Students also examined the deviation of section size from the ideal of 15 students to evaluate the misallocation of TFs. For example, larger sections indicate that not enough TFs were hired for a course.

When comparing expected section size deviation, the MLS reduced the deviation by about half, minimizing the misassignment of TFs.

Shieber warned, however, that the CS model itself is not foolproof. For example, it would not recognize that a course such as a sophomore tutorial is a required class for all concentrators and would most likely underestimate the number of students who will take the course.

And though it can correct TF misallocation, the model would not address classroom allocation, textbook shortages, and coursepack availability, Sheiber said.

But with human corrections, he maintains that MLS gives fairly good predictions for course enrollments.

The class recommended that the College implement a policy of “mandatory non-binding preregistration,” such as anonymous preregistration, to supplement the program.

Dean of the Graduate School of Arts and Sciences Peter Ellison said that although he had not seen a formal report from the class, he is very interested in the implications of course enrollment predictions.

“We will never be able to make all the [TF] appointments in advance, since no enrollment prediction methods are foolproof,” Ellison said. “[But] if the CS 96 project is helpful in this, I would love to use it.”

—Staff writer Risheng Xu can be reached at xu4@fas.harvard.edu.