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New dataset models iterative problem-solving with student code submissions

By PulseAugur Editorial · [1 sources] ·

Researchers have developed CodeInsight, a new dataset comprising over 3 million submissions from 3,286 students in introductory C++ courses. This dataset is designed to model iterative problem-solving by tracking student attempts, feedback, and code revisions. A benchmark was created using this data, evaluating various models including a Recurrent State Space Model (RSSM) and a Large Language Model (LLM). The adapted RSSM demonstrated superior predictive accuracy, while the LLM, though less accurate, provided explicit solutions for analysis. AI

IMPACT Provides a new resource for developing and evaluating AI models that can understand and predict iterative learning processes.

RANK_REASON The cluster is about a new academic paper introducing a dataset and benchmark for modeling iterative problem-solving. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset models iterative problem-solving with student code submissions

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The cluster is about a new academic paper introducing a dataset and benchmark for modeling iterative problem-solving. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 Dansk(DA) · Fagun Patel, Sang T. Truong, Duc Q. Nguyen, Kazunori Fukuhara, Benjamin W. Domingue, Sanmi Koyejo, Nick Haber ·

    A Dataset for Modeling Iterative Problem-Solving

    arXiv:2609.00940v1 Announce Type: new Abstract: 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 a…

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