We compare open- and closed-response versions of two questions that measure students’ reasoning on the Physics Measurement Questionnaire, same mean different spread (SMDS), and different mean same spread (DMSS). We conducted an experiment in one class (𝑁 =248) to test if asking students for their reasoning on an open-response question changes their answer to the closed-response question.

Education literature recommends many different strategies for structuring student group work in labs. Many of these strategies, however, have not been sufficiently evaluated for their effects on student outcomes. One prior study suggested that sharing roles, rather than splitting roles, in lab groups can boost students’ physics interest and self-efficacy. Here, we expand upon this literature by evaluating the effects of a broader range of role distributions across several student outcomes in a large sample at two different institutions.

There is increasing interest in deploying machine learning models at scale for multi-institutional studies in physics education research. Here we investigate the efficacy of applying machine learning models to institutions outside of their training set, using natural language processing to code open-ended survey responses. We find that, in general, changing institutional contexts can affect machine learning estimates of code frequencies: either previously documented sources of uncertainty increase in magnitude, new unknown sources of uncertainty emerge, or both.