Paul Denny
Information Systems · with Hellas · 2009–2023
A researcher in the Science corpus: 13 papers; trusts 20 researchers and is trusted by 24 researchers here, as inferred from the citation graph.
Subfields
- Information Systems — 75 citations into it; #2,195 of 8,771 by trust here, #1,246 by citations
- Computer Science Applications — 74 citations into it; #314 of 1,855 by trust here, #170 by citations
- Artificial Intelligence — 58 citations into it; #9,551 of 35,345 by trust here, #18,186 by citations
- Human-Computer Interaction — 40 citations into it; #1,129 of 2,253 by trust here, #587 by citations
- Software — 38 citations into it; #783 of 1,227 by trust here, #273 by citations
- Large Language Models — 36 citations into it; #435 of 799 by trust here, #576 by citations
- Code Generation & Program Synthesis — 25 citations into it; #72 of 220 by trust here, #33 by citations
- Developmental and Educational Psychology — 23 citations into it; #1,346 of 8,223 by trust here, #3,501 by citations
Whom Paul Denny trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Vincent A. W. M. M. Aleven
- Sami Sarsa
- Juho Leinonen
- Sebastian Deterding
- Arto Hellas
- Raymond Lister
- Elliot Soloway
- Andrew Luxton-Reilly
- Mark Chen
- K. Koedinger
Who trusts Paul Denny overall
- Juho Leinonen
- June Ahn
- Juho Hamari
- Elisa D. Mekler
- Sami Sarsa
- Stephen MacNeil
- Arto Hellas
- Anthony J. Pellicone
- James Prather
- Florian Brühlmann
Papers from Paul Denny's perspective
Ordered by how strongly the researchers Paul Denny trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Language Models are Few-Shot Learners (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Language Models are Unsupervised Multitask Learners
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Comparing Code Explanations Created by Students and Large Language Models (2023) — 209 citations
- The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming (2022) — 509 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
- Programming Is Hard - Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation (2022) — 464 citations
- Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language (2022) — 328 citations
- Common logic errors made by novice programmers (2018) — 80 citations
- The effect of virtual achievements on student engagement (2013) — 487 citations
- All syntax errors are not equal (2012) — 147 citations
- Quality of student contributed questions using PeerWise (2009) — 58 citations