Or: How to solve the Case of the Disappearing Students.

At midnight, we wondered—where do all those people who show up to the first lectures and vanish into the aether go? But perhaps the more important question is, how many of them are there? Using, for the first time, a combination of van Eyring’s 1984 Indifference Theorem and Satthelwaite’s 1532 Chaos Theorem, we here at Bwog can provide you with answers!

Indifference Theorem:

Van Eyring’s Indifference Theorem proposes that, for any college class which is required by a certain number of majors, there will be a corresponding level of apathy among students. This can be modeled in several ways, but, for simplicity, a single-peaked model will do. Thus we work with the variables:

Number of people that don’t care about a class, and thus, won’t show up = (total number of people in class) * (1 – retention value)

Retention value = .9 – (1 – subject-based value)

Subject-based values are complicated probability measures, so Bwog’s developed a shorthand:

• If it’s an intro class, assume immediately that 30% of the class will not show up next time.
• If it’s a math or CS class, right around 25%
• If it’s a core class, all will still be registered, but assume 10% will regularly not show up.
• If it’s an English or humanities lecture, 15%.
• If it doesn’t fit cleanly into any of these, assume 20% and move on.

For example, in an intro CS class of 286 people, van Eyring predicts that roughly 79 of those students won’t show up, while Bwog’s shorthand measure predicts that 71 will miss out—well within the standard deviation.

Chaos Theorem

Sounds simple enough, right? But van Eyring’s theorem fails to account for the difference between students who drop a class, students who attend online lectures, and students who are still registered but drop off the face of the Earth. Fortunately, the unappreciated-in-his-time genius of Theodore Satthelwaite II, who in 1532 predicted the existence of both Zoom and Cool Ranch Doritos, can help us here.

In essence, in any class with a populated waitlist, students who drop open up slots for more students to join. In the intro CS example mentioned above, if 40 of those 71 drop the class, then to the layman’s eye it will appear as though only 31 students are missing.

This churn can be expressed by the function m + 2(s * rv)/3, where m is the predicted number of students missing, s is the number of students on the waitlist, and rv is the retention value as calculated above. Simply take the derivative with respect to time—where it equals 0 is the point at which egress matches ingress, and the class size will stabilize.

Happy number crunching!

Empty Lecture via Bwarchives