Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2407.05437.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:27:51.045568Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T22:03:32.423120Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 5bad4ea6-fa40-46aa-b1b6-406f91c2d51a · inbound
Benchmarking Large Language Models on Homework Assessment in Circuit Analysis cites this paper.
Benchmarking Large Language Models on Homework Assessment in Circuit Analysis Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:27:51.045568Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e6c0281a-ae17-4012-accd-e6cbeaa49312 · inbound
Can LLMs Replace Humans During Code Chunking? cites this paper.
Can LLMs Replace Humans During Code Chunking? Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T23:10:12.892063Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8cf030b4-3b19-465c-8308-7032314ca97c · inbound
Evaluating and Improving Large Language Models for Competitive Program Generation cites this paper.
Evaluating and Improving Large Language Models for Competitive Program Generation Enhancing Computer Programming Education with LLMs: A Study on Effective Prompt Engineering for Python Code Generation
Reference 6
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T22:03:32.550494Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.