Feature Request: Conditional Scoring for Multi-Attempt Assessments
Many instructors are moving toward assessment models that combine:
an initial in-class attempt (to assess independent understanding), and
a follow-up attempt (to allow revision and learning)
This structure is becoming increasingly important as traditional at-home assessments become less reliable indicators of student understanding in the presence of AI tools.
Current limitation
Canvas treats all quiz attempts symmetrically and does not allow:
different weighting of attempts (e.g., Attempt 1 vs Attempt 2)
conditional scoring based on whether an earlier attempt was completed
caps on recovery (e.g., second attempt can improve score, but not to full credit)
differentiation between attempts taken during different time windows (in-class vs later)
As a result, instructors must choose between:
simplified grading models (e.g., averaging attempts), or
complex workarounds (multiple assignments, manual adjustments)
Requested capabilities
Support for flexible scoring rules such as:
Weighted attempts
Example: Attempt 1 = 70%, Attempt 2 = 30%
Conditional scoring based on participation
Example: If Attempt 1 is missing, maximum score is capped
Recovery with cap
Example: Later attempts can improve score, but only up to a defined percentage
Time-aware attempt logic
Example: Attempts during a defined window (class time) are treated differently than later attempts
Why this matters
These features would support widely used teaching practices such as:
in-class formative assessment with revision
mastery-based grading
incentivizing attendance while allowing recovery
maintaining rigor while reducing grading overhead
Currently, these workflows are difficult or impossible to implement cleanly in Canvas without manual intervention or structural workarounds.
Summary:
Instructors need the ability to define conditional, time-aware scoring across attempts in order to support modern, equitable, and AI-resilient assessment practices.
