Topics will vary from semester to semester. See Computer Science Division announcements.

In this special topics course, small teams (2-3) of graduate and undergraduate students will use PrairieLearn to develop and rigorously evaluate rich, machine-gradable assessments that would address learning goals that might arise in typical EECS courses. The assessments will promote mastery learning (aka proficiency learning) by following the acronym STAR: Specific to a particular competency, Tagged to specific learning outcomes, Autogradable, and Randomized so that many variants can be auto-generated on demand. In addition to developing assessments, student teams will evaluate them by using the methods of HCI and education research to run either informal or formal pilot studies. We will encourage students to make any resulting artifacts available as open educational resources.

* Time conflicts are NOT allowed - mandatory attendance.

* Anyone can apply but strict priority will be given to EECS grad students.

Repeat Rules