A Specification Language for Matching Mistake Patterns with Feedback
Jesse Harden, Luke Gusukuma, Austin Cory Bart, Dennis Kafura · 2021
Pattern-based feedback detects incorrect code patterns in students' programs and provides feedback that can be personalized to the details of the matched code. Currently, a high level of instructor effort is required because the pattern detection must be expressed using complex programmatic interfaces. A specification language for pattern-based feedback is presented that mitigates this cost. Examples from actual student code illustrate the language's design and expressiveness. The language's implementation and testing is briefly described. Reflections are given on the the language design, where it is effectively used, and lessons learned from experience with its use. While our implementation is targeted at Python, other programming languages could be targeted using a similar approach.