This biology education research project has two parts: the first 4-week part will involve creating an organized, accessible, digital library of shared pedagogical resources for introductory biology courses. Informed by the current literature on best practices in critical conscious- and evidence-based pedagogies, we will develop, assemble, and curate resources for these courses.

The second 4-week part will involve conducting novel research investigating how students learn (and don’t learn!) fundamental microbiology concepts, with the goal of developing and testing the efficacy of new microbiology teaching tools.

Both parts of this project are likely to include: scientific literature reading and review, creating searchable databases, creating and working with Excel spreadsheets, creating classroom worksheets, and assembling digital resources.

• Focus Areas: Biology
• Project Duration: 8 weeks (June 7-July 30)
• Prerequisite Courses: none
• Preferred Courses: BIOL 247: Biometrics
• Number of Student Positions: 1

Principal Investigator

During the 2020-21 academic year, PHYS 101 and 102 were taught in an online format. Zoom sessions were recorded and saved for student reference. We propose to review this archive of material, and extract appropriate portions for future use: mini-lectures, solved problems, lab introductions, etc. These could be assigned viewing or simply supplementary material.

In addition, we will revise and improve video-analysis and at-home labs that were created for this semester. These labs might be used as part of the regular courses in the future, or serve as make-up labs for students who are unable to attend in-person labs.

• Project Duration: 4 weeks (June 7-July 2)
• Prerequisite Courses: PHYS 101: General Physics 1
• Preferred Courses: PHYS 102: General Physics 2
• Number of Student Positions: 1

Principal Investigator

In academic year 2020-21, MATH 110 was taught in both hybrid and online formats. We propose to examine and expand course materials and course structure in order to identify elements which will contribute to increased student learning, engagement, and sense of belonging even after we return to an in-person, semester-long format in these courses.

In particular, we seek to establish frameworks of universal design for learning by setting best practices for recording and making class sessions and notes available to students outside of the classroom, providing individualized attention to students via Discord, using Desmos group activities and MATLAB labs to increase engagement and build practical skills, assessing learning via standards-based grading of Moodle quizzes, exams, and online homework assignments, incorporating virtual visits from professionals to serve career-readiness, and using other technological solutions which have been effective in an online format in these courses.

Central to our work will be continual mindfulness of the ways in which white supremacist structures in education can lead to exclusion of students from underrepresented groups from mathematics.

• Project Duration: 4 weeks (June 7-July 2)
• Prerequisite Courses: MATH 110: Calculus 1
• Preferred Courses: MATH 115: Calculus II and familiarity with Zoom and Discord.Experience with MATLAB or other programming experience would be especially helpful.
• Number of Student Positions: 1

Principal Investigator

This project will address two questions: 1) what is the relationship between osteological lesions of the skeleton and the presence of tuberculosis DNA, and 2) what are the strongest osteological indicators that would suggest a person had been infected with tuberculosis. Due to the small sample size inherent in bioarchaeological research, performing a meta-analysis would help provide a more complete picture of the relationship between osteological lesions and pathogenic DNA. We will employ Bayesian statistics, using the program R, to determine the prior proportion of individuals who had tuberculosis DNA present in their skeletal tissue from archaeological sources and will then use current data to update the prior.

• Focus Areas: Biology
• Project Duration: 8 weeks (June 7-July 30)
• Prerequisite Courses: BIOL 247: Biometrics
• Preferred Courses: BIOL 289: Genetics; BIOL 208: Microbiology
• Number of Student Positions: 1

Principal Investigator

One of the leading hypotheses in amphibian declines proposes that amphibians exposed to environmental pollutants are immunosuppressed, and therefore, more susceptible to disease pathogens. Antimicrobial skin peptides (AMPs) are an important first line of defense in protecting frogs against viruses, fungi, and bacteria. Previously, our lab found lowered AMP levels in native green frogs located in ponds adjacent to agricultural fields. This site had high levels of nitrates, a primary component of fertilizers applied to agricultural fields, but a subsequent laboratory study investigated the effect of nitrates and phosphates on AMP secretion, and found no effect of nitrate exposure. To continue this investigation, a student will collect water samples in the Avon Bottoms Natural Area to test for the presence of chemical pesticides and other environmental pollutants to determine what additional pollutants might be impacting these frogs. Samples will be taken throughout the summer from numerous sites previously identified as habitat for native frogs. The student on this project will have a strong background in chemistry and interest in developing a protocol to best analyze these water samples for commonly used pesticides (e.g., glyphosate, atrazine, 2-4 D).

• Project Duration: 8 weeks (June 7- July 30)
• Prerequisite Courses: CHEM 117: Chemistry I AND CHEM 230: Organic Chemistry I
• Preferred Courses: CHEM 235: Organic Chemistry II, OR CHEM 220: Environmental, Analytical, Geochemistry OR CHEM 225: Topics in Instrumental Analysis
• Number of Student Positions: 1

Principal Investigator

In this project, we will use several techniques in machine learning and data mining in order to detect Hepatitis, fibrosis, and cirrhosis. We will use a database set of 615 instances that includes important laboratory and demographic values like age. The data are collected from blood donors and Hepatitis C patients. Full data description is available at https://archive.ics.uci.edu/ml/datasets/HCV+data.

The research includes:

1. Developing classifiers for predicting in order to detect Hepatitis, fibrosis, and cirrhosis using decision trees, support vector machines, neural networks, Bayesian classifies, and/ or some other data mining and machine learning techniques.
2. Evaluating their performance, and comparing their accuracy.
3. Employing Python as a primary programming language in the development process.

• Focus Areas: Computer Science
• Project Duration: 8 weeks (June 7-July 30)
• Prerequisite Courses: CSCI 204: Data Structure and Algorithms Programming using Python
• Preferred Courses: CSCI 370: Data Mining
• Number of Student Positions: 1

Principal Investigator