Projects

Mentors: Newman and Wright

The field of Molecular Biology seeks to uncover and understand the underlying mechanisms that govern gene expression, cellular communication and the flow of genetic information. Since concepts and processes of Molecular Biology are not directly observable, experts and learners must rely on visual representations (e.g., graphs, illustrations, diagrams) to communicate, explore and test ideas in this domain. Ongoing work by Newman and Wright investigates how individuals choose to represent their knowledge of molecular biology phenomena through drawings of their own and whether learners are able to find meaningful connections between multiple visual representations of the same phenomena such as gene expression or mutation. This project would build on a new framework that describes DNA-based representations on metrics of scale and abstraction to explore how learners decipher and explain concepts embedded within visualizations. In addition to discovering more about the development of visual literacy skills, future research findings may lead to the development of new research-backed activities and novel assessment tools to improve student learning in molecular biology.

Research questions related to these projects include, but are not limited to:

• How do learners navigate the visual landscape of DNA representations to learn core ideas in Molecular Biology and related fields?

• What strategies effectively promote the development of visual literacy skills in Molecular Biology and related fields?

Sample publications:

https://www.lifescied.org/doi/10.1187/cbe.22-01-0007

https://online.ucpress.edu/abt/article-abstract/82/5/296/110282/Undergraduate-Textbook-Representations-of-Meiosis

https://www.lifescied.org/doi/10.1187/cbe.17-04-0069

https://www.lifescied.org/doi/10.1187/cbe.cbe-13-09-0188

Mentors: Zwickl and Franklin

The career options of a physics major are broad, and students often develop varying levels of interest in particular subfields or methods (computation, experiment or theory). However, there is little research on where and how these interests and subsequent decisions are formed. Using Social Cognitive Career Theory along with constructs of sense-of-belonging and identity, this project would explore the factors within and outside of college that lead students to particular decisions and interests. This project would develop pre-post assessment tools that could be used in physics departments to investigate how learning experiences shape students’ self-efficacy (belief in their ability to succeed), outcome expectations (what a future career doing this might be like), interests, and future decisions (e.g., seeking electives, an internship or an REU). Once the assessments are ready for broader dissemination, studies involving comparisons by gender, race/ethnicity, and first generation college student status could be conducted. The goal would be to identify ways to improve representation of diverse students, to reduce negative experiences, and present students with more positive options. A second topic within the theme of career preparation involves preparation for advanced STEM careers, particularly in the emerging field of Quantum Information Science (QIS). The theme broadly supports the goals of the National Quantum Initiative to build a “quantum smart” workforce. Zwickl has worked on defining critical skills for the quantum workforce and led the development of a minor in Quantum Information Science and Technology, which is targeting students from over a dozen STEM majors in engineering, science, and computing. A fundamental question about such programs is how students from diverse disciplinary backgrounds engage with concepts from quantum computing and quantum technology. This study would seek to identify productive aspects of students’ prior knowledge, uncover common difficulties and guide the improvement of curricular materials, and develop targeted assessments for QIS. Although there is prior work on students’ understanding of quantum mechanics, only the most recent work has started to consider modern developments in QIS.

Research questions related to these projects include, but are not limited to:

• How do students develop interests and make decisions that lead to their specialization in a particular subfield (e.g., astrophysics, biophysics), particular methods (experimental, computational, or theoretical), and post-BS career plans (grad school or job)?

• How do students from diverse disciplinary backgrounds learn and apply concepts in quantum information science?

Sample publications:
https://iopscience.iop.org/article/10.1088/2058-9565/abfa64

https://journals.aps.org/prper/abstract/10.1103/PhysRevPhysEducRes.16.020131

Mentors: Wong and Zwickl

As computing plays an increasing role throughout professional work in math and science, it is important to promote and enhance students’ computational literacy to minimize any skills gaps between college and careers. The idea of computation as a form of literacy builds upon the extensive work on coding literacy and the prominent framework of computational thinking, which describes one’s ability to approach and solve problems by thinking algorithmically. Computational literacy, however, extends on this by including disciplinary knowledge for solving a task at hand, knowledge and skills for implementing computational methods for problem-solving, and knowledge and skills to communicate solutions—and solution processes—to relevant audiences. As with traditional forms of literacy, social groups develop their own distinct literacies to fulfill the needs of their social niches. The various scientific disciplines thus have developed their own forms of computational literacy to fulfill the unique computational needs within those disciplines. However, the specific forms of these disciplinary computational literacies remain poorly defined and there is a lack of formal assessment tools to examine students’ development of computational literacy. Ongoing work by Wong and Zwickl is building a framework to characterize disciplinary computational literacies across various science majors. A postdoctoral scholar engaged in this work could take early ownership of this work. Activities would include developing interview and survey protocols to generate characterizations of disciplinary computational literacy based on emergent themes from participant responses. By classifying elements of disciplinary computational literacy along the three pillars of Material, Cognitive, and Social literacy and the axis of Practices, Knowledge and Beliefs, this work will generate computational literacy profiles for different scientific disciplines. In this way, this project will produce a framework to assess similarities and differences in computational literacy within and between disciplines.

Research questions related to this work can include, but are not limited to:

• How can we best design assessments of students’ computational literacy, and how do these differ across scientific disciplines?

• What are the similarities and differences of disciplinary computational literacy across the sciences, and how can undergraduate STEM curriculum be structured to enhance this intersectionality? This question can be tailored to a postdoctoral scholar’s disciplinary background, and can focus on using computational literacy as a lens for improving teaching specific disciplinary topics.

Mentors: Wong and Franklin

Traditional measures of student success include first-year retention rates, percentages of students receiving non-passing grades or withdrawing from a particular course, and overall graduation rates. These metrics are used to assess the impacts of teaching innovations and program interventions, such as the introduction of remedial and/or bridge courses or the use of undergraduate Learning Assistants. Traditional measures, however, can be limited in their ability to resolve differences between relatively small cohorts of students; for example, underrepresented minority students or first-generation college students. Additionally, traditional analyses of 4-year or 6-year retention rates (for example) can only include information from a particular cohort of students after that cohort has progressed through 4 or 6 years in their programs (respectively). Ongoing work by Franklin and Wong uses a large longitudinal student grade data set to expand on previous studies to incorporate into retention rate estimates data from student cohorts who are still in progress for on-time graduation. This data set is large (over 90,000 unique students), so it provides an ample testing ground for machine learning and data scientific forms of analysis. Stochastic methods, such as constructing a first-order Markov chain, lend themselves well to these analyses by decomposing the overall retention rate into a chain of subsequent year-to-year transition probabilities. Thus, even a cohort that has only had two years (for example) can still be used to estimate the rate at which students transition from their freshman to sophomore year, or transition to a “drop out” state. These methods also partially address the issue of the dearth of data for small subsets of students because they make available additional data from cohorts whose data would be missed in traditional analyses.

Research questions related to this work can include, but are not limited to:

• How can qualitative elements of student experience, including sense of belonging or identity, be integrated with quantitative analyses of retention and persistence?

• Which machine learning methods are best suited for assessing the impacts of educational interventions, in particular for small cohorts, and how do data requirements differ across methods?

Mentors: Franklin and Newman

As of 2020, almost every higher education institution in the United States has put forth some effort towards diversity and inclusion policies and initiatives. However, an increase in policies and initiatives has not necessarily led to a more inclusive environment, particularly for students of color within STEM disciplines. While research thus far has focused heavily on activist identity development and/or the development and incorporation of anti-racist materials within the classroom, little has explored the processes by which instructors become committed to racial (and other) justice and how that commitment impacts their curriculum or manifests within the classroom. Additionally, creating environments that are welcoming, inclusive and that maximize learning requires understanding the different interpretations and experiences that a diverse population have, i.e. developing empathy.

This work explores the critical link between programmatics at the institutional level, individual faculty mindset and impact on students. Understanding and addressing an instructor's abilities to notice racist practices, understand how these impact studnets, and then tincorporate anti-racism content is critical disrupting the notion that STEM exists outside of social context. Results also have implications for classroom instruction, especially when involving concepts that can further cement problematic framings (e.g., phenotype differences and race as a social construct, or the origin of sickle cell anemia).

Research questions related to these projects include, but are not limited to:

• How is empathy perceived as a value across STEM disciplines?

• How does sensitivity and empathy manifest in STEM classrooms, laboratories and public spaces?

• What is the process by which STEM instructors develop a commitment to racial justice?

• In what ways does that commitment impact their inclusive pedagogy and practices within their STEM classroom?

Sample publications:
https://www.ingentaconnect.com/contentone/magna/jfd/2021/00000035/00000002/art00006
https://pubmed.ncbi.nlm.nih.gov/29749843/