A Dataset for Modeling Iterative Problem-Solving
Solving problems through repeated attempts is a sequential modeling task: at each step, the solver receives feedback and decides how to revise their solutions. Predicting whether performance improves, plateaus, or regresses across attempts is central to understanding any iterative problem-solving process in both human learners and autonomous agents. Beyond outcomes, modeling what errors persist and how strategies shift across attempts provides deeper insight into the mechanics of sequential learning. Studying these dynamics requires observing many solvers as they attempt, receive feedback, and revise. Programming courses with automated grading provide this setting, as students iteratively...
| Authors | Fagun Patel, Sang T. Truong, Duc Q. Nguyen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 September 2026 |
| Last updated at source | 7 October 2026 |
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