Pith. sign in

REVIEW 2 cited by

Explaining Code Examples in Introductory Programming Courses: LLM vs Humans

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2403.05538 v2 pith:ZRDAVQXT submitted 2023-12-09 cs.CY cs.HCcs.SE

Arun-Balajiee Lekshmi-Narayanan , Priti Oli , Jeevan Chapagain , Mohammad Hassany , Rabin Banjade , Peter Brusilovsky , Vasile Rus This is my paper · ORCID
classification cs.CYcs.HCcs.SE
keywords codeexplanationsexamplesprogrammingexamplegeneratedstudentsachieve
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide explanations for many examples typically used in a programming class. In this paper, we assess the feasibility of using LLMs to generate code explanations for passive and active example exploration systems. To achieve this goal, we compare the code explanations generated by chatGPT with the explanations generated by both experts and students.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Critique to Clarity: A Pathway to Faithful and Personalized Code Explanations with Large Language Models

    cs.SE 2024-12 conditional novelty 4.0 of 10

    An iterative two-loop LLM pipeline (a faithfulness loop with execution-based checks and a personalization loop with a role-playing judge) produces code explanations that score higher on automatic metrics than simpler ...

  2. From Automation to Cognition: Redefining the Roles of Educators and Generative AI in Computing Education

    cs.CY 2024-12 conditional novelty 3.0 of 10

    Computing educators propose redesigning take-home assignments to include and assess student use of generative AI, while shifting educator focus to metacognitive skill development.

Pith tools