Juho Leinonen
Information Systems · 2018–2023
A researcher in the Science corpus: 12 papers; trusts 15 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 86 citations into it; #3,115 of 8,771 by trust here, #2,307 by citations
- Computer Science Applications — 82 citations into it; #316 of 1,855 by trust here, #335 by citations
- Software — 43 citations into it; #762 of 1,227 by trust here, #418 by citations
- Human-Computer Interaction — 42 citations into it; #790 of 2,253 by trust here, #1,011 by citations
- Code Generation & Program Synthesis — 36 citations into it; #135 of 220 by trust here, #98 by citations
- Large Language Models — 27 citations into it; #415 of 799 by trust here, #576 by citations
- Developmental and Educational Psychology — 26 citations into it
- Software Engineering — 25 citations into it; #94 of 113 by trust here, #65 by citations
Whom Juho Leinonen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Paul Denny
- Nea Pirttinen
- Sami Sarsa
- February Friday
- D. Kerby
- Arto Hellas
- Klaus Krippendorff
- Andrew Luxton-Reilly
- Stephen MacNeil
- Raymond Lister
Who trusts Juho Leinonen overall
- Paul Denny
- Stephen MacNeil
- Sami Sarsa
- Nea Pirttinen
- Arto Hellas
- James Prather
- Andrew Tran
- Brett A. Becker
Papers from Juho Leinonen's perspective
Ordered by how strongly the researchers Juho Leinonen trusts endorse each paper. Same corpus, a different order for every researcher.
- Evaluating Large Language Models Trained on Code (arXiv.org)
- A multi-national study of reading and tracing skills in novice programmers (SIGCSE Bulletin Inroads)
- Learning to program = learning to construct mechanisms and explanations
- The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming
- An Australasian study of reading and comprehension skills in novice programmers, using the bloom and SOLO taxonomies
- Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models
- Further evidence of a relationship between explaining, tracing and writing skills in introductory programming
- Hierarchical Text-Conditional Image Generation with CLIP Latents (arXiv.org)
- Going SOLO to assess novice programmers
- Not seeing the forest for the trees: novice programmers and the SOLO taxonomy (Annual Conference on Innovation and Technology in Computer Science Education)
Their papers
- Comparing Code Explanations Created by Students and Large Language Models (2023) — 209 citations
- The Robots Are Here: Navigating the Generative AI Revolution in Computing Education (2023) — 409 citations
- Automated Questionnaires About Students’ JavaScript Programs: Towards Gauging Novice Programming Processes (2023) — 24 citations
- Computing Education in the Era of Generative AI (2023) — 325 citations
- Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models (2022) — 502 citations
- Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book (2022) — 251 citations
- Robosourcing Educational Resources - Leveraging Large Language Models for Learnersourcing (2022) — 40 citations
- Using Large Language Models to Enhance Programming Error Messages (2022) — 245 citations
- Can Students Review Their Peers?: Comparison of Peer and Instructor Reviews (2022) — 16 citations
- Crowdsourcing Content Creation for SQL Practice (2020) — 20 citations
- Experimenting with Model Solutions as a Support Mechanism (2019) — 5 citations
- Crowdsourcing programming assignments with CrowdSorcerer (2018) — 28 citations