The ALMA (meaing soul in spanish) focuses on understanding how students experience learning in STEM, particularly through reflective writing. Using qualitative analysis and machine learning tools, ALMA examines how students express identity, motivation, and cultural capital in coursework. The lab’s work supports more inclusive teaching practices by making visible the strengths and experiences that students from marginalized backgrounds bring into STEM classrooms. This research is run by Dr. Kim Coble at SFSU Physics and astronomy education research (kcoble@sfsu.edu).
In Summer 2025, I presented my research with ALMA at the American Astronomical Society conference in Anchorage, Alaska. My presentation focused on the use of human validation alongside machine learning algorithms to analyze reflective writing from introductory physics and astronomy students. Specifically, the project explored how computational methods along with human validation can help identify students’ cultural capitals—such as resilience, community support, and prior experiences—that are often overlooked in traditional STEM assessment.
My role involved validating AI-generated codes against human qualitative analysis to ensure accuracy, nuance, and ethical use of machine learning in educational research. This work highlights the importance of combining human insight with computational tools when studying student identity and learning in STEM.
Alongside my own presentation, I saw a poster on accessible tactile astronomy education that used 3D printing to create physical models of galaxies and planets for learners with visual impairments. Although this was not my research, seeing how 3D-printed forms transformed abstract astronomical concepts into tangible learning tools was deeply impactful. The work demonstrated how accessibility in astronomy is not an add-on, but a design choice that can fundamentally reshape who gets to participate in science.
This presentation also felt personally meaningful. My interest in engineering began in eighth grade, when I first learned about 3D printing—at a time when the technology was far less accessible than it is today. Since then, rapid prototyping has shaped how I approach problem-solving, accessibility, and design. I hope to continue working in spaces where 3D printing and rapid prototyping are used to expand access, whether in education, assistive technology, or community-centered engineering.
Traveling to Alaska for this conference was an unforgettable experience. Being surrounded by vast landscapes, mountains, and wildlife reinforced how deeply science, environment, and human experience are connected. It was especially meaningful to share interdisciplinary research in a setting that emphasized both scientific discovery and respect for place.
Although I am a mechanical engineering student, this experience reaffirmed how essential cross-disciplinary work is to meaningful research. Collaborating across astronomy, physics education, machine learning, and social science has shaped how I approach engineering—not just as technical problem-solving, but as a human-centered practice grounded in equity, context, and collaboration.
Accepted NARST abstract for 2026
My current research with ALMA focuses on the Resistance Capital Project, which examines how undergraduate students express resistance within STEM learning environments through reflective writing. Using the Four I’s of Resistance framework (individual, interpersonal, institutional, and ideological), this work analyzes how students name barriers, navigate systems of oppression, and articulate aspirations for change. I have contributed to the development and analysis of this project through qualitative coding, theoretical refinement, and collaborative interpretation of findings. A related paper has been accepted for presentation at NARST, and I will be adding the full manuscript to this page as the project continues to develop.
The Community Engaged Engineering Design (CEED) Lab was founded in January 2025 by Dr. Kenya Z. Mejia (kzmejia@sfsu.edu) in the College of Engineering at San Francisco State University. CEED focuses on building more inclusive engineering environments by centering community knowledge, lived experience, and access. The lab’s work currently emphasizes two areas: family–student engagement workshops that support engineering identity development, and efforts to make the engineering machine shop more accessible and inclusive for all students.
As part of the CEED Lab, I contributed to a Work-in-Progress research paper that was submitted to and accepted by the American Society for Engineering Education (ASEE) for the Minorities in Engineering (MIND) Division. This project examines how family involvement can influence engineering identity, belonging, and persistence for students from historically marginalized backgrounds. The paper reflects CEED’s broader commitment to community-engaged research that challenges traditional, individual-centered models of engineering education.
In October 2025, our team successfully facilitated a pilot family–student engineering workshop with three student–family groups. The workshop used littleBits kits to introduce basic circuit concepts in an accessible, hands-on way. Families were invited to actively build alongside students, positioning them as engineers and supporters rather than observers. This experience helped families recognize that engineering is not only for professionals, but something they can understand, engage with, and belong to—reinforcing students’ confidence and sense of support.
While there is existing research on the importance of social networks—such as family and friends—for first-generation and marginalized students, and separate work focused on outreach and workshops for K–12 students, very little research brings these two approaches together. CEED’s work sits at this intersection, centering families as active participants in students’ engineering journeys rather than distant supporters. Although this kind of research is not always recognized as “traditional” engineering work, it is deeply necessary. Designing machines without considering ethics, access, and the people they serve limits their impact—because technology without purpose or accessibility ultimately fails the communities it claims to support.