My teaching practice centers on two closely connected areas: supporting learners through guided engagement and using assessment and feedback as tools for learning rather than solely evaluation. In practice, this involves working closely with students during office hours, mentoring research and project teams, and designing assessment practices that make expectations transparent while helping students refine their reasoning over time.
I support and guide learners by providing structured and responsive assistance both inside and outside the classroom. I hold weekly office hours and additional sessions before exams to address conceptual challenges and problem-solving strategies, and I often provide one-on-one support when students request additional clarification. During these interactions, I adapt explanations based on students’ questions and levels of confidence, helping them work through misconceptions at their own pace (V1, K1).
I also support student learning through project-based guidance. For example, I meet with student groups working on a cardiovascular engineering design project where they explore how different structural modifications to a modeled blood vessel affect flow behavior. In these meetings, I help students reason through modeling decisions and computational assumptions, and I run simulations that allow them to evaluate the impact of their design choices. By reviewing these results together, students learn how computational evidence can inform engineering decisions (K4). Toward the end of the project, I also assist students in producing clear visualizations of their simulation results using visualization software and immersive tools such as zSpace, Vision Pro, and Sony ELF. These activities help students translate abstract computational outputs into interpretable visual representations.
Beyond formal course settings, I mentor undergraduate and junior lab members by introducing them to computational workflows, simulation pipelines, and core flow concepts used in a computational research environment. I break complex tasks into manageable steps, conduct regular check-ins, and adjust guidance as students gain independence (V2, K1). Through these practices, I aim to reduce barriers to learning in technically demanding contexts and support sustained engagement with quantitative and computational tools (V2, K3).
I contribute to assessment and feedback practices that emphasize clarity, fairness, and learning support. I grade journal article summaries, bi-weekly quizzes, and midterm and final exams. For high-stakes assessments, grading responsibilities are shared with the course instructor, and grading criteria are discussed in advance to ensure consistency across graders (V5).
I also collaborate with the instructor to maintain updated grading rubrics and review exam questions prior to administration to ensure that expectations are clearly communicated to students. During grading, I focus on identifying common conceptual errors and providing feedback that helps students understand not only what was incorrect but how they can improve their reasoning (K3). For example, when grading journal article summaries, I often comment on how effectively students connect experimental results to underlying transport concepts, highlighting where arguments can be strengthened or where key assumptions need to be clarified.
When patterns of misunderstanding appear across multiple submissions, I use those observations to inform follow-up explanations during review sessions or office hours. For instance, when students struggle with translating physical transport processes into mathematical expressions, I revisit those steps during exam review sessions to reinforce the conceptual framework behind the equations. In addition, when using digital grading platforms, I provide structured rubric-based comments so that students can clearly see how their work aligns with assessment criteria and how they can prepare more effectively for future exams (K4).
Integrate & Reflect
Across these areas of activity, my teaching practice centers on structured guidance and clarity in technically demanding contexts. Supporting learners (A4) and designing transparent assessment practices (A3) are mutually reinforcing: the same principles that shape how I respond to student questions during office hours also shape how I design and apply rubrics. For example, when recurring misconceptions emerge during office hours, such as difficulties translating physical transport processes into mathematical expressions, I often revisit those points while grading and adjust feedback comments or rubric explanations to clarify the underlying conceptual expectations. In this way, interactions with students directly inform how I frame assessment criteria and feedback.
Through mentoring and grading experiences, I have become more attentive to how students interpret expectations and where breakdowns in understanding typically occur. For instance, when students struggle with interpreting computational simulation outputs, I have learned to focus feedback not only on correctness but also on helping them articulate the reasoning that connects simulation results to engineering decisions. These experiences have strengthened my commitment to aligning instructional support with assessment clarity so that students not only complete tasks but understand how to refine their thinking and improve over time.
Looking Ahead
As I continue developing as an instructor, I aim to deepen my use of structured formative assessment and evidence-based instructional strategies. In particular, I am interested in expanding opportunities for active learning within technically demanding courses, such as incorporating short conceptual problem-solving activities during lectures or structured peer discussions that allow students to test their understanding before formal assessments.
I also plan to further integrate computational tools into classroom learning in ways that remain accessible to students with diverse quantitative backgrounds. Building on my experience supporting student projects that use simulations and visualization tools, I hope to design learning activities where computational models become exploratory tools rather than purely analytical outputs. More broadly, I view teaching development as an iterative and collaborative process, and I plan to continue engaging in peer observation, feedback, and reflective practice to refine both my instructional design and assessment strategies.