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 they think they are doing badly and how can we design to support them?
Some past work includes:
- Designing an AI-based, in-IDE intervention to help normalize these programming moments while students code
- Understanding the reasoning that students use for their negative self-assessments
My current work builds on this to explore:
- Factors related to students usage of particular reasons and sources
- How we co-design interventions with intro CS instructors to support accurate 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 time in software engineering and machine learning research. I am primarily a qualitative researcher, but also have familiarity with quantitative methods from previous experiences.
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