SelfCode2.0 dataset of student and expert line-by-line code explanations¶
Product or Programme · All products and programmes
Evidence · 2 claims (2 for) · 1 study (1 design),q2· 0 of 1 report an effect size · 2 claims rest on one study
Description¶
A corpus of line-level code explanations produced by novice programming students and experts, annotated for correctness and released for evaluating code-explanation and semantic-similarity systems.
Components¶
Claims¶
- LLM-based automated assessment of student code self-explanations outperforms semantic similarity methods on a balanced classification task [+W]
- LLM-based scoring produced fewer false negatives than Deep Tutor on a balanced test set, which the authors expect would spare students the frustration of correct explanations judged incorrect [+W]
Related Products and Programmes¶
Key Sources¶
- Arun-Balajiee Lekshmi-Narayanan, Mohammad Hassany, Peter Brusilovsky. (2026). Exploring the Effectiveness of Using LLMs for Automated Assessment of Student Self–Explanations in Programming Education. https://arxiv.org/abs/2605.21614
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
selfcode20-dataset-of-student-and-expert-line-by-line-code-explanations |
title |
SelfCode2.0 dataset of student and expert line-by-line code explanations |
description |
A corpus of line-level code explanations produced by novice programming students and experts, annotated for correctness and released for evaluating code-explanation and semantic-similarity systems. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
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