Joanne Kim

Computer Science Applications · 2022–2023

A researcher in the Science corpus: 2 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Science Applications — 44 citations into it
• Information Systems — 32 citations into it
• Human-Computer Interaction — 31 citations into it
• Software — 15 citations into it
• Code Generation & Program Synthesis — 13 citations into it
• Statistics and Probability — 5 citations into it

Whom Joanne Kim trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Paul Denny

Papers from Joanne Kim's perspective

Ordered by how strongly the researchers Joanne Kim trusts endorse each paper. Same corpus, a different order for every researcher.

1. Evaluating Large Language Models Trained on Code (arXiv.org)
2. Program Synthesis with Large Language Models
3. Language Models are Few-Shot Learners (Neural Information Processing Systems)
4. Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing
5. Further evidence of a relationship between explaining, tracing and writing skills in introductory programming
6. Going SOLO to assess novice programmers
7. Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models
8. The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming
9. Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language
10. An Australasian study of reading and comprehension skills in novice programmers, using the bloom and SOLO taxonomies

Their papers

• Comparing Code Explanations Created by Students and Large Language Models (2023) — 209 citations
• Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book (2022) — 251 citations