My research focus is on understanding why students think they are doing poorly in common moments in the programming process, like stopping to think or looking something up on the internet. These are all things we do when we code, and certainly we expect novices to do it, so why do students think they are doing badly and how can we design to support them?
Some past work includes:
- Understanding the reasoning that students use for their negative self-assessments
My current work builds on this to explore:
- How to design an AI-based, in-IDE intervention to help normalize these programming moments while students code
- How to co-design interventions with intro CS instructors to support accurate self-assessments and problem solving strategy usage
- Factors related to how students reason about their self-assessments, such as their environment and their interactions with peers
I am lucky to collaborate with colleagues on other research interests, including:
- Understanding how students interact with generative AI tools when learning to program
- Understanding how debugging is learned and taught
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 time in software engineering and machine learning research. I am primarily a qualitative researcher, but continue to build skills in quantitative and mixed methods research.
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