Cecilia Ka Yuk Chan
Education · 2012–2023
A researcher in the Science corpus: 6 papers; trusts 22 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Education — 28 citations into it
- Artificial Intelligence — 15 citations into it; #21,486 of 35,345 by trust here, #21,316 by citations
- Computer Science Applications — 14 citations into it
- Health Informatics — 12 citations into it; #89 of 240 by trust here, #112 by citations
- Safety Research — 11 citations into it; #4,370 of 5,829 by trust here, #4,795 by citations
- Media Technology — 9 citations into it
- Social Psychology — 6 citations into it
- Developmental and Educational Psychology — 5 citations into it
Whom Cecilia Ka Yuk Chan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- M. Carter
- S. Pokrivčáková
- Junaid Qadir
- Mohammad Hossein Jarrahi
- Stefan A. D. Popenici
- George Siemens
- Jason W. Osborne
- S. Barrie
- Hector Geffner
- Daniel S. Schiff
Who trusts Cecilia Ka Yuk Chan overall
Papers from Cecilia Ka Yuk Chan's perspective
Ordered by how strongly the researchers Cecilia Ka Yuk Chan trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- AUTO-ENCODING VARIATIONAL BAYES
- Attention is All you Need (Neural Information Processing Systems)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Generative Adversarial Networks
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Students’ voices on generative AI: perceptions, benefits, and challenges in higher education (2023) — 1,912 citations
- A comprehensive AI policy education framework for university teaching and learning (2023) — 1,149 citations
- The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? (2023) — 594 citations
- The AI Revolution in Education: Will AI Replace or Assist Teachers in Higher Education? (2023) — 319 citations
- A Validated and Reliable Instrument Investigating Engineering Students’ Perceptions of Competency in Generic Skills (2017) — 95 citations
- Exploring an experiential learning project through Kolb's Learning Theory using a qualitative research method (2012) — 135 citations