I am a design-based researcher who leverages qualitative research methods to support students’ motivation, self-efficacy, and self-assessments. Specifically, I aim to understand how we can design systems to support students in making more accurate (and less self-critical) self-assessments of their programming abilities in moments that are common in the programming process (like looking up syntax or stopping to think).
I’m currently:
- Designing an AI-driven, IDE-based real-time feedback system to support students in making more accurate self-assessments of their programming abilities while coding
- Co-designing with intro CS instructors to support support accurate self-assessments and problem solving strategy usage
- Investigating how students’ learning environments and interactions with their peers relate to their self-assessments
In my work, I leverage theories and methods from computing education, human-computer interaction, design research, and the learning sciences. I also leverage some technical skills from my undergrad work as a software engineer intern and machine learning research intern.
Papers
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, Eleanor O'Rourke
Posters
Designing a Real-Time Intervention to Address Negative Self-Assessments While Programming
Melissa Chen, Eleanor O'Rourke
ICER 2023 | DOI | PDF | Poster PDF