Research

I study the design of sociotechnical systems to support novice computing students’ self-efficacy, motivation, and learning. I explore:

how to support students in making more accurate evaluations of their programming abilities through both AI-based tools and teaching practices in university-level computing classes
how students collaboratively learn and how to facilitate social learning through technology

In my work, I leverage theories and methods from computing education, human-computer interaction, design research, and the learning sciences.

Peer-reviewed papers and posters

Choosing Their Own Way: Guided Self-Placement for Students in an Introductory Programming Sequence

Brett Wortzman, Melissa Chen, Miya Natsuhara, Eleanor O'Rourke

SIGCSE TS 2026 | to appear

Navigating Appropriate Help-seeking with LLMs: Associations with Motivation and Beliefs in a CS course

Stephanie Yang, Melissa Chen, Bertrand Schneider

Koli Calling 2025 | to appear

Exploring Students’ Perceptions of Contextualized Computing in an Introductory Computing Science Course for Non-majors

Yinmiao Li, Melissa Chen, Ayse Hunt, Michael Horn, Eleanor O'Rourke

The Interrelated Nature of Belonging and Self-Assessments in Computing Students’ Decisions to Persist

Kristin Fasiang, Melissa Chen, Eleanor O'Rourke

Understanding the Reasoning Behind Students’ Self-Assessments of Ability in Introductory Computer Science Courses

Melissa Chen, Yinmiao Li, Eleanor O'Rourke

Best Paper Award (top 1 paper / 36)

Exploring the Interplay of Metacognition, Affect, and Behaviors in an Introductory Computer Science Course for Non-Majors

Yinmiao Li, Melissa Chen, Ayse Hunt, Haoqi Zhang, Eleanor O'Rourke

Designing to Support Accurate Self-Assessments of Programming Ability

Melissa Chen

Designing a Real-Time Intervention to Address Negative Self-Assessments While Programming

Melissa Chen, Eleanor O'Rourke

ICER 2023 | DOI | Poster PDF