Implementing an undergraduate learning assistant model to foster engagement and professional development in microbiology courses

Abstract

Integrating undergraduate learning assistants (ULAs) into high-enrollment science, technology, engineering, and mathematics (STEM) courses has been shown to enhance student engagement, yet the impact on ULAs themselves remains underexplored. This manuscript presents a field-tested framework for implementing and sustaining a structured ULA program in a General Microbiology course, offering a replicable model for educators seeking to enhance student learning and ULA professional development (PD). Over the past decade, this program has been refined to provide structured mentorship, leadership opportunities, and active involvement in both in-class and out-of-class activities. The ULA program fosters student engagement by incorporating active learning strategies such as iClicker questions, group exams, and flipped classroom exercises, ensuring a collaborative and supportive learning environment. At the same time, ULAs develop critical skills in communication, teamwork, assessment, and pedagogy, which align with key PD objectives. Faculty observations indicate that students who engage with ULAs exhibit increased conceptual understanding, participation, and peer collaboration. Likewise, anonymous ULA feedback highlights significant growth in teaching confidence, leadership, and academic preparedness. This article serves as a practical guide for educators by outlining a structured, adaptable curriculum, detailing strategies for faculty mentoring, and providing implementation best practices. While formal assessment of learning gains is ongoing, preliminary findings suggest that ULAs serve as valuable instructional assets while simultaneously benefiting from the experience. This dual impact underscores the importance of structured ULA programs in enhancing undergraduate education and preparing future educators and professionals.

Keywords: STEM education; active learning; higher education; peer mentoring; professional development; undergraduate learning assistant (ULA).

Fig 1

(a) Weekly and semester-long time commitments for ULAs, including attending lectures (three sessions per week, 50 minutes each), co-leading weekly learning sessions (1 hour per week), and exam-related activities (2 hours of proctoring per exam and 3 hours of grading per exam with the instructor). Color coding: blue represents lecture-related activities, light beige represents weekly learning sessions, and gray represents exam-related responsibilities. (b) Schematic of ULAs seated with their assigned supergroups in the General Microbiology course. Supergroups are color-coded (using “name tents”) to represent structured seating arrangements, promoting familiarity and interaction among students, ULAs, and the instructor. (c) Weekly ULA class (Supervised College Teaching) with course instructors (Black). These meetings foster open communication, provide opportunities for ULAs to practice teaching activities, and strengthen their understanding of course material to stay ahead of the General Microbiology students.

Fig 2

(a) List of Likert-scale survey prompts assessing ULA experiences and perceptions of professional and academic development. The prompts evaluate how the ULA program has contributed to skill development, career preparation, subject matter understanding, and overall professional growth. (b) Heatmap visualization of response distributions across the Likert-scale categories (strongly disagree to strongly agree). Warmer colors (red) indicate higher agreement, while cooler colors (blue) represent lower agreement. Higher agreement is observed for statements related to professional growth, skill development, and career preparation, while lower agreement is seen for areas of potential improvement within the program. ChatGPT was used to assist in analyzing survey responses, organizing data, and generating the heatmap to efficiently visualize trends in participant feedback.

Fig 3

(a) Identified themes from ULA feedback, including Professional Development, Supportive Environment, Learning Experience, Community and Relationships, Suggestions for Improvement, and Overall Satisfaction. These themes highlight key aspects of the ULA experience, from skill development and mentorship to areas for program refinement. (b) Numerical scale was used to categorize feedback, ranging from strongly agree (5) to strongly disagree (1), with intermediate values indicating varying levels of agreement or critique. (c) Summary table displays the frequency of responses for each theme, their average score on a 0–5 scale, and an explanation of key takeaways. High scores in Professional Development, Supportive Environment, and Overall Satisfaction indicate strong positive feedback, while Suggestions for Improvement received a lower score, suggesting minor areas for refinement. (d) Each theme is accompanied by representative, ULA comments, highlighting the impact of the ULA program on student experiences, skill development, and areas for potential refinement. ChatGPT was utilized to assist in processing and organizing qualitative feedback, categorizing responses into themes, and summarizing numerical trends. This approach streamlined data interpretation and visualization, ensuring a clear representation of key insights.

Fig 4

General Microbiology Student responses to the question, “Which options helped improve your sense of community and belonging in [this course] this semester?” Data were collected anonymously from students over two semesters (Spring and Fall 2024) and represent the proportion and frequency of individual response options. Students could select multiple options, resulting in some overlap across categories. The chart displays percentages calculated based on the total number of valid responses (n = 403), with raw counts also included. The color scheme used in the visualization is optimized for colorblind accessibility to ensure clarity and inclusivity.