Teaching
Overview
I have served as a Teaching Assistant across seven instructional appointments within the University of Maryland’s College of Computer, Mathematical, and Natural Sciences (CMNS), spanning introductory biology laboratories, upper-level ecology discussions, and graduate-level quantitative training. Across courses, my goal is consistent: to create a learning environment where students feel supported, capable, and empowered to ask questions.
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​Managed instructional delivery for over 350+ students, leading high-intensity 3-hour laboratory sessions (25 students per section) and large-scale graduate quantitative training (50-70 students in BIOL 705: Statistics & Modeling for Biologists).
Consistently recognized for fostering inclusive, high-engagement environments, achieving a 3.9/4.0 mean score (2025 Spring, Section 1100, 1101) with 88% of students selecting “Strongly Agree” for “The TA created an inclusive environment where I belonged,” during demanding 3-hour lab interactions. My teaching portfolio requires both technical mastery of experimental protocols and the ability to bridge complex computational frameworks with diverse student backgrounds.
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BSCI 103 The World of Biology Lab (2026 Spring) (Section 1100, 1101)
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BSCI 103 The World of Biology Lab (2025 Spring) (Section 1100, 1101)
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BSCI 161 Principles of Ecology and Evolution Lab (2024 Spring) (Section 6307, 6407)
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BSCI 161 Principles of Ecology and Evolution Lab (2023 Fall) (Section 6211, 6210)
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BIOL 705 Statistics & Modeling for Biologists (2023 Spring)
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BSCI 361 Principles of Ecology (2022 Winter)
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BSCI 361 Principles of Ecology (2022 Fall) (Section 0101, 0102, 0103)
Teaching Approach
My teaching is shaped by three core commitments:
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Clarity and structure. I design my own instructional slides and lab walk-throughs (rather than relying only on templates), and I break complex concepts into step-by-step reasoning students can follow and apply.
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Inclusive, student-centered instruction. I make expectations explicit and create a space where students can participate without fear of being “wrong,” which is especially important for first-generation and international students navigating unfamiliar academic norms.
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Intentional communication and accessibility. I supplement verbal explanations with written terminology and visual cues, and I regularly incorporate student feedback to improve how I teach and support diverse learning needs.
Selected Teaching Evidence (University Course Evaluations)
BSCI 103 (Spring 2025): Mean score 3.9 / 4.0 on “The TA created an inclusive environment where I belonged,” with 88% of respondents selecting “Strongly Agree.”
(Anonymous student evaluations; official university course feedback.)
Selected Student Feedback
“Our TA clearly cared about the lab section and was consistently engaged with helping us out…”
— Anonymous student feedback, BSCI 103 (Spring 2025)
“She was quick with responding emails on questions, and explained the exercises very well.”
— Anonymous student feedback, BSCI 103 (Spring 2025)
“Speaking along with the slides was very beneficial… Overall, she’s a great TA.”
— Anonymous student feedback, BSCI 103 (Spring 2025)
“My TA was very engaging and helpful in the lab… made lab go by smoothly by answering all of our questions!”
— Anonymous student feedback, BSCI 103 (Spring 2025)
“Qianru was the best TA I have ever had… very understanding, professional, and kind at all times.”
— Anonymous student feedback, BSCI 103 (Spring 2025)
“Qianru did an excellent job organizing concise and intuitive reviews of the journal articles… she helped break down the complicated statistical framework and concepts…”
— Anonymous student feedback, BSCI 361 (Fall 2022)
“Qianru’s powerpoints she made and shared were very helpful study materials… gave everyone equal opportunities to speak…”
— Anonymous student feedback, BSCI 361 (Fall 2022)
“Qianru was very good at explaining science papers that were very confusing… and was able to break them down so we all took away the key ideas.”
— Anonymous student feedback, BSCI 361 (Fall 2022)
Mentorship
Beyond formal teaching duties, I mentor undergraduate research assistants in spatial analysis, movement ecology, and reproducible research workflows, supporting students in building confidence with data-driven scientific thinking and independent problem-solving.
Notes on Documentation: Summaries and excerpts above are drawn from official University of Maryland course evaluation reports. Full reports are available upon request.