Daniel M. Mejia, Ph.D.

Teaching philosophy

The greatest impact a teacher can have is not only to teach the student, but to build the student’s own capacity to succeed. My commitment began in a single class session covering for my advisor as a graduate student—the joy of watching students learn from words I spoke—and that is when I knew teaching, not industry, was where I wanted to build a career. Many of my students come from backgrounds and a culture similar to my own. I know first-hand what it is like to enter a first computer science class knowing nothing about the field, and to still feel there is more to learn upon completing a graduate degree. When I look at my students, I often see a version of myself.

I do not define student success by grades alone. For one student, success is understanding a difficult topic for the first time; for another, an internship, a research paper, a scholarship, graduation, graduate school, or a role where they are genuinely happy. My role is to meet students where they are and help them grow from that point, even when they do not yet see their own potential. CS 2302, Data Structures, is both the course I most enjoy teaching and the course I find hardest to teach well: it often determines whether a student stays in the major, and students arrive with widely different preparation. Closing that gap within a single semester, without losing those who arrive furthest behind, is where meeting students where they are becomes daily practice.

Two components sit at the core of how I teach: engagement and humanity. Engagement through activities, delivery, and sustained interaction keeps students curious. Humanity means letting students see that I am still learning, making mistakes, and growing alongside them. Students learn through different modalities—visuals, problem solving, storytelling, live coding, reflection—and I weave several into a session so no single group is left behind. Practice is critical; the struggle of difficult material is real and necessary. My role is not to remove that struggle, but to make sure no student faces it alone, and that every student who leaves feels prepared for what comes next.

In class I use a see-one, do-one, teach-one progression: a worked example with common pitfalls, then practice, then—in select units—students teaching the idea back. I emphasize the why behind a concept, not only the how, so students can adapt when the problem changes. Every lecture is recorded to Blackboard alongside walkthrough videos, a practice I kept after student feedback. With generative AI, I ask students to identify the problem, pinpoint where they are stuck, and attempt improvement on their own before prompting a model—mastery first, then AI as a tool used ethically, not a substitute. Closing the classroom–industry gap drives Applied Agile Software Engineering and partnerships with Google and VISA.

Philosophy

My research exists because our department, like many computer science departments, has at times taught agile software engineering using methods that no longer reflect how the industry actually works. It is the same gap I hear from former students returning from internships: how much they had to learn on the job that could have been taught, and taught well, in the classroom. Understanding how to teach industry-standard practices as a core part of preparing students for professional roles—not as an afterthought—is critical.

Teaching and research inform each other continuously. That is most visible with generative AI: what I observe students doing with these tools shapes the questions I ask in research, and what the research finds shapes how I teach the next semester. Classroom methodologies are formalized through publications at SIGCSE and ITiCSE so they can be examined and adopted more broadly—two competitively reviewed papers (ITiCSE 2025, SIGCSE 2025) and a peer-reviewed poster (ITiCSE 2026). Through CAHSI, those techniques extend across the network and the CAHSI-HACU AI Readiness Consortium; partnerships with Google and VISA keep a direct line between academia and industry.

Approach

My leadership grew out of a straightforward instinct: when something needs work, I want to fix it, and I want it to run efficiently once fixed. That instinct took me from the classroom into Program Director of the M.S. in Software Engineering, CAHSI Lead for the GenAI in CS Education Consortium, Education, Workforce & Professional Development Lead for the Institute for Applied AI Innovation, and related roles. Each serves the same students I have taught for years, aimed at giving twenty-first-century students genuine access to an excellent computer science education—not just a seat in a classroom.

Standard-setting, role modeling, and direct communication define how I lead. Where it makes sense, I work through initiatives with the people involved; where it does not, I delegate and trust those I have prepared. I build structures that outlast a single semester—a TA hiring pipeline, an attendance-tracking tool, a graduation-tracking system—so the work keeps running when I am not the one holding it. Sometimes leadership is simply showing up: when a faculty shortage left CS 1301 without enough instructors, I took on sole responsibility rather than let the department’s largest gateway course go understaffed.

My filter for new commitments is the impact of the work against what I can realistically contribute given existing responsibilities. That filter led me to the MSSwE directorship: proof that I can build and lead a program at the departmental level, and a foundation for the same kind of leadership at the college, university, and national level.