Evidence record 3202 · automatically gathered

Leveraging BART to Assess CS1 C++ Programming Assignments using Rubric-based Criteria

This paper investigates rubric-aware, multitask fine-tuning of transformer models for automated grading of introductory C++ programming assignments, with the goal of producing grade predictions that better reflect instructor grading behavior than general-purpose LLMs. Using multi-semester CS1 data, student submissions are paired with numeric scores, letter-grade buckets, and assignment rubrics, then preprocessed into unified sequences for transformer input. A BART encoder-decoder with LoRA adapt

Record details

Published: 2 June 2026
Source: arXiv
Category: Research
Topics: Children & education · Finance, VC & PE
Retrieved: 14 July 2026

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ethics.ai (2 June 2026), “Leveraging BART to Assess CS1 C++ Programming Assignments using Rubric-based Criteria,” evidence record 3202, https://ethics.ai/record/3202 (originally published by arXiv).

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