We designed an online instructional module to teach the concepts of force and motion. The module was created in two versions, taking into account the expertise reversal effect, which suggests that different levels of guidance may be needed for learners with different levels of domain knowledge in physics. The high level guidance (HLG) version included continuous animations and voice narration to guide learners, while the low level guidance (LLG) version allowed learners to progress at their own pace and provided pop-up content based on their interactions with the module. We tested these versions with students enrolled in a first-year algebra-based mechanics course. The results showed that both versions of the module were generally effective in teaching students the principles of force and motion. However, we did not find any evidence of the expertise reversal effect in that the modules did not seem to provide adaptive instruction based on students' domain knowledge levels.  We will discuss possible reasons for our results and implications for creation of multimedia materials for learning.

The Modeling Assessment for Physics Laboratory Experiments (MAPLE) is a research-based assessment instrument designed to measure student proficiency with modeling in experimental physics in upper-division electronics and optics courses. The assessment's design was informed by the Experimental Modeling Framework, which consists of iterating through five subtasks: Making Measurements, Constructing Models, Making Comparisons, Proposing Causes, and Enacting Revisions. Analysis of the electronics survey data indicate that students engage less with the Propose Causes subtask in the ‘choose your own adventure’ activity part of the assessment as compared to other subtasks. Here, we explore how students are interacting with the Propose Causes subtask and how this can inform laboratory instruction.

To understand the connections students make between discrete and continuous systems and how computation supports those connections, we present a case study of one student. This participant took a junior-level quantum mechanics course and the accompanying computational lab course. The following year, she became an undergraduate TA for that computational lab course. In a video elicitation interview, the participant watched a video clip of herself when she was a student working on a computational activity with a partner. She was asked to reflect on her experience with the task both as a student and as a TA. From this interview, we have identified some of the challenges and affordances of using computational activities for facilitating students’ connections between discrete and continuous systems. Challenges included understanding functions as arrays in the context of computation and the contextual meaning of mathematical objects like Δx.

This talk will share preliminary results from an ongoing investigation of the development of quantitative literacy among physics majors through the use of the Physics Inventory for Quantitative Literacy (PIQL). The PIQL is a reasoning inventory that assesses students' quantitative skills in calculus-based introductory physics courses through upper-division courses. Through qualitative analysis of free response survey items and quantitative analysis of PIQL responses taken at different stages of majors’ course-taking, we characterize features of  physics quantitative literacy development of a sample of students as they progress through the physics major. This research examines the aspects of quantitative literacy in which students tend to improve, the factors that might contribute to better performance, and some implications for future pedagogy. The findings will inform a larger, ongoing research project and contribute to the understanding of how students develop quantitative literacy in the college physics curriculum, and how the instructors can foster its development.

Building on many prior studies that have looked at student ability to use vectors and vector skills like addition, subtraction, and multiplication, we use network analysis techniques to explore the relationship between students' performance on vector addition and vector subtraction questions. To facilitate this, we surveyed introductory algebra-based students using a multiple-choice assessment that prompted them to do many addition and subtraction questions of vectors in different alignments. Using ego network analysis on this multiple-choice data, in conjunction with handwritten versions of the same questions, we investigate the intersection of different student mistakes. Results of this analysis will be discussed, as well as implications for teaching.