Search for: All records

Recent years have seen a movement within the research-based assessment development community towards item formats that go beyond simple multiple-choice formats. Some have moved towards free-response questions, particularly at the upper-division level; however, free-response items have the constraint that they must be scored by hand. To avoid this limitation, some assessment developers have moved toward formats that maintain the closed-response format, while still providing more nuanced insight into student reasoning. One such format is known as coupled, multiple response (CMR). This format pairs multiple-choice and multiple-response formats to allow students to both commit to an answer in addition to selecting options that correspond with their reasoning. In addition to being machine-scorable, this format allows for more nuanced scoring than simple right or wrong. However, such nuanced scoring presents a potential challenge with respect to utilizing certain testing theories to construct validity arguments for the assessment. In particular, Item Response Theory (IRT) models often assume dichotomously scored items. While polytomous IRT models do exist, each brings with it certain constraints and limitations. Here, we will explore multiple IRT models and scoring schema using data from an existing CMR test, with the goal of providing guidance and insight for possible methods for simultaneously leveraging the affordances of both the CMR format and IRT models in the context of constructing validity arguments for research-based assessments. 

Research based assessments have a productive and storied history in PER. While useful for conducting research on student learning, their utility is limited for instructors interested in improving their own courses. We have developed a new assessment design process that leverages three-dimensional learning, evidence-centered design, and self-regulated learning to deliver actionable feedback to instructors about supporting their students' learning. We are using this approach to design the Thermal and Statistical Physics Assessment (TaSPA), which also allows instructors to choose learning goals that align with their teaching. Perhaps more importantly, this system will be completely automated when it is completed, making the assessment scalable with minimal burden on instructors and researchers. This work represents an advancement in how we assess physics learning at a large scale and how the PER community can better support physics instructors and students.