The paper presents a risk‑adaptive, evidence‑constrained framework that uses learning analytics to provide personalized feedback in introductory programming. By training models on 2,993 failed‑submission states from 215 students, the authors predict persistent failure and generate four tailored feedback conditions for 136 cases. A calibrated risk policy selects interventions for 17.8% of eligible states, capturing 25.2% of persistent failures, and the framework ensures that generated messages contain all required components after evidence gating.
The paper introduces CodeInsight, a large-scale dataset of over 3 million code submissions from 3,286 undergraduate students in two introductory C++ courses, capturing test‑case outcomes, timestamps, and source code. It presents a benchmark that evaluates various modeling approaches—including a Recurrent State Space Model and an LLM‑based predictor—on their ability to predict iterative problem‑solving dynamics such as performance changes and error persistence. The study finds that the RSSM outperforms other models on most courses, while the LLM generates full submissions but with lower predictive accuracy, suggesting it functions more as a generative solver than a behavior predictor.
By Fagun Patel, Sang T. Truong, Duc Q. Nguyen, Kazunori Fukuhara, Benjamin W. Domingue, Sanmi Koyejo, Nick Haber
arXiv:2606. 12425v1 Announce Type: cross Abstract: Active learning is widely recognized as an effective approach for improving learning outcomes in introductory programming courses.
By Muntasir Hoq, Griffin Pitts, Bradford Mott, Seung Lee, Jessica Vandenberg, Shuyin Jiao, Narges Norouzi, James Lester, Bita Akram
arXiv:2608. 12351v1 Announce Type: cross Abstract: Generative artificial intelligence (GenAI) has challenged the validity of unsupervised online assessment, especially in technical subjects where plausible answers can be produced with little effort.
By Riasat Islam (School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom), Thomas Roelleke (School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom)
arXiv:2606. 18617v1 Announce Type: cross Abstract: There exist numerous tutor training platforms.
By Danielle R. Thomas, Marie Cynthia Abijuru Kamikazi, Clara Brandt, Conrad Borchers, Kenneth R. Koedinger
arXiv:2609.39957v1 Announce Type: cross
Abstract: Coding agents solve repository-level tasks through sequences of actions, where a single erroneous action can misdirect subsequent decisions and incre...
By Jiangrui Zhao, Chenglong Li, Meng Zhang, Xiaoting Du