Neural Feedback Generation
Neural Feedback Generation is a computational method for automated feedback generation that leverages neural network architectures to synthesize personalized, context-aware feedback by modeling complex patterns in learner responses through learned representations.
16
Publications
525
Citations
60
Authors
25
Institutions
Publications per year
2017–2026
7
16
FOOD TEXTURE AND ITS EFFECT ON INGESTION, MASTICATION AND SWALLOWING
Peter W. Lucas, J.F. Prinz, Kalpana R. Agrawal et al. · Journal of Texture Studies · 2004 · 91 citations
Learning Program Embeddings to Propagate Feedback on Student Code
Chris Piech, Jonathan Huang, Andy Nguyễn et al. · arXiv (Cornell University) · 2015 · 86 citations · Full text
Learning Program Embeddings to Propagate Feedback on Student Code
Chris Piech, Jonathan Huang, Andy Nguyễn et al. · arXiv (Cornell University) · 2015 · 60 citations · Full text
1–5 of 16
60
2
h-index
2
h-index
Massachusetts Institute of Technology, United States
2 publications · 40 citations
2
h-index
2
h-index
2
h-index
1–5 of 60
25
Stanford University
Stanford, United States
2 h-index
10 publications
730 citations
Google (United States)
Mountain View, United States
2 h-index
2 publications
146 citations
Massachusetts Institute of Technology
Cambridge, United States
2 h-index
6 publications
98 citations

University of Maryland, Baltimore
Baltimore, United States
1 h-index
1 publications
10 citations
Shanghai Sunshine Rehabilitation Center
Shanghai, China
1 h-index
8 publications
240 citations
Wageningen University & Research
Wageningen, The Netherlands
1 h-index
1 publications
91 citations
Microsoft (United States)
Redmond, United States
1 h-index
1 publications
29 citations

King Saud University
Riyadh, Saudi Arabia
1 h-index
4 publications
92 citations
1–8 of 25
Repository · 3 publications · 157 citations
3
h-index
1 publications · 61 citations
1
h-index
1 publications · 10 citations
1
h-index
1 publications · 30 citations
1
h-index
Conference · 1 publications · 12 citations
1
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1–5