arXiv subjects

Seth Copen Goldstein

Publications and source records attributed to Seth Copen Goldstein.

3 recordsLinked to original sources

arXiv2026

Personalized Assessments from Personal Artifacts

The rapid development and popularization of AI-enabled coding agents have meant software engineering students and professionals cannot be assumed to understand their own code, which risks academic integrity and professional accountability. We developed a method called Personalized Probing Puzzles ($p^3$) to evaluate students' understanding of their own code, and tested $p^3$ in a graduate-level cloud computing course. Our pilot study shows that $p^3$ can help identify potential gaps in students' understanding of their own code. The puzzles are automatically generated, asynchronously administered, and finished in minutes. Future work is needed to correlate puzzle results with code understanding and to embed $p^3$ in a professional code review process.

cs.SE↗

arXiv2026

Changes in Coding Behavior and Performance Since the Introduction of LLMs

The widespread availability of large language models (LLMs) has changed how students engage with coding and problem-solving. While these tools may increase student productivity, they also make it more difficult for instructors to assess students' learning and effort. In this quasi-longitudinal study, we analyze five years of student source code submissions in a graduate-level cloud computing course, focusing on an assignment that remained unchanged and examining students' behavior during the period spanning five semesters before the release of ChatGPT and five semesters after. Student coding behavior has changed significantly since Fall 2022. The length of their final submissions increased. Between consecutive submissions, average edit distances increased while average score improvement decreased, suggesting that both student productivity and learning have decreased after ChatGPT's release. Additionally, there are statistically significant correlations between these behavioral changes and their overall performance. Although we cannot definitively attribute them to LLM misuse, they are consistent with our hypothesis that some students are over-reliant on LLMs, which is negatively affecting their learning outcomes. Our findings raise an alarm around the first generation of graduates in the age of LLMs, calling upon both educators and employers to reflect on their evaluation methods for genuine expertise and productivity.

cs.SE↗

arXiv2014

A Linear Logic Programming Language for Concurrent Programming over Graph Structures

We have designed a new logic programming language called LM (Linear Meld) for programming graph-based algorithms in a declarative fashion. Our language is based on linear logic, an expressive logical system where logical facts can be consumed. Because LM integrates both classical and linear logic, LM tends to be more expressive than other logic programming languages. LM programs are naturally concurrent because facts are partitioned by nodes of a graph data structure. Computation is performed at the node level while communication happens between connected nodes. In this paper, we present the syntax and operational semantics of our language and illustrate its use through a number of examples.

cs.PL↗