Research Projects
Below are some of the specific projects that make up PERL’s research areas. Where a project has produced open-source software, it is listed alongside the project. All PERL software is available on GitHub under a Creative Commons Attribution-NonCommercial 4.0 license.
TODO: review these project descriptions, add team members and funding for each, and add any current projects that are missing.
Current Projects
Integrating computation into undergraduate physics
Folks involved: TODO
PERL studies how computation is taught in physics courses and how faculty come to integrate it into their teaching. This includes a national survey of physics faculty that used machine learning to identify the factors most predictive of whether faculty include computation in their courses (Young et al. 2019). PERL members also lead the Partnership for Integration of Computation into Undergraduate Physics (PICUP), which was recently awarded an NSF IUSE grant to build a national community of computational physics educators, expand its resource collections, and develop assessment tasks for computational physics learning goals.
Software:
- ComputationSurveyAnalysis is a set of Python notebooks for analyzing a national survey of physics faculty using the Random Forest algorithm.
- PhysUtil is an open-source module that can generate motion diagrams (stroboscopic images), graphs, and timers from within VPython or GlowScript programs.
Projects and Practices in Physics (P-Cubed)
Folks involved: TODO
P-Cubed is an introductory physics course in which students work in groups on problems that include computational modeling. It was designed using communities of practice as a guiding framework (Irving, McPadden, & Caballero 2020). PERL researchers study how students take part in the course’s group work, how learning assistants approach its computational problems, and how formative feedback supports students. See Curriculum Development for the course materials.
Software:
- WebCAT is a web-based formative assessment tool that gives students feedback on their development in courses that use group work.
Student pathways through STEM degree programs
Folks involved: TODO
Using institutional data and machine learning, PERL studies the paths students take toward their degrees. Examples include modeling student pathways in the physics bachelor’s program (Aiken, Henderson, & Caballero 2019), predicting time to graduation (Aiken et al. 2020), and studying the degree pathways of transfer students.
Software:
- Pathways is a set of Python notebooks for analyzing registration data and determining the paths students take through the college towards their degree.
TODO: Project title
Folks involved: TODO
TODO: Project description, and funding acknowledgment.
Past Projects
TODO: Move completed projects here.