Overview
Gradiance emerged from years of practice running oral competency interviews in computer science courses. The system is grounded in research on equitable assessment, the limitations of traditional testing, and the pedagogical value of explanation-as-learning.
The publications below represent work presented at peer-reviewed venues on these themes. Links go directly to the published proceedings.
This section will be updated with links to published proceedings, conference papers, and related work. Check back soon.
Research Themes
Grading for Equity
How oral assessment removes structural advantages that traditional testing gives to students with more time, resources, or test-taking experience.
Oral Assessment in CS Education
The case for explanation-based evaluation in technical disciplines — why talking about code reveals more than writing it under timed conditions.
Competency-Based Progression
Moving from point accumulation to demonstrated mastery — how competency frameworks change student behavior and learning outcomes.
Throughput and Fairness
The operational design behind first-come queues, daily attempt limits, and the tradeoffs between efficiency and access.
Related Work
The following topics connect to the research behind Gradiance. Publication links will be added as they become available.
- Oral examinations in undergraduate computer science
- Mastery-based grading systems
- Equitable assessment practices in STEM
- Student queue management and throughput optimization
- LLM-era academic integrity in CS courses
For Faculty
If you're a faculty member considering adopting Gradiance or the oral assessment model for your own courses, feel free to reach out. The system is designed to be adaptable across CS disciplines and course sizes.
The instructor onboarding checklist is a good starting point for understanding the workflow. The architecture and API documentation are available for technical integration questions.