysis to generate, there would have been no practical way to obtain the data without the assistance of DropOut Detective.